The DOI matches its Crossref record; 30 of the 34 references it cites were found in a registry or an index; 4 of the 24 sources its claims cite were checked for support.
Data governance: Organizing data for trustworthy Artificial Intelligence
Open accessAbstract
The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements. However, they all rely on data which is not only big, open and linked but varied, dynamic and streamed at high speeds in real-time. Managing such data is challenging. To overcome such challenges and utilize opportunities for BDAS, organizations are increasingly developing advanced data governance capabilities. This paper reviews challenges and approaches to data governance for such systems, and proposes a framework for data governance for trustworthy BDAS. The framework promotes the stewardship of data, processes and algorithms, the controlled opening of data and algorithms to enable external scrutiny, trusted information sharing within and between organizations, risk-based governance, system-level controls, and data control through shared ownership and self-sovereign identities. The framework is based on 13 design principles and is proposed incrementally, for a single organization and multiple networked organizations.
Overview
Integrity signals
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Verification
Claims the paper makes
61 Own interpretation · 21 No basis given · 16 No full text of the sources · 3 Sources not found · 2 Supported · 2 Partly supported · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Data governance is a success factor for BDAS (Brous, Janssen, & Krans, 2020)”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 1. Introduction
What the sources say
- Supportedcites Brous, Janssen, & Krans, 2020 source
The Conclusions (page not available) explicitly call data governance an important success factor for data science outcomes. The Literature Background connects those outcomes to big-data methods and data lakes, supporting the citing sentence’s paraphrase about BDAS. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In information sharing, traditional trusted third parties (TTPs) that use certificates to provide non-repudiation services, are being replaced with distributed ledger or blockchain technology (DLT) (Dunphy & Petitcolas, 2018;Ølnes, Ubacht, & Janssen, 2017).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
What the sources say
- Partly supportedcites Dunphy & Petitcolas, 2018 sourceExperimental — accuracy not yet measured
The Introduction describes DLT as operating without a central authority to validate ledger transactions, and §6.2 discusses sharing identity attributes certified by third parties. But §6.1 says the schemes reshape rather than eradicate intermediaries. The paper does not establish that certificate-based trusted third parties providing non-repudiation services are being replaced in information sharing. (LLM, conf=0.84)
- Not checked — no full text of the sourcecites Ølnes, Ubacht, & Janssen, 2017 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
In the paper
“These problems arise due to, in large part, sensitive attributes embedded in data sets which are used by machine learning algorithms to search for patterns (Beretta et al., 2018).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
What the sources say
- Not checked — no full text of the sourcecites Beretta et al., 2018 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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In the paper
“The same technological advancements, with data collected from heterogeneous resources, stored in various ways and having different qualities, make data types and structures increasingly complex (Dasu, 2013).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 2. Data governance
What the sources say
- Not checked — no full text of the sourcecites Dasu, 2013 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“public officers become merely mediators rather than decision-makers”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
What the sources say
- Not checked — no full text of the sourcecites Wihlborg, Larsson, & Hedström, 2016 source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“has an overall positive effect on the performance of organizations that apply BDAS (Zhang, Zhao, & Kumar, 2016)”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 1. Introduction
What the sources say
- Not matchedcites Zhang, Zhao, & Kumar, 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Not matched
In the paper
“the decision-making authority is hidden from the user directly affected by the outcomes, public officers become merely mediators rather than decision-makers, and automated public services become”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
What the sources say
- Not checked — no full text of the sourcecites Wihlborg, Larsson, & Hedström, 2016 source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“interoperability also facilitates inaccurate data to flow smoothly across systems and contaminate them in an exponential manner (Dasu, 2013).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 2. Data governance
What the sources say
- Not checked — no full text of the sourcecites Dasu, 2013 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“automated public services become”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
What the sources say
- Not checked — no full text of the sourcecites Wihlborg, Larsson, & Hedström, 2016 source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“and an effective solution to AI-specific risks such as data or algorithmic errors, data or algorithmic bias, or even data-embedded discrimination (Janssen & Kuk, 2016).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
What the sources say
- Not checked — no full text of the sourcecites Janssen & Kuk, 2016 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 90
- It may state two separate claims as one. 27
Every source the paper cites for this claim was checked and supports it.
In the paper
“Data quality consists of many dimensions including accuracy, timeliness, completeness, consistency, objectivity, believability and relevance, which all determine whether data is fit for use (Strong, Lee, & Wang, 1997).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 2. Data governance
What the sources say
- Supportedcites Strong, Lee, & Wang, 1997 source
The Definitions and Methods in Context section defines high-quality data as data fit for use by consumers. The dimensions table reproduced in the Data not used section lists accuracy, objectivity, believability, relevancy, timeliness, completeness, and consistent representation; the claim paraphrases the last as consistency. (LLM, conf=0.91)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Given such challenges, the choice of the data governance approach is critical, albeit this choice is not always clear or even explicit (Koltay, 2016).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
What the sources say
- Partly supportedcites Koltay, 2016 sourceExperimental — accuracy not yet measured
In “Data governance in detail” (page unavailable), Koltay says data governance should not be optional and that cross-functional perspectives on it vary. This supports its importance and suggests some ambiguity, but the passages do not say that choosing a governance approach is not always clear or explicit. (LLM, conf=0.82)
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
In the paper
“This approach is often advocated as a foundation for data governance (Rothstein, Borraz, & Huber, 2013)”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
What the sources say
- Not checked — no full text of the sourcecites Rothstein, Borraz, & Huber, 2013 source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The organizational approach to data governance emphasizes structure, responsibility, accountability and reporting (Mullon & Ngoepe, 2019).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
What the sources say
- Not checked — no full text of the sourcecites Mullon & Ngoepe, 2019 source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mismatch between organizational structure and data usage can easily result in data silos, duplications, unclear responsibilities, and missing control of data over its entire life-cycle.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“too much governance can result in excessive overhead and lower performance.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Another is ad-hoc handling of data without procedures and processes and secure data infrastructure, which might easily result in individual data items being accessible to non-authorized persons.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Beyond a single organization, data governance depends on collaboration between organizations and persons that make up the system.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Risk-assement, defining responsibilities, and joint planning and control can be done between organizations, but require the establishment of trusted frameworks.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The approach is often criticized for not easily adapting to change (Janssen & van der Voort, 2016).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
What the sources say
- Not checked — no full text of the sourcecites Janssen & van der Voort, 2016 source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Too little governance, however, might result in unclear responsibilities, uncontrolled risks and not taking the right precautions and actions.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Another risk area is poor generalization of the algorithms to situations outside the data upon which they were trained.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“With the advent of GDPR and AI, the risk-based approach has gained attention as a way to identify risks of BDAS and introduce appropriate governance mechanisms to address them (Ladley, 2019).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
What the sources say
- Not matchedcites Ladley, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The risks of training algorithms using historical data are that mistakes, inconsistencies and bias embedded in such data will be reflected in the working of the algorithms.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As data that is not collected cannot be misused or abused, the collection of data should be minimized.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Low data quality and unclear dependencies between data and algorithms can easily bias or skew the outcomes of the AI algorithms.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 2. Data governance
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Shared roles and joint operations performed among departments and organizations may dilute responsibilities.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 2. Data governance
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The other way around, the usefulness principes could result in data protection being violated by data stewards.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A common challenge with data governance is that the data flow and logic may not follow the structure of an organization.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Data governance contains mechanisms to incentivize correct behaviours and sanction incorrect ones.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the opening of algorithms should be done with care as it might reveal bugs in the working systems, which could be exploited by hackers.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These expectations and values are translated in the planning and control, organizational, or risk-based governace approaches, resulting in the assignment of decision-making authority and the establishment of processes and procedures.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, mechanisms are needed to ensure responsible data sharing, allowing data sharing when required, but also blocking data sharing when necessary.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“If sensitive data, like gender, race, residential address, health status or political preference is collected, then it can be misused or abused.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Dawes (2010) formulated the stewardship and usefulness principles for information sharing: stewardship should assure data quality, validity and security; manage risks; manage and preserve data; and make public officials and organizations responsible for handling information with care and integrity.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
What the sources say
- Not checked — no full text of the sourcecites Dawes (2010) source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI capabilities generate immense powers, which are dangerous if systems are allowed to expand without proper oversight, accountability and governance.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Plotkin (2013) view data stewardship as a way to formalize accountabilities for managing information resources on behalf of and in the best interest of others.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
What the sources say
- Not matchedcites Plotkin (2013)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For this, trusted data sharing frameworks are required.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While applying BDAS in consequential decision-making is conditional on data quality, perfect data quality does not exist.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 2. Data governance
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
- Rests on an assumption: Perfect data quality does not exist.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While most people will be unable to make much sense of the algorithms, this would allow auditors, experts, scientists, citizen scientists and other professionals, equipped with proper tools, to check the working of the algorithms.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Three approaches to data governance are planning and control, organizational, and risk-based.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Even if data is pooled and linked at the conceptual level, physical storage and responsibility for data should be distributed to reduce vulnerability.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The causal relationship between an event and a system failure might be difficult to establish without proper data governance.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 2. Data governance
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Controls can play similar preventive and detective roles, guided by socio-technical arrangements within the organizations concerned.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There might be a lack of established mechanisms for data governance for dealing with BDAS while involving various organizations.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Creating sound data governance requires a balance between complete control, which is unreachable, and lack of control, which is dangerous, into the mode of governance that is necessary and feasible.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Nevertheless, there are numerous situations where sensitive data must be collected and shared for the sake of transparency, fraud detection, service improvements, or better decisions.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The more controls are embedded in governance processes, the higher auditability and problem detection rates, and the more opportunities for learning and improvement.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the case of data, the notion of stewardship is preferred over ownership.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Dawes (2010) further argued that stewardship and usefulness are complementary principles and should guide information-based transparency.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
What the sources say
- Not checked — no full text of the sourcecites Dawes (2010) source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data governance is based on the expectations and values of the society, which is ultimately affected by the outcomes of BDAS.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The purpose is to increase the value of data and minimize data-related costs and risks (Abraham, Schneider, & vom Brocke, J., 2019).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 1. Introduction
What the sources say
- Not checked — no full text of the sourcecites Abraham, Schneider, & vom Brocke, J., 2019 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, in law, tangible goods are more natural to own than intangible ones, like data which can be shared without limits.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Others emphasize that stewards are also responsible for information security (Cuganesan, Hart, & Steele, 2017).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
What the sources say
- Not checked — no full text of the sourcecites Cuganesan, Hart, & Steele, 2017 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This raises the challenge of defining and sharing responsibilities between data providers, algorithms provides and BDAS operators as part of data governance, particularly when multiple organizations engage in such governance jointly.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 2. Data governance
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Risk-based approaches to data governance are suited to maximize the value created from data while mitigating risks and reducing costs.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The ownership of data is often challenging to establish, and multiple persons might claim the rights to it.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The foundation of data governance is responsible data collection.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Data is the basis for BDAS, but the outcomes of BDAS should be monitored as well, as they are part of the data life-cycle.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These approaches are not mutually exclusive and can be used to complement each other.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 3. Data governance approaches
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, incentives cannot replace proper policing by audit organizations.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.1. System-level controls for BDAS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data stewardship and base registries provide a foundation for data and information sharing.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The stewardship principle might deal with information sharing risks by withholding that information and thus reducing transparency and accountability.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To avoid the situation of a single entity exercising control over data without others' consent, distributed responsibilities and separation of concerns make systems less vulnerable.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Data stewardship is a team effort where responsibilities and expertise are divided among members, who can manage data on behalf of others.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.2. Data stewardship and base registries
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second involves users exercising Self-Sovereign Identity (SSI).”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This reduces the chances of using the data for unrelated purposes.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, access to their own data not only results in transparency but also empowers citizens and organizations to be in control of their data, including data about themselves.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nevertheless, such a decentralized information sharing system needs to be guided by proper governance mechanisms.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, there is a need for technologies like base registries and self-sovereign identities to make trustworthy BDAS work.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore organizations collect and store identity information such as name, gender, age, profession and others to be able to identify persons.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The realization of data governance in multi-organizational settings will also require the use of trusted datasharing frameworks to guide inter-organizational data exchange, and ensure compliance with regulations as well as the creation of public value.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This extension relies on trusted data sharing frameworks, or trusted frameworks for short, that guide data exchange within and between participating organizations while ensuring compliance with regulations and the realization of public value.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Organizations or users can control the data.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the foundation of BDAS data governance is responsible data collection, citizens' control of data, and data stewardship, this new research field has to continue advancing before it creates a solid research foundation for trusted BDAS.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, this also requires agreements to regulate access, openness and inclusion.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Trusted frameworks are a standard mechanism for governing relationships and are used in payment systems, in domain registration systems, or in mobile networks to provide roaming services.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For that, system-level governance is needed.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Available approaches to data governance can be based on clear organizational structures, responsibilities and accountabilities, planning and control cycles, and risks.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Trusted frameworks limit access to data to those who are authorized, and ensure non-repudiation of the data origin.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Increasingly, trusted frameworks which are based on agreements among participating parties are used for data sharing and service provision.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data governance for BDAS is a complex field, and the development of BDAS without due attention to data governance is a significant risk.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data governance can help mitigate the issues of transparency, accountability, fairness, discrimination, and trust.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As DLT ensures decentralized execution, avoiding the creation of a single point of failure or misuse, they are less vulnerable.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Adhering to these principles will help improve data governance and contribute to trustworthy BDAS.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, as BDAS often depend on data sources that are external to the organization, there is a need to extend such data governance to cover multiple organizations.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Critical aspects of data governance are identity and trusted data sharing, which are not part of the standard Internet infrastructure.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Apart from sociotechnical measures focused on controls and safeguards, an organizational culture that promotes awareness and ethical value of data and algorithms is part of data governance.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“BDAS face the risks of violating the privacy, using data for undesired purposes, allowing bias or discrimination in data to inform algorithmic decisions, making wrong decisions, and so on.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This raises security risks for identity information to be stolen, privacy risks with handling personal information, and data integrity risks as data might not be consistent with each other and the states of the real world.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For a single organization, these types of decisions are made within the framework of data governance.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Interoperability is created to be able to share data based on commonly agreed standards specified by the trusted framework.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“BDAS often depend on data sources external to the organization, which can be easily manipulated or misused.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Then, when the data is hacked or shared for illegitimate purposes, this is immediately revelated.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This also results in non-repudiation, that the data origin cannot be disputed.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“SSI provides control over and ownership of data to citizens, who can give consent to share such data with others.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The first situation entails organizations defining base registries using data stewardship.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This results in fragmented data landscape where such information is stored in many places.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 4.3. Trusted data sharing framework
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although these principles might at first appear simple, they are challenging to realize.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although greater transparency into the inner workings of algorithms is necessary, it is insufficient for effective oversight.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper relies on this without support.
There is limited understanding of what constitutes data governance for BDAS in general and AI-based BDAS in particular, even less how to carry out system-level data governance between different organizations.
“Yet there is limited understanding of what constitutes data governance for BDAS in general and AI-based BDAS in particular, even less how to carry out systemlevel data governance, between different organizations.”
The paper relies on this without support.
Data governance has been given scant attention and is often overlooked by organizations in their efforts to realize BDAS and create Fair, Accountable and Transparent (FAT) algorithms.
“Data governance has been given scant attention and is often overlooked by organizations in their efforts to realize BDAS and create Fair, Accountable and Transparent (FAT) algorithms.”
The paper relies on this without support.
Often the focus is on experimenting with AI, but acquiring and preparing data for AI, which often consumes most of the time, is given less consideration.
“Often the focus is on experimenting with AI, but acquiring and preparing data for AI, which often consumes most of the time, is given less consideration.”
The paper relies on this without support.
Data is often fragmented over many organizations which implement different data policies.
“While data governance should help lower the cost of data management and create value from data, data is often fragmented over many organizations which implement different data policies.”
The paper relies on this without support.
BDAS rely heavily on the use of data combined from various sources, some controlled by the organization itself, others controlled by partner organizations, yet others controlled by unknown entities.
“However, BDAS rely heavily on the use of data combined from various sources, some controlled by the organization itself, others controlled by partner organizations, yet others controlled by unknown entities.”
The paper relies on this without support.
Most BDAS are based on a type of machine learning algorithms that identify patterns in data, which they use for descriptive, predictive or prescriptive goals.
“Most BDAS are based on a type of machine learning algorithms that identify patterns in data, which they use for descriptive, predictive or prescriptive goals.”
The paper relies on this without support.
AI algorithms are often opaque.
“Although these controls are needed, AI algorithms are often opaque, controls alone are insufficient, and additional governance mechanisms may be required.”
The paper relies on this without support.
Perfect data quality does not exist.
“While applying BDAS in consequential decision-making is conditional on data quality, perfect data quality does not exist.”
The paper relies on this without support.
Data collection and sharing has become easier over the last decade, partly due to interoperability solutions.
“Data collection and sharing has become easier over the last decade, partly due to interoperability solutions.”
The paper relies on this without support.
Vast volumes of data complicate entity resolution.
“Besides, vast volumes of data complicate entity resolution -identifying the same real-world entity like e.g. a person, within a single database (de-duplication) or in multiple databases (record linkage).”
The paper relies on this without support.
BDAS are typically crossing departmental boundaries, not bound to any single function or process, and have to deal with data originating in multiple departmental silos.
“This is particularly the case for BDAS, which are typically crossing departmental boundaries, not bound to any single function or process, and have to deal with data originating in multiple departmental silos.”
The paper relies on this without support.
AI may violate privacy, discriminate, avoid accountability, manipulate and misinform public opinion, and be used for surveillance.
“It may violate privacy, discriminate, avoid accountability, manipulate and misinform public opinion, and be used for surveillance.”
The paper relies on this without support.
BDAS enable automatic decision-making within public institutions.
“BDAS enable automatic decision-making within public institutions.”
The paper relies on this without support.
AI algorithms and their implementations can range from black-box to white-box approaches.
“AI algorithms and their implementations can range from black-box to white-box approaches.”
The paper relies on this without support.
BDAS are often opaque, and their working is difficult to understand.
“BDAS are often opaque, and their working is difficult to understand.”
The paper relies on this without support.
The foundation of trustworthy BDAS is sound data governance, but this area is often overlooked.
“Although the foundation of trustworthy BDAS is sound data governance, this area is often overlooked.”
The paper relies on this without support.
There are hardly any good practices for successful adoption and application of data governance for BDAS.
“There are hardly any good practices for successful adoption and application of data governance for BDAS.”
The paper relies on this without support.
Scarce research exists about trusted frameworks and SSIs, and there is no consensus yet about how they should be realized in BDAS.
“Scarce research exists about trusted frameworks and SSIs, and there is no consensus yet about how they should be realized in BDAS.”
The authors state this as an assumption.
The model assumes that data, algorithms and processes will be opened to controlled groups for scrutiny.
“Hence, the model assumes that data, algorithms and processes will be opened to controlled groups for scrutiny.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Collaboration and sharing of data between government departments (Alexopoulos et al., 2019;Janssen, Brous, et al., 2020) help develop better models.”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Janssen, Brous, et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, this accuracy comes at the cost of propagating human biases and discrimination inherent in the data used for machine training”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Janssen, Brous, et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Data stewardship and separation of control can become key aspects of the legal framework to define accountability of public decisions and enumerate delegation between humans and machines (Janssen, Brous, et al., 2020;Pencheva et al., 2020).”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Janssen, Brous, et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Data governance principles within public administration can ensure analogous management practices towards higher data quality and trustworthiness”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Janssen, Brous, et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, the drawback of increased transparency is the ability to game the system for private motives”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Janssen, Brous, et al., 2020
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Public decisions supported by AI that cannot be explained, and more importantly justified, constitute challenges to legal accountability”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Janssen, Brous, et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Organizations establish ethical guidelines for AI usage by engaging in a multifaceted process that involves the identification of core ethical principles, the development of practical frameworks, and the implementation of governance models that ensure adherence to these guidelines throughout the AI system's lifecycle [18,19].”
From AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities (2024) · cites this paper as [18]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Similarly, data governance principles on accessibility (see Table 1 in Janssen, Brous, et al., 2020) can help alleviate tensions related to privacy and security.”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Janssen, Brous, et al., 2020
The cited source says what the claim attributes to it.
In the citing paper
“Previous research (Brous et al. 2020; Janssen et al. 2020) has suggested that data governance can be viewed as a boundary condition for data science.”
From Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes (2020) · cites this paper as Janssen et al. 2020
In the citing paper
“Low data quality and unclear dependencies between data and algorithms may lead to biased or skewed AI algorithms' outcomes (Janssen et al., 2020).”
From Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021) · cites this paper as Janssen et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Essential data governance principles
“Table 1 provides an overview of the 12 main data governance principles that are discussed in this article.”
§ Data governance: Organizing data for trustworthy Artificial Intelligence > 5. Inform when sharing
“13.Usefulness Data should be recognized as a valuable asset that can be used by BDAS (Dawes, 2010).”
Citations
Cites
Reengineering Work: Don’t Automate, Obliterate
Hammer, M. M. (1990) · Cited by 2,855
Political Trust and Trustworthiness
Levi, M., & Stoker, L. (2000) · Annual Review of Political Science · doi:10.1146/annurev.polisci.3.1.475 · Cited by 2,190
Emmers, E. (2019) · SGEM International Multidisciplinary Scientific Conferences on Social Sciences and Arts · doi:10.5593/sgemsocial2019v/1.1 · Cited by 1,317
Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997) · Communications of the ACM · doi:10.1145/253769.253804 · Cited by 1,298
Ølnes, S., Ubacht, J., & Janssen, M. (2017) · Government Information Quarterly · doi:10.1016/j.giq.2017.09.007 · Cited by 1,107
Self-citationKhatri, V., & Brown, C. V. (2010) · Communications of the ACM · doi:10.1145/1629175.1629210 · Cited by 816
Data governance: A conceptual framework, structured review, and research agenda
Abraham, R., Schneider, J., & vom Brocke, J. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.07.008 · Cited by 645
Adaptive governance: Towards a stable, accountable and responsive government
Janssen, M., & van der Voort, H. (2016) · Government Information Quarterly · doi:10.1016/j.giq.2016.02.003 · Cited by 459
Self-citationA first look at identity management schemes on the blockchain
Dunphy, P., & Petitcolas, F. A. P. (2018) · IEEE Security & Privacy · doi:10.1109/msp.2018.3111247 · Cited by 416
The challenges and limits of big data algorithms in technocratic governance
Janssen, M., & Kuk, G. (2016) · Government Information Quarterly · doi:10.1016/j.giq.2016.08.011 · Cited by 363 · cited 1×
“This approach is often advocated as a foundation for data governance (Rothstein, Borraz, & Huber, 2013) and an effective solution to AI-specific risks such as data or algorithmic errors, data or algorithmic bias, or even data-embedded discrimination (Janssen & Kuk, 2016).”
Self-citation
Research works printed but not linked: 1
- European Parliament and European Council, 2016, Regulation (EU) 2016/679 of the European Parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46, Official Journal of the European Union, 59, 294
Cited by
Sorted by citations
Items 301–400 of 782
Harrison, S., Potter, S., Prasanna, R., et al. (2024) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2024.104395 · Cited by 5
Fadmastuti, M., Nowak, D. J., & Crompvoets, J. (2024) · Environmental Science & Policy · doi:10.1016/j.envsci.2024.103938 · Cited by 5
Bena, Y. A., Ibrahim, R. B., Mahmood, J., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3498947 · Cited by 5
Ethical aspects using AI in CRIS
Azeroual, O., Schöpfel, J., Störl, U., & Marušić, A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.11.058 · Cited by 6
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Fannouch, A., Gharib, J., & Gahi, Y. (2025) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2025.100321 · Cited by 10
Kadam, P. A., Bhushan, S., Rishiwal, V., et al. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02010-y · Cited by 5
Role and Governance of Artificial Intelligence in the Public Policy Cycle
Valle-Cruz, D., & Sandoval‐Almazán, R. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.25 · Cited by 6
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Chen, Y., & Ahn, M. J. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.31 · Cited by 6
COVID-19 and the Data Governance Gap
Austin, L. M. (2023) · Annual Review of Law and Social Science · doi:10.1146/annurev-lawsocsci-050520-101947 · Cited by 5
Human‐supervised data science framework for city governments: A design science approach
Hagen, L., Patel, M., & Luna‐Reyes, L. F. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24764 · Cited by 7
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Millán-Gómez, M. Á., & Mujica-Sequera, R. M. (2023) · Revista Docentes 2 0 · doi:10.37843/rted.v16i2.376 · Cited by 5
Zhu, Q., Kim, D., Eldardiry, H., & Ausman, M. (2023) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10343253 · Cited by 6
“…is transforming the world we live in. Scholars have widely acknowledged that AI has brought both promise and peril to nearly every aspect of human life ranging from family life, education, and social interaction to social governance, healthcare, military, and international politics [1].”
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Rahimi, F., Kaleibar, F. J., Feizi, F., et al. (2023) · doi:10.1109/icaea60387.2023.10414472 · Cited by 5
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BackgroundKnutsen, L. Z., Hannay, J. E., & Riegler, M. (2024) · ACM/IEEE International Workshop on Software-intensive Business (IWSiB) · doi:10.1145/3643690.3648235 · Cited by 5
Rao, B., Lipizzi, C., & Mansouri, M. (2024) · IEEE International Symposium on Systems Engineering · doi:10.1109/isse63315.2024.10741091 · Cited by 5
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BackgroundJiménez, D. L., Dittmar, E. C., & Portillo, J. P. V. (2024) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/979-8-3693-7678-2.ch002 · Cited by 4
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Basereh, M., Caputo, A., & Brennan, R. (2022) · International Journal of Semantic Computing · doi:10.1142/s1793351x22400013 · Cited by 6
Andrews, D., Leitner, P., Schön, S., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05657-4_12 · Cited by 5
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Wang, F., Zhu, H., Wu, Y., He, D., & Liu, X. (2023) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.875 · Cited by 8
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Abouelenein, Y. A. M., Ghazala, A. F. A., Mahdy, E. M. M., & Khalaf, M. H. R. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13616-3 · Cited by 11
“AI, despite its promise, encounters obstacles such as significant computational resource demands, elevated costs linked to implementation and scaling, and ethical issues about job displacement and privacy (Janssen et al., 2020 ; Hagendorff & Wezel, 2020 ; Siau & Wang, 2020 ; Luan et al., 2020 ).”
Utomo, S., Pratap, A., Karthikeyan, P., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103440 · Cited by 4
The Usage of AI in the Public Sector: The Case of Greece and Proposals for Future Research
Mantas, C., Karapetsas, V., & Malik, S. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83608-904-920251007 · Cited by 4
Future-ready public institutions: rethinking data governance through maturity assessment
Cretu, V. (2025) · ECONOMY AND SOCIOLOGY: Theoretical and Scientifical Journal · doi:10.36004/nier.es.2025.1-04 · Cited by 4
Ahmadi, L., Bilal, M., Olumo, A., Mollaei, A., Jalaei, F., Ebrahimi, N., Mozaffari, H., Kim, J., & Haas, C. T. (2026) · Journal of construction engineering and management · doi:10.1061/jcemd4.coeng-16899 · Cited by 7
Yousefi, F., Dehnavieh, R., Laberge, M., Haghdoost, A., Sasseville, M., Hekmat, S. N., Ghaemi, M. M., & Nadali, M. (2026) · Healthcare · doi:10.3390/healthcare14020145 · Cited by 4
Enhancing data governance through transparency: An empirical study of the data trust model
Kim, Y. J., Park, Y. S., & Park, S.-P. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124551 · Cited by 6
Shatila, K. (2026) · International Journal of Disclosure and Governance · doi:10.1057/s41310-026-00377-9 · Cited by 4
Artificial Intelligence in European Union Tax Administrations: A Comparative Assessment
Angelov, A. (2026) · Journal of risk and financial management · doi:10.3390/jrfm19040295 · Cited by 10
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Bena, Y. A., Ibrahim, R. B., & Mahmood, J. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-59711-4_15 · Cited by 4
Impact of data cultural aspect to data governance program in higher education
Tridalestari, F. A., & Prasetyo, H. N. (2024) · Journal of Governance and Regulation · doi:10.22495/jgrv13i3art1 · Cited by 4
“…of data governance, research that reviews culture is still small and not specific, such as research conducted by Duvier et al. (2018), Jim and Chang (2018), Paskaleva et al. (2017), and Janssen et al. (2020) where the fourth study only states that culture is an important aspect of data governance.”
BackgroundSarantis, D., Rizun, N., Alexopoulos, C., & Saxena, S. (2025) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-12-2023-0234 · Cited by 4
Filgueiras, F. D. B., Lui, L., Rosa, T. G., & Ferreira, G. C. (2025) · Policy Design and Practice · doi:10.1080/25741292.2025.2460838 · Cited by 4
“Data governance aims to implement a data agenda by maximizing the value of assets and carrying out risk management by collecting, storing, using, and sharing data between different public and private actors (Janssen et al. 2020).”
Factors influencing Big Data governance in enhanced service delivery in South African public sector
Mudzunga, P. P., Adeyelure, T. S., & Kalema, B. M. (2025) · South African journal of information management · doi:10.4102/sajim.v27i1.1931 · Cited by 4
Wang, Z. (2023) · Kybernetes · doi:10.1108/k-01-2023-0061 · Cited by 4
Designing Human and Artificial Intelligence Interactions in Industry X
Walter, S. (2023) · Springer series in design and innovation · doi:10.1007/978-3-031-29306-1_12 · Cited by 4
Digitalisation and Financial Data Governance in Africa: Challenges and Opportunities
Ndemo, B., & Mkalama, B. W. (2023) · Springer eBooks · doi:10.1007/978-3-031-24498-8_6 · Cited by 4
Personal Information Protection in Government Data Openness Using Decision Tree Model
Gao, B., Sun, J., & Wang, B. (2023) · Journal of Global Information Management · doi:10.4018/jgim.332815 · Cited by 4
Future Trends in AI and Sustainable Business Practices
Nandhini, R., Chandra, S., Pandey, S., Selvakumar, P., Srivastava, P., & Manjunath, T. C. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9571-4.ch008 · Cited by 4
Big Data Versus Big GPU: Evolving Requirements and Governance Dynamics of AI Training Data
Cheng, L., Gong, X., & Zhao, Y. (2025) · International Journal of Digital Law and Governance · doi:10.1515/ijdlg-2025-0004 · Cited by 5
Blockchain enabled collective and combined deep learning framework for COVID19 diagnosis
Periyasamy, S., Kaliyaperumal, P., Thirumalaisamy, M., Balusamy, B., Elumalai, T., Meena, V., & Jadoun, V. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-00252-7 · Cited by 4
“The significance of blockchain technology was underlined, particularly in light of its ability to facilitate global data transfer and public access to medical records 27 .”
McNutt, J. G., & Goldkind, L. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9609-8.ch003 · Cited by 4
Alghamdi, F. S., & Nor, R. M. (2023) · International Journal of Professional Business Review · doi:10.26668/businessreview/2023.v8i12.4035 · Cited by 4
The Security of Data Governance in the Digital WorldPreprint
Khordadpour, P. (2023) · doi:10.36227/techrxiv.22129160.v1 · Cited by 4
Basharat, A., Bewong, M., Krivokapic‐Skoko, B., Ip, R. H. L., Lewis, C., Al‐Saggaf, Y., & Islam, M. Z. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4863615 · Cited by 3
Li, X., Cheng, L., Jiao, H., & Li, H. (2024) · Engineering Construction & Architectural Management · doi:10.1108/ecam-07-2023-0701 · Cited by 5
Delving into the Dark Side of Artificial Intelligence in Green Marketing
Dalhoum, S., Zouari, A., & Khemakhem, R. (2024) · Lecture notes in business information processing · doi:10.1007/978-3-031-76368-7_21 · Cited by 4
Alshehhi, K., Cheaitou, A., & Rashid, H. S. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02653-9 · Cited by 6
“The challenges may deal with data and legal concerns that result from data use in machine learning services (Janssen et al. 2020; Tan 2021).”
Zhai, J., & Huang, J. (2025) · Frontiers in Environmental Economics · doi:10.3389/frevc.2025.1607149 · Cited by 4
EU AI Act and the Rights-Based Approach to Technological Governance
Pavlidis, G. (2026) · Review of European and Comparative Law · doi:10.31743/recl.19283 · Cited by 3
Aguilar, R. M., Alayón, S., Torres, J. M., & Martín, C. A. (2026) · AI & Society · doi:10.1007/s00146-026-02978-y · Cited by 10
“Building on public value theory (Moore 1995; Janssen et al. 2020) and the literature on responsible and algorithmic AI governance (Floridi and Cowls 2021; Danaher et al. 2021), this study pursues two interrelated research objectives.”
Population and Civil Registration as the Spearhead of Inclusive and Effective Domestic Governance
Gatiningsih, & Saputra, R. M. H. (2026) · Journal of Ethnic and Cultural Studies · doi:10.66815/ejecs/2974 · Cited by 3
“Digital governance scholarship provides analytical leverage for understanding identity-linked data infrastructures and associated risks (Janssen et al., 2020; Margetts & Dunleavy, 2013).”
InfluentialYiğitcanlar, T., David, A. J., Marasinghe, R., Senadheera, S., Hossain, T., Ye, X., & Taeihagh, A. (2026) · Smart Cities · doi:10.3390/smartcities9050081 · Cited by 6
“Evidence indicates that ethics by design reduces costs associated with retrofitting AI systems, ensures alignment with societal values, and strengthens public legitimacy [19].”
Operational responsibility in AI governance: a user-centric liability framework
Chen, Z. (2026) · AI and Ethics · doi:10.1007/s43681-026-01163-7 · Cited by 5
“Tamò-Larrieux [ 92 ] finds that regula - tory tools identifying accountable human actors generate higher public confidence than rules that spread responsibil - ity across unclear arrangements.”
The Deinstitutionalization of Business Support Functions through Artificial Intelligence
Bauer, J. C., & Wolff, M. (2022) · Information · doi:10.3390/info13080352 · Cited by 3
Towards Data Governance for Federated Machine Learning
Peregrina, J. A., Ortiz, G., & Zirpins, C. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23298-5_5 · Cited by 4
Developing a Data Lakehouse for a South African Government-Sector Training Authority
Mthembu, Z. K., Patel, S. S., Naicker, N., Joseph, S., Madamshetty, L., Moonsamy, D., Akinola, A. T., & Govender, T. P. (2023) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-6684-9716-6.ch007 · Cited by 2
Artificial intelligence in healthcare facilities asset information management: mixed review
Tjebane, M. M., & Musonda, I. (2024) · Infrastructure Asset Management · doi:10.1680/jinam.23.00033 · Cited by 3
A Survey on AI Integration into Industry 5.0
Raj, G. D., & Prabadevi, B. (2024) · GAZI UNIVERSITY JOURNAL OF SCIENCE · doi:10.35378/gujs.1320760 · Cited by 6
Chen, H., & Hu, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3493860 · Cited by 5
Opportunities and Applications of Federated Learning in the Financial Services Industry
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Loulaki, M., Kiourtis, A., Mavrogiorgou, A., et al. (2024) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb64556.2024.10805685 · Cited by 3
“Unified data is critical across industries, especially in today’s data-driven world where organizations depend on diverse data sources [11].”
Tangi, L., Müller, A. P. R., & van Noordt, C. (2025) · Digital Government Research and Practice · doi:10.1145/3776547 · Cited by 7
“It is essential to develop a trustworthy environment where AI solutions are used responsibly and ethically [6], [7].”
Integration of Identity Governance and Management Framework within Universities for Privileged Users
Parveen, S., Ahmad, S., & Khan, M. A. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120664 · Cited by 3
Research on Electric Power Data Governance System and Its Application
Chen, P., Bing, C., Yi, C., et al. (2021) · International Conference on Power System Technology (POWERCON) · doi:10.1109/powercon53785.2021.9697771 · Cited by 3
“How to improve the data governance system, effectively standardize the workflow, manage massive data, and fully tap its value is one of the main problems that modern power industry needs to face[1-2].”
BackgroundSpanaki, K., Karafili, E., & Despoudi, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91614-1.00009-5 · Cited by 3
Data governance in China’s platform economy
Shen, Y. (2022) · China Economic Journal · doi:10.1080/17538963.2022.2068833 · Cited by 3
Empowering quality data – the Gordian knot of bringing real innovation into healthcare system
Horgan, D., Hamdi, Y., Lal, J. A., Nyawira, T., Meyer, S., Kondji, D., Francisco, N. M., Guzman, R. B. D., Paul, A., Nallamalla, K. R., et al. (2022) · Diagnosis · doi:10.1515/dx-2022-0115 · Cited by 3
Zanellati, A., Macauda, A., Panciroli, C., & Gabbrielli, M. (2023) · Research on Education and Media · doi:10.2478/rem-2023-0014 · Cited by 3
“In fact, the close intertwining between the datafication process leads to a massive collection and analysis of data and the pervasiveness of artificial intelligence (Janssen et al., 2020).”
BackgroundWhy Decentralize Deep Learning?
Wright, S. A. (2023) · International Symposium on Autonomous Decentralized Systems · doi:10.1109/isads56919.2023.10091996 · Cited by 3
“2) Data & Governance Jansen et al.,[39] argue that data governance is the foundation for trustworthy AI.”
BackgroundAlbarrak, A. M. (2023) · Diagnostics · doi:10.3390/diagnostics13111916 · Cited by 5
Fundamentals of inherently safer design
Alauddin, M., Khan, F. I., Amyotte, P. R., et al. (2023) · Methods in chemical process safety · doi:10.1016/bs.mcps.2023.05.002 · Cited by 3
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Osorio, F. D. T., Becerra, V. E. O., & Estévez, E. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-40942-4_11 · Cited by 3
Self-citationK-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals
Emigawaty, E., Adi, K., Rochim, A. F., et al. (2023) · JOIV International Journal on Informatics Visualization · doi:10.30630/joiv.7.3.1761 · Cited by 3
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Zeginis, D., & Tarabanis, K. A. (2024) · Computers · doi:10.3390/computers13010017 · Cited by 3
Wang, P., & Wen, Z.-Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-82433-4 · Cited by 3
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“For example, the normative studies have analyzed the drivers and barriers to reach the desired future state of borderless data movements as a means to facilitate social innovation and its diffusion across societies (see, e.g., Janssen et al., 2020).”
Automated Anomaly Detection for Sales and Inventory in Data-Driven Industries
Makhmetova, G. (2025) · Universal library of engineering technology. · doi:10.70315/uloap.ulete.2025.0201002 · Cited by 3
van Wijk, S., Lemke, F., & Draheim, D. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-67238-6_1 · Cited by 3
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Valle-Cruz, D., Muñoz-Chávez, J. P., & García-Contreras, R. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-1210-0.ch001 · Cited by 3
Bolívar, M. P. R. (2026) · International Review of Administrative Sciences · doi:10.1177/00208523261433893 · Cited by 11
“For example, Janssen et al. (2020) argue that AI can enhance predictive policymaking, improve resource allocation and support real-time monitoring of public programmes, but these bene fi ts are contingent on governance mechanisms ensuring accountability, fairness and interpretability.”
InfluentialThe Personalization of Digital Services: How is it Being Driven by European Union Member States?
Martínez, A. C. I. (2025) · Digital Government Research and Practice · doi:10.1145/3734697 · Cited by 2
“To guarantee the quality of the data and its usability in the personalization of services, public administration must also provide data governance [ 40 , 41 ].”
Seo, H., Lee, H., & Tsai, F.-S. (2025) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.379768 · Cited by 3
Miñón, R., Díaz-de-Arcaya, J., Torre-Bastida, A. I., López-de-Armentia, J., Zárate, G., Bonilla, L., Garcia-Perez, A., & Aguirre-Usandizaga, J. (2025) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-025-00761-w · Cited by 2
“As highlighted in [42], data monitoring to detect a data drift is directly related with data quality concepts such as bias detection and, in turn, model drift detection is related with model quality when detecting changing patterns in the model output (or indeed in the output of a code block).”
Big data ethics: implications for organizational performance and business value
Nilashi, M., Abumalloh, R. A., Boon, O. K., Tan, G. W., Aw, E. C., & Cham, T. (2025) · Management Decision · doi:10.1108/md-04-2024-0801 · Cited by 3
A digital participation transformation in the chinese local government: A bibliometric analysis
Xinru, W., & Manaf, H. B. A. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2026273 · Cited by 2
“…a long-standing research foundation in the fields of digital governance, public administration, and information systems, and their research findings have played a leading role in both theory and practice for the digital transformation of developing countries such as China (Janssen et al., 2020).”
Hummel, L., Lorenz-Artz, K. C. A. G., Bierbooms, J. J. P. A., & Bongers, I. M. B. (2025) · JMIR Medical Informatics · doi:10.2196/74240 · Cited by 4
Kwarteng, R. A., Idoko, I. P., & Azonuche, T. I. (2025) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2511666 · Cited by 2
Al-Okdeh, S. K., Al-Sulaiti, I., Shwedeh, F., Alzoubi, H. M., & Ahmed, G. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83608-578-220251023 · Cited by 2
Smart and International Campuses in Research Collaborations
Mukminin, A., Marzulina, L., Abdrazakova, A., Harto, K., Fridiyanto, F., Erlina, D., & Amir, R. Z. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6831-3.ch012 · Cited by 2
User trust in AI and major tech companies in twelve countries
Cvetkovic, A., Savolainen, I., Celuch, M., Heiskari, M., Ruokosuo, E. S., Arriaga, P., Koike, M., & Oksanen, A. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2619648 · Cited by 4
“One common concern related to the use of AI is how these companies treat users’ personal data (Hayes et al. 2021; Janssen et al. 2020; Mühlhoff 2023).”
Harnessing Artificial Intelligence (AI) for enhanced organizational performance in public sectors
Thanh, N. H., & Cong, B. T. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06571-y · Cited by 6
Albulayhi, O., & Alkhalifah, A. (2026) · Sustainability · doi:10.3390/su18062986 · Cited by 2
Artificial intelligence in policymaking: Mapping integration gaps across the public policy cycle
Lněnička, M., Clarinval, A., Nikiforova, A., Rudmark, D., Luterek, M., Symeonidis, D., & Bolívar, M. P. R. (2026) · Government Information Quarterly · doi:10.1016/j.giq.2026.102138 · Cited by 4
Nam, M., & Chang, Y. (2026) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2026.101115 · Cited by 4
Cervantes, N. Y., & Jaramillo, C. M. Z. (2021) · Lecture notes in educational technology · doi:10.1007/978-981-16-3941-8_9 · Cited by 2
Identifying Principles and Ownership of Data Governance Framework for Higher Education Institution
Hanapiah, N. M., Iahad, N. A., & Bahari, M. (2021) · International Conference on Research and Innovation in Information Systems · doi:10.1109/icriis53035.2021.9617097 · Cited by 2
Artificial Intelligence in Education as a Means to Personalize Learning
Lacárcel, A. M. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9609-8.ch016 · Cited by 2
Martínez, J. E. P. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9609-8.ch013 · Cited by 2
Research on Data Governance System for Information Sharing and Openness
Yang, B. (2023) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns.2023.2.01098 · Cited by 2
Metadata
- Authors
- Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T.
- Affiliations
- Delft University of Technology (NL)
- Universität für Weiterbildung Krems (AT)
- Corresponding author
- Janssen, Marijn
- Venue
- Government Information Quarterly
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 37
- Issue
- 3
- Pages
- 101493
- ISSN
- 0740-624X (ISSN-L)
- OpenAlex
- W3036911563
- Cited by
- 811
- References
- 34
- Citations by year
- 2026: 239 · 2025: 170 · 2024: 159 · 2023: 99 · 2022: 95 · 2021: 44 · 2020: 5
- FWCI
- 82.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Government Information Quarterly (publishedVersion)
- UNU Collections (United Nations University) (submittedVersion)
- Lirias (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- European Commission
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Information Systems › Data Quality and Management
- Keywords
- data governance, trustworthy AI, algorithmic decision-making, data stewardship, algorithmic transparency, risk-based governance, self-sovereign identity, data ownership, networked organizations, design principles
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