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
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“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
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Cited by
Sorted by citations
Items 201–300 of 782
Leveraging big data for agile transformation in technology firms: Implementation and best practices
Simpson, B. D., Johnson, E., Adeleke, G. S., et al. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i6.1216 · Cited by 12
Fraud Detection in Fintech Leveraging Machine Learning and Behavioral AnalyticsPreprint
Josyula, H. P. (2023) · Research Square · doi:10.21203/rs.3.rs-3548343/v1 · Cited by 18
Ruslan, I. F., Alby, M. F., & Lubis, M. (2022) · International Conference on Industrial and Business Engineering (ICIBE) · doi:10.1145/3568834.3568866 · Cited by 11
“In applying data governance, we will be using the organizational approach of data governance which emphasizes structure, responsibility, accountability, and reporting within an organization and treats data governance as a defining authority [18].”
MethodThe State of Ethical AI in Practice: A Multiple Case Study of Estonian Public Service Organizations
Hinton, C. (2023) · International Journal of Technoethics · doi:10.4018/ijt.322017 · Cited by 8
“Janssen et al. (2020) noted that this tedious task is given less consideration due to the time it takes.”
BackgroundAn Empirical Study on Core Data Asset Identification in Data Governance
Chen, Y., Zhao, Y., Xie, W., et al. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7040161 · Cited by 11
“Data governance aims to maintain high-quality data that are secure and easily accessible for deeper business insights, therefore enhancing management efficiency and empowering product innovations [1,2].”
BackgroundOpportunities and Challenges of Using Artificial Intelligence in Securing Cyber-Physical Systems
Nweke, L. O., & Yayilgan, S. Y. (2024) · Springer eBooks · doi:10.1007/978-3-031-57452-8_7 · Cited by 12
Yu, M., jiang, W., Wan, Y., & Mao, A. (2024) · Current Issues in Tourism · doi:10.1080/13683500.2024.2394569 · Cited by 13
“AI capability enhances both the perception of user-centric design and the risk of personal information disclosure (Janssen, Brous, et al., 2020).”
Xu, X., & Yuan, H. (2025) · Asia Pacific Business Review · doi:10.1080/13602381.2025.2496511 · Cited by 12
Grounding data governance motivations: a review of the literature
Walsh, M. J., McAvoy, J., & Sammon, D. (2022) · Journal of Decision System · doi:10.1080/12460125.2022.2073637 · Cited by 11
“It is argued that the best practice is to adopt a need to know or least-privilege model (JANSSEN et al., 2020).”
BackgroundSimonofski, A., Tombal, T., de Terwangne, C., Willem, P., Frénay, B., & Janssen, M. (2022) · Data & Policy · doi:10.1017/dap.2022.6 · Cited by 10
“For instance, the network structure for data sharing between the SSIs can be considered as a concrete illustration of the distributed governance advocated by Janssen et al. (2020).”
BackgroundSelf-citationTowards a Peer-to-Peer Federated Machine Learning Environment for Continuous Authentication
Monschein, D., Peregrina, J. A., Piotrowski, T., Nochta, Z., Waldhorst, O. P., & Zirpins, C. (2021) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc53001.2021.9631491 · Cited by 13
Hintz, M. J., Groß, M., Creutzig, F., & Kaack, L. H. (2025) · Energy Research & Social Science · doi:10.1016/j.erss.2025.104498 · Cited by 11
“The challenge of data protection and sharing requires not a one-off solution but a revised data governance approach from city administrations in line with trustworthy AI as defined in the EU AI Act [70,71].”
Basereh, M., Caputo, A., & Brennan, R. (2021) · International Conference on Transdisciplinary AI (TransAI) · doi:10.1109/transai51903.2021.00024 · Cited by 11
“Since, there is a strong connection between AI and data governance [2], we analyse the relation between FAIR, as a set of widely accepted data governance principles and FAccT as a set of most important AI governance aspects.”
BackgroundMethodWanckel, C. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101705 · Cited by 10
Pavone, P., Ricci, P., Calogero, M., & Capaccioni, P. (2023) · Public Integrity · doi:10.1080/10999922.2022.2163049 · Cited by 12
“In recent years, with the development of data governance for public good aims, this discussion is becoming less polarized (Janssen et al. 2020).”
BackgroundGodoy, M. P., Rusu, C., Díaz, F. H., Granollers, T., & Ugalde, J. (2023) · Sustainability · doi:10.3390/su152215998 · Cited by 10
Trejos-Gil, C. A., & Monsalve, W. D. G. (2024) · Palabra Clave · doi:10.5294/pacla.2024.27.4.1 · Cited by 10
Chabilall, J., Brown, Q., Cengiz, N., & Moodley, K. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000635 · Cited by 13
Munjeyi, E., & Schutte, D. (2024) · Cogent Social Sciences · doi:10.1080/23311886.2024.2419537 · Cited by 13
“…the distortion of or discrimination within estimates and computational processes, as well as widening the digital gap (chen et al ., 2021; 2021; Janssen et al ., 2020). sienkiewicz-Małyjurek (2023) also found that ai can substitute for human labor and contribute to unemployment. in addition,…”
Zhang, X., & Tan, Y. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.04.036 · Cited by 10
Metaverse for logistics and supply chain management: new framework and research advances
Zhang, Z., Wu, H., Qiu, Y., Liao, Z., & Song, M. (2025) · The International Journal of Logistics Management · doi:10.1108/ijlm-07-2024-0461 · Cited by 12
Academic exploration of blockchain and AI in financial services
Shi, J., & Wang, Y. (2025) · Journal of Electronic Business & Digital Economics · doi:10.1108/jebde-08-2024-0023 · Cited by 10
Value Creation through Urban Data Platforms: A Conceptual Framework
Bagheri, S., Brandt, T., Sheombar, H. S., et al. (2021) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2021.302 · Cited by 10
“Data quality is a multifaceted construct, encompassing a set of quality attributes, such as accuracy, timeliness, completeness, consistency, and relevance, which all determine whether data is fit for use [39].”
BackgroundInfluentialDarusalam, D., Omar, N. B., Janssen, M., Said, J., & Sohag, K. (2021) · Information Development · doi:10.1177/02666669211026363 · Cited by 10
“Opening information to the public enables the people of the society to observe the government activities and realize the improvement of overall governance (Bonina and Eaton, 2020; Janssen et al., 2020).”
BackgroundSelf-citationData governance in healthcare information systems: A systematic literature review
Ngesimani, N. L., Ruhode, E., & Harpur, P. (2022) · South African journal of information management · doi:10.4102/sajim.v24i1.1475 · Cited by 14
“The entire organisation needs to align its goals to DG. Organisations are increasingly developing advanced DG capabilities (Janssen et al. 2020).”
BackgroundLiu, J., Yu, W., Yang, M., & Liu, F. (2025) · Safety Science · doi:10.1016/j.ssci.2025.107090 · Cited by 10
Toward Integrating Trust in Open Innovation
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Integral system safety for machine learning in the public sector: An empirical account
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Criado, J. I. (2024) · Reforma y democracia. · doi:10.69733/clad.ryd.n88.a387 · Cited by 14
“…públicas (Valle-Cruz et al., 2020); las nuevas capacidades analíticas y de gobernanza de datos que son necesarias para la implantación de la IA (Janssen et al., 2020; Salvador & Ramió, 2020); las percepciones del personal público sobre la IA (Criado & O. de Zarate, 2021; Ruvalcaba-Gómez,…”
A Survey on Privacy Attacks Against Digital Twin Systems in AI-Robotics
Fernandez, I. A., Neupane, S., Chakraborty, T., Mitra, S., Mittal, S., Pillai, N., Chen, J., & Rahimi, S. (2024) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic62241.2024.00019 · Cited by 18
“In fact, data governance (DG) lays the foundation for Trustworthy AI [113].”
Ravanelli, N., Lefebvre, K., Brough, A., Paquette, S., & Lin, W. (2025) · Sensors · doi:10.3390/s25092926 · Cited by 9
Vining, R., Karimian, S., McDonald, N., Ebiele, M., Doyle, B., McKenna, L., Ward, M. E., Bendechache, M., & Brennan, R. (2025) · Ergonomics · doi:10.1080/00140139.2025.2512426 · Cited by 9
“The lack of availability or access to high-quality data is also emphasised by Vining et al. (2022) and Janssen et al. (2020).”
Algorithmic ethics in AI-enabled workplace systems for food delivery employees
Zhao, H., Yuan, B., Wen, M., & Zhang, T. (2025) · Service Industries Journal · doi:10.1080/02642069.2025.2538071 · Cited by 14
xinlin, J., Wenting, L., Shah, K. A. M., Na, M., & Alam, S. S. (2025) · Journal of Quality Assurance in Hospitality & Tourism · doi:10.1080/1528008x.2025.2528044 · Cited by 13
Research and Development Agenda for the Use of AI in Parliaments
von Lucke, J., Fitsilis, F., & Etscheid, J. (2023) · Annual International Conference on Digital Government Research · doi:10.1145/3598469.3598517 · Cited by 9
“Hence, any organization that handles data, is advised to pay special attention to the recruitment and training of appropriate professionals who deal with data quality and privacy (see, for example: [14]).”
BackgroundUpreti, K., Verma, A., Mittal, S., Vats, P., Haque, M., & Ali, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-45124-9_18 · Cited by 9
Boniface, M., Carmichael, L., Hall, W., Pickering, B., Stalla-Bourdillon, S., & Taylor, S. (2022) · Data & Policy · doi:10.1017/dap.2022.1 · Cited by 10
“5 While there is no universal definition of the term “ data governance, ” Janssen et al. (2020) provide a useful description of this term in a multiorganizational context: “ Organizations and their personnel defining, applying, and monitoring the patterns of rules…”
BackgroundThe Antecedents of Customer Satisfaction in the Portuguese Telecommunications Sector
Torrão, J., & Teixeira, S. (2023) · Sustainability · doi:10.3390/su15032778 · Cited by 17
Al-Ghamdi, B. A. M. A.-R. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.102705 · Cited by 11
Chang, T., & Bau, D.-Y. (2025) · Library Hi Tech · doi:10.1108/lht-03-2024-0158 · Cited by 8
Lim, J. S., Lee, C., Shin, D., Kim, J., & Zhang, J. (2025) · Journal of Public Relations Research · doi:10.1080/1062726x.2025.2501552 · Cited by 13
“Data governance entails determining who can make decisions about data usage and for what purposes, as well as how data is collected, stored, processed, used, and destroyed (Janssen et al., 2020).”
Synthetic data for visual machine learning : A data-centric approach
Tsirikoglou, A. (2022) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789179291754 · Cited by 9
Artificial Intelligence Adoption Among Nepalese Industries
Devkota, N., Paudel, R., Parajuli, S., Paudel, U. R., & Bhandari, U. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9609-8.ch012 · Cited by 8
Manik, L. P., Akbar, Z., Yaman, A., & Indrawati, A. (2022) · Publications · doi:10.3390/publications10040050 · Cited by 11
“A solution for that is via data governance, a process of applying authority and control over data such that the data can function adequately and ensure accountability [1].”
BackgroundA Smart Co-Operative Management Framework Based on an EA Concept for Sustainable Development
Chawviang, A., Kiattisin, S., Thirasakthana, M., & Mayakul, T. (2023) · Sustainability · doi:10.3390/su15097328 · Cited by 8
Ethics and Responsible Research and Innovation in Practice
Esteban, E. G., Feenstra, R. A., & Camarinha-Matos, L. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33177-0 · Cited by 8
An Evaluation Framework for the Impact of Digitalization on the Quality of Governance
Darusalam, D., Janssen, M., Said, J., Sanusi, Z. M., & Omar, N. B. (2023) · International Journal of Public Administration in the Digital Age · doi:10.4018/ijpada.332880 · Cited by 9
Self-citationResearch on Data Quality Governance for Federated Cooperation Scenarios
Shen, J., Zhou, S., & Xiao, F. (2024) · Electronics · doi:10.3390/electronics13183606 · Cited by 8
Public Service with Generative AI: Exploring Features and Applications
Aryfiyanto, H., & Alamsyah, A. (2024) · International Conference of Computer and Informatics Engineering (IC2IE) · doi:10.1109/ic2ie63342.2024.10747963 · Cited by 11
“Critical issues like data governance, accountability, transparency, ethical considerations, and regulatory frameworks need to be thoroughly addressed to ensure that AI is used responsibly and effectively in the public sector [60], [62], [67], [68].”
BackgroundSoul of a new machine: Self-learning algorithms in public administration
Gerrits, L. (2021) · Information Polity · doi:10.3233/ip-200224 · Cited by 10
Hurmelinna‐Laukkanen, P., Niemimaa, E., Rantakari, A., & Helander, N. (2022) · Journal of Management Studies · doi:10.1111/joms.12893 · Cited by 10
Using open government data for economic development
Petrović, N., Milić, P., & Prlincevic, B. P. (2022) · The European Journal of Applied Economics · doi:10.5937/ejae19-39004 · Cited by 8
“Janssen et al. (2020) claim that each AI system could be subject to a risk audit to anticipate and address the possible undesirable effects of the AI algorithms.”
BackgroundPeregrina, J. A., Ortiz, G., & Zirpins, C. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23298-5_1 · Cited by 8
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Luna‐Reyes, L. F., & Harrison, T. M. (2023) · Digital Government Research and Practice · doi:10.1145/3609228 · Cited by 12
“…and iterative process of designing and making decisions around aspects of data lifecycle and project associated risks, organizational structures for decision making and accountability, and processes of resource allocation and project implementation, monitoring, and evaluation [ 43 , 50 , 63 ].”
BackgroundMethodInfluentialAn Ecosystem Framework of AI Governance
Wirtz, B. W., Langer, P. F., & Weyerer, J. C. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.24 · Cited by 18
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Carrasco, L. B. (2025) · Journal of Library Metadata · doi:10.1080/19386389.2025.2515766 · Cited by 8
“It also supports cross-border data transfers and multi-jurisdictional regulations by tracking data movements and determining applicable legal frameworks (Janssen et al., 2020; Longpre et al., 2024).”
InfluentialWho are you? Examining the multifaceted innovation roles of municipal governments in AI governance
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Mandalawi, S. A., Mohammed, M. A., Maclean, H., et al. (2025) · doi:10.1109/kse68178.2025.11309632 · Cited by 7
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Usmani, U. A., Usmani, A. Y., & Usmani, M. U. (2023) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis60361.2023.10425285 · Cited by 7
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Romero, F. T., Araújo, J. F., & Ribeiro, M. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04404-y · Cited by 7
“Extant literature claims that the governance of open data is crucial for data processing and the dissemination of quality open data (Kim and Eom, 2019; Ruijer et al., 2020; Janssen et al., 2020; Robinson and Scassa, 2022).”
Liu, J., Wang, M., Kang, X., Zhang, X., & Chen, X. (2022) · Systems Research and Behavioral Science · doi:10.1002/sres.2875 · Cited by 9
AI Technologies for Delivering Government Services to Citizens: Benefits and Challenges
Mohamad, I., Hughes, L., Dwivedi, Y. K., & Alalwan, A. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15342-6_4 · Cited by 8
Akello, P., Vemprala, N., Beebe, N. L., & Choo, K. R. (2022) · ACM Transactions on Management Information Systems · doi:10.1145/3571290 · Cited by 7
Vining, R., McDonald, N., McKenna, L., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17615-9_22 · Cited by 7
Denisart, L., Troncoso, J. F., Loup‐Escande, É., & Franco, A. A. (2024) · Batteries & Supercaps · doi:10.1002/batt.202400042 · Cited by 6
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Stray, V. G., Stol, K.-J., Paasivaara, M., & Kruchten, P. B. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-08169-9 · Cited by 9
Towards the governance of government data using artificial intelligencePreprint
Alqudah, M. A. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3992303 · Cited by 7
Alshammari, K., Thomran, M., AlObaid, R. O. H., et al. (2024) · Sustainability · doi:10.3390/su16125025 · Cited by 6
Stelbotsky, B., & Park, S. (2024) · Public Management Review · doi:10.1080/14719037.2024.2443064 · Cited by 10
“Additionally, past negative experience with AI and chatbots contribute to this aversion, especially when chatbots fail to meet expectations for interpreting complex user inputs (Janssen et al. 2020; Luger and Sellen 2016).”
Emerging Data Practices: Data Work in the Era of Large Language Models
Garcia, A. A., Candello, H. C. D. S. P., Badillo-Urquiola, K. A., & Wong-Villacrés, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714069 · Cited by 15
“Researchers such as [53, 84] suggest that a critical step towards exploring data governance frameworks’ suitability for different challenges is understanding how organizations and their personnel define and apply existing frameworks.”
InfluentialGascó‐Hernández, M., & Valle-Cruz, D. (2025) · Government Information Quarterly · doi:10.1016/j.giq.2025.102036 · Cited by 7
Semiotic mediation in data governance: Towards valuing data as assets
Benfeldt, O., & Persson, J. S. (2025) · Information and Organization · doi:10.1016/j.infoandorg.2025.100588 · Cited by 6
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Castelao-López, J., Santamaría, T. C., & Lagoa‐Varela, D. (2025) · Journal of Money Laundering Control · doi:10.1108/jmlc-10-2024-0159 · Cited by 10
Muhammad, A. S., Hammed, S., Shishah, W., & Isiaku, L. (2025) · Technological Sustainability · doi:10.1108/techs-06-2025-0130 · Cited by 8
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“Robust data governance, based on shared standards and interoperability, enables secure data exchange, lowers coordination frictions, and builds institutional trust essential for cross-firm collaboration (Janssen et al. 2020).”
Fragmented Governance, Shared Norms: Navigating Regime Complexity in Aid Data Governance
Park, K. R. (2025) · Politics and Governance · doi:10.17645/pag.10508 · Cited by 6
“…sought to define data governance from the perspective of information systems (Alhassan et al., 2019; Basukie et al., 2020), public administration (Janssen et al., 2020), communication (Winter & Davidson, 2019), philosophy (Hummel et al., 2021), and political science (Dammann & Glasze, 2023; Liu,…”
Lin, Y.-H., Yuan, W., & Wang, T. (2026) · Buildings · doi:10.3390/buildings16071437 · Cited by 7
SMART DIGITALIZATION AND PUBLIC SERVICES IN THE EU
Klarić, M. (2023) · EU and comparative law issues and challenges series · doi:10.25234/eclic/27446 · Cited by 6
Kim, H., An, J., & Lee, C. C. (2023) · Aslib Journal of Information Management · doi:10.1108/ajim-12-2022-0531 · Cited by 6
Wang, W., Li, J., Jiang, J., et al. (2024) · Symmetry · doi:10.3390/sym16010081 · Cited by 6
Kamzari, M. A., Mir, F. A., & Suliman, A. (2024) · International Journal of Innovation Management · doi:10.1142/s1363919624500038 · Cited by 7
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“Having a dedicated committee allows the process to run smoothly while ensuring that the different members of the project team have time to focus on their specialized tasks [21].”
BackgroundOsborne, S. P., & Nasi, G. (2024) · Public Money & Management · doi:10.1080/09540962.2024.2379103 · Cited by 7
“As emphasized by Janssen et al. (2020), such frameworks should include guidelines and standards for security and ethical use.”
The Readiness of the Public Sector to Implement AI: A Government-Specific Framework
Sirait, E. R. E., Zuiderwijk, A., & Janssen, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70274-7_19 · Cited by 7
Self-citationOverview and Importance of Data Governance
Sargiotis, D. (2024) · Springer eBooks · doi:10.1007/978-3-031-67268-2_1 · Cited by 9
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AlFadhli, M. S., Ayvaz, B., Küçükvar, M., et al. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00701-y · Cited by 6
Strategies for Master Data Management: A Case Study of an International Hearing Healthcare Company
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“Instead, a more overarching strategy for how data are organized, analyzed, and deployed throughout the organization is needed (DalleMule & Davenport, 2017; Janssen et al., 2020).”
BackgroundBernardo, B. (2022) · Forensic Science & Addiction Research · doi:10.31031/fsar.2022.05.000625 · Cited by 6
Ethics and Development of Advanced Technology Systems in Public Administration
Alexandre, A. D. C., & Pereira, L. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33177-0_14 · Cited by 7
The Role of Open Data in Driving Sectoral Innovation and Global Economic Development
Okunleye, O. J. (2024) · Journal of Engineering Research and Reports · doi:10.9734/jerr/2024/v26i71205 · Cited by 6
Tomczyk, P., Brüggemann, P., & Doligalski, T. (2024) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213024500234 · Cited by 6
Human-centered Approach to the Governance of AI in Higher Education
Selvaratnam, R., & Venaruzzo, L. (2024) · Journal of Ethics in Higher Education · doi:10.26034/fr.jehe.2024.6864 · Cited by 18
Generative artificial intelligence policies: information governance
Wang, X., Liu, B., & Bao, L. (2025) · Management Decision · doi:10.1108/md-06-2024-1422 · Cited by 7
Artificial Intelligence for Impact Assessment of Administrative Burdens
Costa, V., Coelho, P. S., & Castelli, M. (2024) · Emerging Science Journal · doi:10.28991/esj-2024-08-01-019 · Cited by 7
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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