The DOI matches its Crossref record; none of the 1 sources its claims cite is checked for support yet.
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Open accessAbstract
As directed by the National Artificial Intelligence Initiative Act of 2020 (P.L. 116-283), the goal of the AI RMF is to offer a resource to the organizations designing, developing, deploying, or using AI systems to help manage the many risks of AI and promote trustworthy and responsible development and use of AI systems. The Framework is intended to be voluntary, rights-preserving, non-sector specific, and use-case agnostic, providing flexibility to organizations of all sizes and in all sectors and throughout society to implement the approaches in the Framework. The AI RMF is intended to be practical, to adapt to the AI landscape as AI technologies continue to develop, and to be operationalized by organizations in varying degrees and capacities so society can benefit from AI while also being protected from its potential harms.
Overview
Integrity signals
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Claims the paper makes
95 Own interpretation · 7 No basis given · 1 Sources not found
In the paper
“NIST has identified three major categories of AI bias to be considered and managed: systemic, computational and statistical, and human-cognitive.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
What the sources say
- Not matchedcites NIST Special Publication 1270
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.
- 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.
- 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
“Systems in which harmful biases are mitigated are not necessarily fair.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
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.
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
“AI systems also bring a set of risks that are not comprehensively addressed by current risk frameworks and approaches.”
- Also stated in: § Appendix B: How AI Risks Differ from Traditional Software Risks · plain
“At the same time, guidance available before publication of this AI RMF does not comprehensively address many AI system risks.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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.
- Also stated in: § Appendix B: How AI Risks Differ from Traditional Software Risks · plain
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
“Each of these can occur in the absence of prejudice, partiality, or discriminatory intent.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
plain- 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.
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
“As a result, the best intentions within one dimension of the AI lifecycle can be undermined via interactions with decisions and conditions in other, later activities.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 5.2 Map
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.
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 interdependencies between these activities, and among the relevant AI actors, can make it difficult to reliably anticipate impacts of AI systems.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 5.2 Map
plainconditional- 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. 97
- It may state two separate claims as one. 37
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 practices can increase the likelihood that risks arising in social contexts are managed appropriately.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3. AI Risks and Trustworthiness
hedgedconditional- 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.
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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
“Deployment of AI systems which are inaccurate, unreliable, or poorly generalized to data and settings beyond their training creates and increases negative AI risks and reduces trustworthiness.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.1 Valid and Reliable
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. 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
“Dealing with tradeoffs requires taking into account the decision-making context.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3. AI Risks and Trustworthiness
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
“Understanding and managing the risks of AI systems will help to enhance trustworthiness, and in turn, cultivate public trust.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Executive Summary
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
“Representing complex human phenomena with mathematical models can come at the cost of removing necessary context.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Additional information
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.
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
“Biases can stem from end-user decision-making tasks and be introduced across the AI lifecycle via human assumptions, expectations, and decisions during design and modeling tasks.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Additional information
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. It may state two separate claims as one.
- Rests on an assumption: Decisions that go into the design, development, deployment, evaluation, and use of AI systems reflect systemic and human cognitive biases.
- Rests on an assumption: AI actors bring their cognitive biases, both individual and group, into the process.
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
“Implementation of this function is enhanced by incorporating perspectives from a diverse internal team and engagement with those external to the team that developed or deployed the AI system.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 5.2 Map
plainconditional- 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
“This broader collective perspective creates opportunities for surfacing problems and identifying existing and emergent risks.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 2. Audience
plaindirect- 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’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
“Where tradeoffs among the trustworthy characteristics arise, measurement provides a traceable basis to inform management decisions.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories
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
“Trustworthy AI depends upon accountability.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Trustworthy AI depends upon accountability. Accountability presupposes transparency.
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
“Identifying and managing AI risks and potential impacts -both positive and negative -requires a broad set of perspectives and actors across the AI lifecycle.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 2. Audience
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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
“Accuracy and robustness contribute to the validity and trustworthiness of AI systems, and can be in tension with one another in AI systems.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > AI RMF 1.0
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.
- 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
“When these variations are judiciously taken into account in organizing human-AI teams, however, they can result in complementarity and improved overall performance.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Additional information
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.
- Rests on an assumption: Human-AI interaction results vary.
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
“Safe operation of AI systems is improved through: • responsible design, development, and deployment practices; • clear information to deployers on responsible use of the system; • responsible decision-making by deployers and end users; and • explanations and documentation of risks based on empirical evidence of incidents.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.2 Safe
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.
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
“Outcomes in the MAP function are the basis for the MEASURE and MANAGE functions.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 5.2 Map
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
“Treating AI risks along with other critical risks, such as cybersecurity and privacy, will yield a more integrated outcome and organizational efficiencies.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.2.4 Organizational Integration and Management of Risk
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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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.
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
“Maintaining organizational practices and governing structures for harm reduction, like risk management, can help lead to more accountable systems.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Trustworthy AI depends upon accountability. Accountability presupposes transparency.
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.
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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.
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
“Approaches which enhance AI trustworthiness can reduce negative AI risks.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3. AI Risks and Trustworthiness
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
“The relationship between risk and accountability associated with AI and technological systems more broadly differs across cultural, legal, sectoral, and societal contexts.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Trustworthy AI depends upon accountability. Accountability presupposes transparency.
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.
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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
“This is difficult to systematize since AI systems carry out different tasks -and perform tasks differently -than humans.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Availability of reliable metrics:
plaindirect- 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’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
“Organizations' risk management efforts will be enhanced by identifying and tracking emergent risks and considering techniques for measuring them.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.2.1 Risk Measurement
hedgeddirect- 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.
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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
“Privacy-related risks may influence security, bias, and transparency and come with tradeoffs with these other characteristics.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.6 Privacy-Enhanced
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. 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
“Accountability presupposes transparency.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Trustworthy AI depends upon accountability. Accountability presupposes transparency.
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
“Strong governance can drive and enhance internal practices and norms to facilitate organizational risk culture.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 5.1 Govern
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. 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
“For example, systems in which predictions are somewhat balanced across demographic groups may still be inaccessible to individuals with disabilities or affected by the digital divide or may exacerbate existing disparities or systemic biases.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
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: 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 loss of context may in turn make it difficult to understand individual and societal impacts that are key to AI risk management efforts.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Additional information
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
“Bias is broader than demographic balance and data representativeness.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
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
“Contextual information gleaned from expert consultation and input from relevant AI actors -established in GOVERN and carried out in MAP -is utilized in this function to decrease the likelihood of system failures and negative impacts.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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.
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
“Organizations' risk management efforts will be enhanced by recognizing and considering these differences.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
hedgeddirect- 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
“These analyses can highlight the existence and extent of tradeoffs between different measures, but they do not answer questions about how to navigate the tradeoff.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3. AI Risks and Trustworthiness
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
“Explainable and interpretable AI systems offer information that will help end users understand the purposes and potential impact of an AI system.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.5 Explainable and Interpretable
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.
- The authors state this as an assumption: Perceptions of negative risk stem from a lack of ability to make sense of, or contextualize, system output appropriately.
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
“Security and resilience are related but distinct characteristics.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.3 Secure and Resilient
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.
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
“Neglecting these characteristics can increase the probability and magnitude of negative consequences.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3. AI Risks and Trustworthiness
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 inability to appropriately measure AI risks does not imply that an AI system necessarily poses either a high or low risk.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.2.1 Risk Measurement
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
“While bias is not always a negative phenomenon, AI systems can potentially increase the speed and scale of biases and perpetuate and amplify harms to individuals, groups, communities, organizations, and society.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
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: 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
“These biases, which are not necessarily always harmful, may be exacerbated by AI system opacity and the resulting lack of transparency.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Additional information
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.
- Rests on an assumption: Decisions that go into the design, development, deployment, evaluation, and use of AI systems reflect systemic and human cognitive biases.
- Rests on an assumption: AI actors bring their cognitive biases, both individual and group, into the process.
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
“Systemic bias can be present in AI datasets, the organizational norms, practices, and processes across the AI lifecycle, and the broader society that uses AI systems.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
hedgedcorrelational- 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
“Many of the data-driven approaches that AI systems rely on attempt to convert or represent individual and social observational and decision-making practices into measurable quantities.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Additional information
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
“This risk-based approach also enables Framework users to compare their approaches with other approaches and to gauge the resources needed (e.g., staffing, funding) to achieve AI risk management goals in a costeffective, prioritized manner.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 6. AI RMF Profiles
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. 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
“Attempting to eliminate negative risk entirely can be counterproductive in practice because not all incidents and failures can be eliminated.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.2.3 Risk Prioritization
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
“A risk management culture can help organizations recognize that not all AI risks are the same, and resources can be allocated purposefully.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.2.3 Risk Prioritization
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. 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
“Successful risk management depends upon a sense of collective responsibility among AI actors shown in Figure 3.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 2. Audience
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.
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
“Both AI and traditional software technologies and systems are subject to rapid innovation.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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
“Bias is tightly associated with the concepts of transparency as well as fairness in society.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
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: 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
“However, it is difficult to determine whether an opaque system possesses such characteristics, and to do so over time as complex systems evolve.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Trustworthy AI depends upon accountability. Accountability presupposes transparency.
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.
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 RMF profiles assist organizations in deciding how they might best manage AI risk that is well-aligned with their goals, considers legal/regulatory requirements and best practices, and reflects risk management priorities.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 6. AI RMF Profiles
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
“Standards of fairness can be complex and difficult to define because perceptions of fairness differ among cultures and may shift depending on application.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
plainconditional- 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
“Employing safety considerations during the lifecycle and starting as early as possible with planning and design can prevent failures or conditions that can render a system dangerous.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.2 Safe
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
“Privacy and cybersecurity risk management considerations and approaches are applicable in the design, development, deployment, evaluation, and use of AI systems.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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
“The information gathered while carrying out the MAP function enables negative risk prevention and informs decisions for processes such as model management, as well as an initial decision about appropriateness or the need for an AI solution.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 5.2 Map
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. 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
“Different types of safety risks may require tailored AI risk management approaches based on context and the severity of potential risks presented.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.2 Safe
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
“Having a diverse team contributes to more open sharing of ideas and assumptions about purposes and functions of the technology being designed, developed, AI RMF 1.0 deployed, or evaluated -which can create opportunities to surface problems and identify existing and emergent risks.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 5. AI RMF Core
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
“Transparency, explainability, and interpretability are distinct characteristics that support each other.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.5 Explainable and Interpretable
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: 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.
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
“Privacy and cybersecurity risks are also considered as part of broader enterprise risk management considerations, which may incorporate AI risks.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
hedgedcorrelational- 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 complexity and varying levels of visibility can introduce uncertainty into risk management practices.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 5.2 Map
plainconditional- 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
“Robustness requires not only that the system perform exactly as it does under expected uses, but also that it should perform in ways that minimize potential harms to people if it is operating in an unexpected setting.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > AI RMF 1.0
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
“Privacy-enhancing technologies (“PETs”) for AI, as well as data minimizing methods such as de-identification and aggregation for certain model outputs, can support design for privacy-enhanced AI systems.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.6 Privacy-Enhanced
plainconditional- 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
“Risk management can enable AI developers and users to understand impacts and account for the inherent limitations and uncertainties in their models and systems, which in turn can improve overall system performance and trustworthiness and the likelihood that AI technologies will be used in ways that are beneficial.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.1 Understanding and Addressing Risks, Impacts, and Harms
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. 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
“Identifying contextual factors in the MAP function will assist AI actors in determining the level of risk and potential management efforts.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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
“Systemic biases at the organizational level can influence how teams are structured and who controls the decision-making processes throughout the AI lifecycle.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Additional information
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.
- Rests on an assumption: Decisions that go into the design, development, deployment, evaluation, and use of AI systems reflect systemic and human cognitive biases.
- Rests on an assumption: AI actors bring their cognitive biases, both individual and group, into the process.
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
“GOVERN is a cross-cutting function that is infused throughout AI risk management and enables the other functions of the process.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 5.1 Govern
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. 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
“Maintaining the provenance of training data and supporting attribution of the AI system's decisions to subsets of training data can assist with both transparency and accountability.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Trustworthy AI depends upon accountability. Accountability presupposes transparency.
plainconditional- 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
“Transparency is often necessary for actionable redress related to AI system outputs that are incorrect or otherwise lead to negative impacts.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Trustworthy AI depends upon accountability. Accountability presupposes transparency.
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: 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
“Effective risk management is realized through organizational commitment at senior levels and may require cultural change within an organization or industry.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.2.4 Organizational Integration and Management of Risk
hedgedconditional- 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
“Explainable systems can be debugged and monitored more easily, and they lend themselves to more thorough documentation, audit, and governance.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.5 Explainable and Interpretable
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. It may state two separate claims as one.
- The authors state this as an assumption: Perceptions of negative risk stem from a lack of ability to make sense of, or contextualize, system output appropriately.
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
“Comparing Current and Target Profiles likely reveals gaps to be addressed to meet AI risk management objectives.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 6. AI RMF Profiles
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.
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 management of AI systems that are intended to augment or replace human activity, for example decision making, requires some form of baseline metrics for comparison.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Availability of reliable metrics:
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
“Addressing AI trustworthiness characteristics individually will not ensure AI system trustworthiness; tradeoffs are usually involved, rarely do all characteristics apply in every setting, and some will be more or less important in any given situation.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3. AI Risks and Trustworthiness
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. 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
“Anticipating, assessing, and otherwise addressing potential sources of negative risk can mitigate this uncertainty and enhance the integrity of the decision process.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 5.2 Map
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
“Systematic documentation practices established in GOVERN and utilized in MAP and MEASURE bolster AI risk management efforts and increase transparency and accountability.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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
“Under certain conditions such as data sparsity, privacyenhancing techniques can result in a loss in accuracy, affecting decisions about fairness and other values in certain domains.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.6 Privacy-Enhanced
plainconditional- 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
“Framework users will have enhanced capacity to manage the risks of deployed AI systems and to allocate risk management resources based on assessed and prioritized risks.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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: 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
“For example, early decisions in identifying purposes and objectives of an AI system can alter its behavior and capabilities, and the dynamics of deployment setting (such as end users or impacted individuals) can shape the impacts of AI system decisions.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 5.2 Map
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. 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
“Ultimately, trustworthiness is a social concept that ranges across a spectrum and is only as strong as its weakest characteristics.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3. AI Risks and Trustworthiness
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
“Measurement of validity, accuracy, robustness, and reliability contribute to trustworthiness and should take into consideration that certain types of failures can cause greater harm.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > AI RMF 1.0
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. 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 biases can also influence downstream decisions by end users, decision makers, and policy makers and may lead to negative impacts.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Additional information
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.
- Rests on an assumption: Decisions that go into the design, development, deployment, evaluation, and use of AI systems reflect systemic and human cognitive biases.
- Rests on an assumption: AI actors bring their cognitive biases, both individual and group, into the process.
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
“Effectively managing the risk of potential harms could lead to more trustworthy AI systems and unleash potential benefits to people (individuals, communities, and society), organizations, and systems/ecosystems.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.1 Understanding and Addressing Risks, Impacts, and Harms
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
“Use of the AI RMF alone will not lead to these changes or provide the appropriate incentives.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.2.4 Organizational Integration and Management of Risk
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
“By promoting higher levels of understanding, transparency increases confidence in the AI system.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Trustworthy AI depends upon accountability. Accountability presupposes transparency.
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
“Computational and statistical biases can be present in AI datasets and algorithmic processes, and often stem from systematic errors due to non-representative samples.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
plainconditional- 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
“Documentation can enhance transparency, improve human review processes, and bolster accountability in AI system teams.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 5.1 Govern
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
“Processes for independent review can improve the effectiveness of testing and can mitigate internal biases and potential conflicts of interest.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories
plainconditional- 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
“Bias exists in many forms and can become ingrained in the automated systems that help make decisions about our lives.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
plainconditional- 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
“Profiles may illustrate and offer insights into how risk can be managed at various stages of the AI lifecycle or in specific sector, technology, or end-use applications.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 6. AI RMF Profiles
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
“Creating trustworthy AI requires balancing each of these characteristics based on the AI system's context of use.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3. AI Risks and Trustworthiness
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
“A transparent system is not necessarily an accurate, privacy-enhanced, secure, or fair system.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Trustworthy AI depends upon accountability. Accountability presupposes transparency.
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
“Some AI system features that present risks also can be beneficial.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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.
- Rests on an assumption: Pre-trained models and transfer learning can advance research and increase accuracy and resilience when compared to other models and approaches.
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
“Human-cognitive biases are omnipresent in decision-making processes across the AI lifecycle and system use, including the design, implementation, operation, and maintenance of AI.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.7 Fair -with Harmful Bias Managed
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
“While there are significant differences between these frameworks based on the domain addressed -and because AI risk management calls for addressing many other types of risks -frameworks like those mentioned above may inform security and privacy considerations in the MAP, MEASURE, and MANAGE functions of the AI RMF.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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.
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
“These frameworks have some features in common with the AI RMF.Like most risk management approaches, they are outcome-based rather than prescriptive and are often structured around a Core set of functions, categories, and subcategories.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Appendix B: How AI Risks Differ from Traditional Software Risks
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
“Unrealistic expectations about risk may lead organizations to allocate resources in a manner that makes risk triage inefficient or impractical or wastes scarce resources.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.2.3 Risk Prioritization
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.
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
“Without contextual knowledge, and awareness of risks within the identified contexts, risk management is difficult to perform.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Categories Subcategories 5.2 Map
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
“Like safety and security, specific technical features of an AI system may promote or reduce privacy.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.6 Privacy-Enhanced
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
“Addressing, documenting, and managing AI risks and potential negative impacts effectively can lead to more trustworthy AI systems.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > Part 1: Foundational Information 1. Framing Risk
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
“AI risks or failures that are not well-defined or adequately understood are difficult to measure quantitatively or qualitatively.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 1.2.1 Risk Measurement
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
“AI systems can also present new risks to privacy by allowing inference to identify individuals or previously private information about individuals.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.6 Privacy-Enhanced
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
“Together, explainability and interpretability assist those operating or overseeing an AI system, as well as users of an AI system, to gain deeper insights into the functionality and trustworthiness of the system, including its outputs.”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.5 Explainable and Interpretable
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.
- The authors state this as an assumption: Perceptions of negative risk stem from a lack of ability to make sense of, or contextualize, system output appropriately.
The paper relies on this without support.
AI technologies pose risks that can negatively impact individuals, groups, organizations, communities, society, the environment, and the planet.
“AI technologies, however, also pose risks that can negatively impact individuals, groups, organizations, communities, society, the environment, and the planet.”
The paper relies on this without support.
AI systems may be trained on data that can change over time, sometimes significantly and unexpectedly, affecting system functionality and trustworthiness in ways that are hard to understand.
“AI systems, for example, may be trained on data that can change over time, sometimes significantly and unexpectedly, affecting system functionality and trustworthiness in ways that are hard to understand.”
The paper relies on this without support.
AI systems and the contexts in which they are deployed are frequently complex, making it difficult to detect and respond to failures when they occur.
“AI systems and the contexts in which they are deployed are frequently complex, making it difficult to detect and respond to failures when they occur.”
The paper relies on this without support.
AI systems are inherently socio-technical in nature, meaning they are influenced by societal dynamics and human behavior.
“AI systems are inherently socio-technical in nature, meaning they are influenced by societal dynamics and human behavior.”
The paper relies on this without support.
AI risks and benefits can emerge from the interplay of technical aspects combined with societal factors related to how a system is used, its interactions with other AI systems, who operates it, and the social context in which it is deployed.
“AI risks -and benefitscan emerge from the interplay of technical aspects combined with societal factors related to how a system is used, its interactions with other AI systems, who operates it, and the social context in which it is deployed.”
The paper relies on this without support.
Without proper controls, AI systems can amplify, perpetuate, or exacerbate inequitable or undesirable outcomes for individuals and communities.
“Without proper controls, AI systems can amplify, perpetuate, or exacerbate inequitable or undesirable outcomes for individuals and communities.”
The paper relies on this without support.
With proper controls, AI systems can mitigate and manage inequitable outcomes.
“With proper controls, AI systems can mitigate and manage inequitable outcomes.”
The paper relies on this without support.
The current lack of consensus on robust and verifiable measurement methods for risk and trustworthiness, and applicability to different AI use cases, is an AI risk measurement challenge.
“The current lack of consensus on robust and verifiable measurement methods for risk and trustworthiness, and applicability to different AI use cases, is an AI risk measurement challenge.”
The paper relies on this without support.
Risk tolerance and the level of risk that is acceptable to organizations or society are highly contextual and application and use-case specific.
“Risk tolerance and the level of risk that is acceptable to organizations or society are highly contextual and application and use-case specific.”
The paper relies on this without support.
Different AI actors have different responsibilities and awareness depending on their roles in the lifecycle.
“Different AI actors have different responsibilities and awareness depending on their roles in the lifecycle.”
The authors state this as an assumption.
Perceptions of negative risk stem from a lack of ability to make sense of, or contextualize, system output appropriately.
“The underlying assumption is that perceptions of negative risk stem from a lack of ability to make sense of, or contextualize, system output appropriately.”
The authors state this as an assumption.
A governance structure is in place.
“Assuming a governance structure is in place, functions may be performed in any order across the AI lifecycle as deemed to add value by a user of the framework.”
The paper relies on this without support.
Pre-trained models and transfer learning can advance research and increase accuracy and resilience when compared to other models and approaches.
“For example, pre-trained models and transfer learning can advance research and increase accuracy and resilience when compared to other models and approaches.”
The paper relies on this without support.
Presenting AI system information to humans is complex; humans perceive and derive meaning from AI system output and explanations in different ways, reflecting different individual preferences, traits, and skills.
“Presenting AI system information to humans is complex.Humans perceive and derive meaning from AI system output and explanations in different ways, reflecting different individual preferences, traits, and skills.”
The paper relies on this without support.
Decisions that go into the design, development, deployment, evaluation, and use of AI systems reflect systemic and human cognitive biases.
“Decisions that go into the design, development, deployment, evaluation, and use of AI systems reflect systemic and human cognitive biases.”
The paper relies on this without support.
AI actors bring their cognitive biases, both individual and group, into the process.
“AI actors bring their cognitive biases, both individual and group, into the process.”
The paper relies on this without support.
Human-AI interaction results vary.
“Human-AI interaction results vary.”
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
“Moreover, this procedural feature operationalizes the integration between what Mökander [44] refers to as 'narrow' and 'broad' auditing, where 'narrow' is exemplified by technology-focused testing or assessments and 'broad' is characterized by process-focused review of management of the technology.”
From A Framework for Assurance Audits of Algorithmic Systems (2024) · cites this paper as [44]
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
“Encapsulating the guardrails in versioned skills also makes them auditable, since any change to a constraint is reviewed and tracked in the same repository as the proposals themselves [10].”
From A Workflow for Grant Discovery and Proposal Development Using Large Language Models: Development and Formative Evaluation (2026) · cites this paper as [10]
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
“Unintentionally, the increased data volume and complexity of generative AI threatens data integrity because errors and biases are prone to occur [63].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [63]
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
“Moreover, this measure is centered on the uniqueness of AI, especially generative AI, in “human centricity, social responsibility, and sustainability” [63].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [63]
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
“The current consensus is that AI trustworthiness itself is a complex measurement with multiple dimensions, such as reliability, resilience, accuracy, and completeness [63,90].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [63]
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
“Standardized risk frameworks such as the NIST AI Risk Management Framework provide a general lifecycle process applicable to any AI system in any sector, but by design they do not address the specific division of labor in hiring, where HR procures a tool that IT deploys [22].”
From Rejected by Code: Algorithmic Bias in AI-Driven Recruitment and the Case for HR-IT Governance Frameworks (2026) · cites this paper as [22]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Risk-based practices have been extended to healthcare AI/ML in not only the medical device domain, such as with AAMI’s Technical Information Report 3497125, but more broadly in emerging frameworks such as the NIST AI Risk Management Framework3, the Whitehouse Blueprint for an AI Bill of Rights5”
From Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions (2023) · cites this paper as [3]
The cited source says what the claim attributes to it.
In the citing paper
“The NIST AI Risk Management Framework, for example, provides a practical structure for identifying and managing risks across the AI lifecycle, while its generative AI profile extends that orientation to risks associated with generative systems (Autio et al., 2024;Tabassi, 2023).”
From Hermeneutic Sovereignty under Generative AI: A Humanities Framework for Protecting Human Meaning-Making from Interpretive Foreclosure (2026) · cites this paper as Tabassi, 2023
The cited source says what the claim attributes to it.
In the citing paper
“The NIST riskmanagement approach is lifecycle-oriented and deliberately broad (Autio et al., 2024;Tabassi, 2023).”
From Hermeneutic Sovereignty under Generative AI: A Humanities Framework for Protecting Human Meaning-Making from Interpretive Foreclosure (2026) · cites this paper as Tabassi, 2023
The cited source says what the claim attributes to it.
In the citing paper
“risk-management frameworks provide valuable organisational scaffolding (Autio et al., 2024;Tabassi, 2023)”
From Hermeneutic Sovereignty under Generative AI: A Humanities Framework for Protecting Human Meaning-Making from Interpretive Foreclosure (2026) · cites this paper as Tabassi, 2023
The cited source says what the claim attributes to it.
In the citing paper
“while NIST's AI RMF includes safety, security, reliability, transparency, explainability, and privacy, in addition to fairness, its guidance for specific evaluation techniques remains fairly broad [17].”
From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [17]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Emerging AI models trained on evolutionary constraints, structural features, or functional data are increasingly used to resolve the significance of VUSs,138,139 and could help reclassify VUSs in known ADRD risk genes (e.g., APP, PSEN1/2, MAPT).140,141”
From Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias (2026) · cites this paper as [140]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“While the idea to follow the financial auditing practices-at least in part-is in itself not new [27,44,57], our paper offers an actionable approach for performing external compliance and assurance audits which is missing in the literature.”
From A Framework for Assurance Audits of Algorithmic Systems (2024) · cites this paper as [44]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“In AI auditing, tensions also arise from disagreements about which role audits ought to play[44].”
From A Framework for Assurance Audits of Algorithmic Systems (2024) · cites this paper as [44]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“While regulatory authorities, AI coalitions, medical device manufacturers, and the medical informatics community have acknowledged the current gap not only in common standards but also in the maturity of HCOs to develop and/or deploy health AI, a primary concern for HCOs remains unresolved”
From Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions (2023) · cites this paper as [3]
Experimental — accuracy not yet measured
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
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.1 Valid and Reliable
“Reliability is defined in the same standard as the "ability of an item to perform as required, without failure, for a given time interval, under given conditions" (Source: ISO/IEC TS 5723:2022).”
§ Artificial Intelligence Risk Management Framework (AI RMF 1.0) > 3.1 Valid and Reliable
“Validation is the "confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled" (Source: ISO 9000:2015).”
Citations
Cited by
Sorted by citations
Items 1,201–1,300 of 1,306
Ji, X., Song, K., & Feng, Y. (2026) · Applied Sciences · doi:10.3390/app162010103
Kutsiuk, D., & Gorokhovskyi, K. (2026) · Cybersecurity Education Science Technique · doi:10.28925/2663-4023.2026.34.1269
Kayadibi, S. Y. (2026) · Discover Robotics · doi:10.1007/s44430-026-00048-y
AI Governance and Ethical Implications for Organizational Oversight in CybersecurityPreprint
Rodriguez, I. J. T. (2026) · Research Square · doi:10.21203/rs.3.rs-10662896/v1
Governança de dados e inteligência artificial na gestão de pequenas e médias empresas
Roncaglio, O. W. F. (2026) · RCMOS - Revista Científica Multidisciplinar O Saber · doi:10.51473/rcmos.v1i1.2026.3032
Operationalising CAPA for AI: Evidence Requirements and Limits from a Production Case StudyPreprint
Kabir-Chisty, M. (2026) · Research Square · doi:10.21203/rs.3.rs-10893823/v1
Generative AI for Decision-Making in Resilient Agro-Ecosystems
Hassani, M. E. A. E., & Rhouiri, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9903-4.ch007
Who gets to verify AI? Provenance, authority and accountability in digital governmentPreprint
Wang, S., Wei, S., Li, P., Chan, S.-K., & Liu, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-10445068/v1
An Integrative AI Governance Framework for Trust-Centric Marketing
Khalaf, A. I., Alkhalidy, K. A., Abdullah, H. O., Hussein, H. A., & Al-Lami, G. T. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1508-7.ch004
Afre, R. A., Thakre, B. B., Panchal, R., & Kadam, S. (2026) · Artificial Intelligence for Sustainable Cities · doi:10.65582/aifsc.2026.013
“The United States National Institute of Standards and Technology’s AI Risk Management Framework provides voluntary guidance for managing AI risks through governance, contextual assessment, measurement, and risk management (Tabassi 2023).”
Securing Intelligent Edge Ecosystems
Babu, C. V. S., William, M. P., Dharshini, B. R., Kumar, M. N., & Kanth, G. P. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0363-3.ch006
Khamula, O., & Strelbycka, S. (2026) · ГРААЛЬ НАУКИ · doi:10.36074/grail-of-science.16.10.2026.026
Sanaguano, F. D. E., Morocho, J. P. O., Flores, C. F. B., & Cruz, C. (2026) · Saber Estratégico Internacional · doi:10.70577/s.e.v4i4.140
The Psychology of Trust in Intelligent Marketing Systems
Abrrow, H. A., Mohammed, A. A., Abdullah, H. O., Noor, A. A. S., & Al-Maliki, D. N. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1508-7.ch017
Workplace Safety Judgment from Images: Comparing AI Assistants Across Five Occupational Contexts
Çögenli, M. Z., & Karadaş, Y. (2026) · Paradigma İktisadi ve İdari Araştırmalar Dergisi · doi:10.37989/paradigmaiiad.2000748
Bortnikov, D. M., Lebedev, M. A., & Репин, Д. А. (2026) · Economics and Management · doi:10.35854/1998-1627-2026-9-1210-1221
Building Customer Trust Through Responsible AI
Farooqi, A. R., & Karim, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1508-7.ch007
Mykhaylova, O. (2026) · Cybersecurity Education Science Technique · doi:10.28925/2663-4023.2026.34.1333
Artificial Intelligence and the Future of Investment Strategy
Khan, R., & Taqi, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9645-3.ch005
Ethics and Governance in Adversarial Deep Learning for Network Intrusion Detection
Abuhashish, F., Maqableh, W. F., Yousef, N., Ahmed, E., & Sunar, M. S. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0363-3.ch007
Navigating the Legal Architecture of AI
Mia, B., Kali, J. B. M., & Tusher, T. A. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-1368-7.ch004
Marchetti, E., Nikghadam-Hojjati, S., Costa, A., Calabrò, A., & Barata, J. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-41230-0_6
Исходные модельные оценки и распределение операций
Хруничев, Р. В., Grinchenko, N. N., Д.В., Г., А.Д., Б., & А.А., К. (2026) · Cifra LLC Journals · doi:10.60797/irj.2026.172.10.6
Хруничев, Р. В., Grinchenko, N. N., Д.В., Г., А.Д., Б., & А.А., К. (2026) · CyberLeninK (CyberLeninka) · doi:10.60797/irj.2026.172.10
Conteh, O. A., Kamara, A., & Conteh, A. K. (2026) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/26oct578
Хруничев, Р. В., Grinchenko, N. N., Д.В., Г., А.Д., Б., & А.А., К. (2026) · Cifra LLC Journals · doi:10.60797/irj.2026.172.10.4
Ajdari, B., Barutti, A., & Cepolina, E. M. (2026) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa68377.2026.11725169
Запас значения Ia до ближайшей проектной границы
Хруничев, Р. В., Grinchenko, N. N., Д.В., Г., А.Д., Б., & А.А., К. (2026) · Cifra LLC Journals · doi:10.60797/irj.2026.172.10.7
Хруничев, Р. В., Grinchenko, N. N., Д.В., Г., А.Д., Б., & А.А., К. (2026) · Cifra LLC Journals · doi:10.60797/irj.2026.172.10.1
Deniz, V. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1517.c6107
Ioannou, I. I., & Vassiliou, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2518-5.ch007
Manca, G., Windmann, A., Dix, M., et al. (2026) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa68377.2026.11725192
“AI risk frameworks, cyber-resilience guidance, and MLOps practices add useful concepts for model documentation, monitoring, lifecycle control, and deployment readiness [49,52,57].”
Yang, T., Zhang, X., & Ying, W. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-41230-0_10
Сопоставление подходов для интеллектуального выполнения бизнес-процессов
Хруничев, Р. В., Grinchenko, N. N., Д.В., Г., А.Д., Б., & А.А., К. (2026) · Cifra LLC Journals · doi:10.60797/irj.2026.172.10.5
Соответствие показателя Ia предварительному уровню автономности
Хруничев, Р. В., Grinchenko, N. N., Д.В., Г., А.Д., Б., & А.А., К. (2026) · Cifra LLC Journals · doi:10.60797/irj.2026.172.10.2
Secure Design Principles for AI-Driven Learning Platforms
Yonga, E. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2518-5.ch006
Хруничев, Р. В., Grinchenko, N. N., Д.В., Г., А.Д., Б., & А.А., К. (2026) · Cifra LLC Journals · doi:10.60797/irj.2026.172.10.3
Generative AI Decision Support in B2C E-Commerce: A Consumer Buying Behaviour Framework
Ilieva, G. (2026) · Electronics · doi:10.3390/electronics15204781
Algoritmik Hemşirelik: Yapay Zekâ Hemşirelik Sezgisine Rakip Mi, Tamamlayıcı Mı?
KARAMAN, F., & Aktaş, A. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1519.c6123
Örgütlerde Yapay Zekâ Entegrasyonu ve Stratejik Dönüşüm Dinamikleri
AYRAL, G. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1539.c6223
Can AI help us hear each other? A restorative listening framework for interfaith youth dialogue
Dursun, E., Akgobek, C., & Yilmaz, M. (2026) · Proceedings of London International Conferences · doi:10.31039/plic.2026.17.403
MIT’nin Eğitimde Yapay Zekâ Kullanımı Raporundan Hareketle Örgütlerde Yapay Zekâ Entegrasyonu
Delibaş, M. B. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1551.c6293
Altan, S. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1539.c6225
Trustworthy AI-Assisted Environmental Communication: A Verification-Centered Governance Framework
Euajarusphan, A., & Banterng, T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-6910-5_6
G-AIM: A General AI Governance Model for the Responsible Adoption of AI and Generative AI
Marques, G. D. S., Batista, V. V., Canedo, E. D., Praciano, F. G., Gondim, J. J. C., & de Mendonça, F. L. L. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-34132-7_12
Vodenska, I., Choi, K.-S., & Chitkushev, L. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-42320-7_21
Sengupta, S. (2026) · World Journal of Engineering and Technology · doi:10.4236/wjet.2026.144053
Amodu, O. A., Olalekan, O. S., Althumali, H., Mahmood, R. A. R., Elesemoyo, I. O., Adam, M. S., & Farid, M. (2026) · Drones · doi:10.3390/drones10100783
Reddy, Y. A., Priya, A. S., Reddy, K. S., et al. (2026) · International Conference on Educational Technology, Language and Technical Communication (ETLTC) · doi:10.3390/engproc2026143080
Artificial Intelligence and Legal Transformation in the Global South
Butt, M. F., & Asghar, M. S. (2026) · doi:10.65758/isbn.9786277813673
Tsoni, R., Μιχιώτης, Α., Κουτούζης, Μ., & Verykios, V. S. (2026) · Pan-Hellenic Conference on Progress in Computing and Informatics (PCI) · doi:10.1145/3829216.3829329
Gong, H., Samawi, M., & Medda, F. (2026) · Systems · doi:10.3390/systems14101329
Rethinking AI governance for social work through the triple mandate
Ahn, E., Choi, M., McNellan, C. R., et al. (2026) · The British Journal of Social Work · doi:10.1093/bjsw/bcag260
The Identity Fabric: Governing Delegation in Autonomous AI Systems
Goradia, J. (2026) · IEEE Security & Privacy · doi:10.1109/msec.2026.3707774
“7,9 A utonomous AI agents are reshaping how authority is created and exercised in modern systems.”
Li, J., Li, X., Shang, Y., et al. (2026) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec69754.2026.11732789
“The NIST AI Risk Management Framework emphasizes validity and reliability, safety, security and resilience, accountability, transparency, explainability, privacy, and fairness [7] .”
Faccia, A., & Itani, R. (2026) · FinTech · doi:10.3390/fintech5040097
AI-Powered Cyber Attacks and Defence Mechanisms
Sarvaiya, M. G. M. D. S. B., & Borade, P. A. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-38948
Ramphabana, F., Swana, E. F., & Paepae, T. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8100343
Концепция нормативной социализации ИИ как возможный подход к глобальному управлению технологией
Юрьевич, К. А. (2026) · Азиатско-Тихоокеанский регион Экономика политика право · doi:10.24866/1813-3274/2026-3/108-121
Udaykumar, V. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.5532
Return on AI (RoAI): A framework for assessing AI investments in the marketing organization
Kanuri, V. K., Pattabhiramaiah, A., & Sridhar, S. (2026) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-026-01217-6
Ladhania, R., Ho, A. Z., Dhillon, P. S., Brummett, C. M., Singh, K., & Fernandez, A. C. (2026) · npj Health Systems · doi:10.1038/s44401-026-00158-0
López, I. M. G., Espinosa, J. M. M., Montoya, M. S. R., et al. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1885306
From AI Adoption to Institutional Preparedness: Governing Enterprises for AGI
Sun, F. (2026) · AI & Future Society · doi:10.63802/afs.v2.i2.324
Figueroa, R. (2026) · Journal of Space Safety Engineering · doi:10.1016/j.jsse.2026.10.004
Biloborodova, T., Trump, B. D., Chemeris, A., et al. (2026) · Environment Systems & Decisions · doi:10.1007/s10669-026-10151-5
From Risk to Resilience Through Digital Pathways in Service-Based Supply Chain Recovery
Thuy, D. N., Dinh, L. N., & Vu, H. N. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-1182-9.ch011
Egorova, A., Abdulkadirov, T., Ryabova, E., Morozova, Y., Axyonov, A., & Ryumin, D. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10100358
Abuzaid, A. A. A. (2026) · Journal of Oncology Pharmacy Practice · doi:10.1177/10781552261498674
Explainable AI for Mechanistic Toxicology and Regulatory-Grade Safety Assessment
Alzoubi, A. E., Wahed, M. K. Y. A., & Wahed, S. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8562-4.ch011
Interpretable Multi-Modal Reinforcement Learning for 3D Search-and-Rescue Decision Support
Sugimoto, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-4876-4.ch006
Law and technology convergence: Artificial intelligence and blockchain regulation
Sartaev, S. A., Kalshabaeva, M. Z., & Altynbekova, A. (2026) · Corporate Law & Governance Review · doi:10.22495/clgrv8i4p4
A Modality-Adaptive Software Architecture for Explainable Clinical Decision Support
Lissovoy, D., Khalyk, A., Oralov, A., Mustafin, R., Kenzhegaliyev, A., & Ali, H. (2026) · GlobeIS International Journal of Global Information Systems · doi:10.66395/globeis.25
Karimova, G. A. (2026) · Buxoro xalqaro universiteti ilmiy axborotnomasi · doi:10.67780/buxoro-xalqaro-universiteti-ilmiy-axborotnomasi.2026.5ab5856d
The REAL framework: evaluating the cognitive and formative impact of generative AI
Jones, M. R. (2026) · Journal of Ethics in Entrepreneurship and Technology · doi:10.1108/jeet-08-2026-0046
Schwebs, C., & Barone, P. M. (2026) · Forensic Sciences · doi:10.3390/forensicsci6040095
A practice-ready framework for measurable AI ethics in education
Küçükuncular, A. (2026) · SN Social Sciences · doi:10.1007/s43545-026-01865-z
Ke, S. (2026) · International Conference on Big Data and Intelligent Medicine (BDIMed) · doi:10.1145/3816325.3843671
Youssef, A. O., Salam, M. A., Hemeda, O. T., et al. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02631-z
Liu, Y.-W. (2026) · International Journal of Law Management & Humanities · doi:10.63108/ijlmh.13101
Lu, T., & Xia, Z. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02638-6
AI agents accelerate the discovery and engineering of food molecules
Weng, C., Zhao, C., Lu, B., Ai, L., & Yao, Y. (2026) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2026.106188
Xie, Y., Liu, Y., & Ning, H. (2026) · International Journal of Financial Studies · doi:10.3390/ijfs14110292
ASSURE-BVLOS: runtime assurance for risk-adaptive space–air–ground unmanned aircraft operations
Barua, N. (2026) · Aerospace Systems · doi:10.1007/s42401-026-00565-z
Demirsoy, H. B., & Akcayol, M. A. (2026) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap71869.2026.11728053
DMAIC, Augmented: Generative AI Strengthens Lean Six Sigma Execution
Smirnov, O. (2026) · American Journal of Mechanical and Industrial Engineering · doi:10.11648/j.ajmie.20261105.15
Silva, I. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21361334
Takahashi, K. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20116148
SINT Protocol: Runtime Authorization and Evidence Logging for LLM-Driven Physical AI SystemsPreprint
Pashkov, I. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20389766
Liu, C., Chen, C.-J., & Ko, Y.-L. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1965076
Public Data as Public Health Infrastructure
Chen, D. D., Karpyn, A. E., Dillard, D., et al. (2026) · Delaware Journal of Public Health · doi:10.32481/djph.2026.10.05
Metadata
- Authors
- Tabassi, E.
- Corresponding author
- Tabassi, Elham
- ORCID
- Tabassi, Elham
- Published
- 2023-01-26
- OpenAlex
- W4317748910
- Cited by
- 2,951
- References
- 0
- Citations by year
- 2026: 2184 · 2025: 458 · 2024: 203 · 2023: 100
- Type
- report
- Language
- en
- Open access
- gold
- doi.org
- Locations
- University of North Texas Digital Library (University of North Texas) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Standards and Technology
- Information Technology Laboratory
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- AI risk management framework, trustworthy AI, responsible AI development, AI harms, AI governance
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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