Pith. sign in

REVIEW 2 major objections 4 minor 99 references

Defining the scope of AI regulations

T0 review · 2 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read To regulate AI safely, policy makers should stop trying to define "artificial intelligence" and instead define the specific risks—technical approaches, applications, and capabilities—that they want to reduce.

desk verdict Useful policy paper with a real contribution, but the stress-test is right that the risk-based categories individually fail the paper's own 'most important' requirements; the multi-element solution is asserted, not evaluated. read the letter →

arxiv 1909.01095 v3 pith:XDLU2Y6X submitted 2019-08-26 cs.CY

Jonas Schuett This is my paper · ORCID
classification cs.CY
keywords AIregulationlegaldefinitionsrisk-basedapproachmaterialscopeAGItechnicalapproachesapplicationscapabilities
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that the material scope of AI regulations should not be anchored to the term "artificial intelligence," because every existing definition of AI is too vague and over-inclusive to satisfy basic requirements for legal definitions. It proposes a set of six requirements for legal definitions—over-inclusiveness, under-inclusiveness, precision, understandability, practicability, and flexibility—and evaluates existing definitions against them. It then argues that a risk-based approach, defining scope via technical approaches (e.g., reinforcement learning), applications (e.g., facial recognition), and capabilities (e.g., physical interaction with the environment), meets these requirements better and allows finer-grained targeting. The paper concludes that policy makers should define AI regulations by the risks they want to reduce rather than by a contested umbrella term, and suggests this approach also works for future AGI regulation, where the emphasis shifts to capabilities.

What carries the argument

The engine of the argument is a six-requirement checklist for legal definitions (Table 1): over-inclusiveness, under-inclusiveness, precision, understandability, practicability, and flexibility, anchored in proportionality, legal certainty, the vagueness doctrine, effectiveness, and good legislative practice. The checklist does double work: it first eliminates the term "AI" by scoring existing definitions against it (Table 2), then it validates the replacement categories—technical approaches, applications, and capabilities—(Table 3). The positive proposal's operative mechanism is the multi-element scope definition, which combines entries from those three categories so that a regulation applies to precisely those systems whose risks justify it.

What would settle it

Give a panel of lawyers and technical experts the same 100 deployed AI systems and ask them to classify each as in or out of scope under the EU AI Act's current definition; if their classifications are consistently precise and the resulting inclusion set matches the regulation's stated risk objectives without capturing harmless systems, the paper's claim that term-based definitions fail the key requirements would be contradicted.

Watch

Extended reading notes

Core claim

The paper's central claim is that the term "AI" cannot carry the material scope of a regulation because any workable definition of it is over-inclusive, vague, and hard to apply in practice. The author derives six requirements for legal definitions from EU and US legal principles and good legislative practice, then uses them to show that existing definitions from computer science, philosophy, and policy fail the most important tests. The positive alternative is a risk-based scope: define regulation by the main causes of the risks it targets, namely technical approaches (e.g., reinforcement learning), applications (e.g., facial recognition), and capabilities (e.g., physical interaction or automated decision-making). Combining these elements—as in "facial recognition systems for law enforcement purposes based on supervised learning"—meets the requirements better than any single definition of AI, and the same logic can be extended to AGI regulation by leaning more on capability definitions such as recursive self-improvement.

Load-bearing premise

The argument depends on the six requirements in Table 1 being the correct and sufficiently complete standard for legal definitions; if a jurisdiction does not accept those requirements or would weight them differently, the failure of term-based AI definitions and the success of risk-based categories do not automatically follow.

Editorial extensions

If this is right

  • Drafters of future AI regulations can define material scope as a list of technical approaches, applications, and capabilities, without ever using the word "AI."
  • Such scopes can be tailored to the specific risk profile of each system, reducing over-inclusiveness and increasing precision relative to a single umbrella definition.
  • The EU AI Act's current structure is close to this recommendation—Annex I lists technical approaches and Annex III high-risk applications—but making the break from "AI" explicit and distinguishing between technical approaches would sharpen it.
  • For AGI regulation, capability-based definitions such as recursive self-improvement become the most workable route when the future technical approach is unknown.
  • The six-requirement list offers a reusable standard for evaluating any proposed legal definition of a technology.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same decomposition could be applied to other broad regulatory terms, such as automation or algorithmic systems, suggesting that regulators should generally ask which applications and capabilities create harm before deciding what to call the regulated technology.
  • The six-requirement checklist could be operationalized as a scoring rubric for regulatory impact assessments, making trade-offs between precision and future flexibility explicit.
  • A natural empirical test would compare the systems captured by the EU AI Act's current definition with those captured by a risk-based definition using the same risk list; divergence would show how often the choice of definition changes the regulated set.
  • The AGI section implies that legal scholarship could productively focus on defining a small set of risk-relevant capabilities before advanced systems exist, rather than waiting for technical consensus on what counts as general intelligence.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper argues that policy makers should not use the term "artificial intelligence" to define the material scope of AI regulations, because existing AI definitions fail what the author identifies as the most important requirements for legal definitions (over-inclusiveness, under-inclusiveness, precision, understandability, practicability, flexibility). Instead, the author proposes a risk-based approach that defines scope through three categories of risk factors: technical approaches (e.g., reinforcement learning), applications (e.g., facial recognition), and capabilities (e.g., physical interaction). The paper evaluates each category against the six requirements in Table 3, claims that these alternatives meet more requirements than AI definitions, and extends the argument to AGI regulation. The central contribution is a structured set of evaluative criteria and a concrete alternative framework for AI regulation.

Significance. If the argument is accepted, the paper provides a useful, policy-relevant framework for drafting AI regulation, with a clear set of criteria and concrete definitional elements. Its strengths include the systematic derivation of requirements from EU and US legal principles, the survey of existing definitions, and the explicit discussion of AGI as a regulatory target. The paper is clearly written and engages with prior literature. However, the central comparative claim is incomplete: the alternatives are shown to meet more requirements only on precision, understandability, and practicability, while still failing over- and under-inclusiveness, which the paper itself calls most important. The multi-element approach is asserted to reduce over-inclusiveness and increase precision but is never evaluated. These gaps are load-bearing for the main recommendation, so the paper requires substantive revision before its conclusion can be accepted.

major comments (2)
  1. [Section 3.4, Table 3] The central claim that risk-based definitions "meet more of the requirements" is undermined by the paper's own evaluation: technical approaches and capabilities each fail over-inclusiveness and under-inclusiveness, and applications fail under-inclusiveness. Since Section 2.3 identifies over-inclusiveness and vagueness (precision) as the "most important requirements," the table shows that the proposed alternatives do not address the most important failure of AI definitions. The paper should either justify why satisfying precision, understandability, and practicability outweighs continued failure on over- and under-inclusiveness, or it should evaluate a combined multi-element definition against all six requirements. As written, the conclusion that policy makers should favor the risk-based approach does not follow from Table 3.
  2. [Section 3.4, multi-element example] The paper proposes the example "facial recognition systems for law enforcement purposes based on supervised learning" and asserts that this approach allows policy makers to "reduce over-inclusiveness and increase precision," but it never evaluates the example against the requirements in Table 1. On its face, the example appears over-inclusive (it covers all such systems regardless of their actual risk) and under-inclusive (it excludes systems using unsupervised learning or other approaches that could pose comparable risks). Without a systematic evaluation of a multi-element definition against over- and under-inclusiveness, the central recommendation rests on an untested assumption. The author should add an explicit evaluation of the multi-element approach, or qualify the claim accordingly.
minor comments (4)
  1. [Section 2.1] In the sentence about the US Supreme Court, there is a missing space: "According to the(US Supreme Court, 1926, p. 391)" should read "According to the (US Supreme Court, 1926, p. 391)."
  2. [Section 3.1] The phrase "the termssupervised learningand unsupervised learning" appears without spaces around the italicized terms; this should be corrected to "the terms 'supervised learning' and 'unsupervised learning'."
  3. [Section 3.3] The sentence "A third category of AI risk factors is a system’s capabilities" is awkward; consider "A third category of AI risk factors is a system's capabilities" or more simply "A third category is capabilities." Ensure that the possessive is typeset consistently.
  4. [Section 2.2] The reference to "White House, 2020" is listed in the references as "Guidance for regulation of artificial intelligence applications" with a URL; the in-text citation appears once in the list of definitions and once in the discussion of AGI, which is fine, but the reference entry would benefit from a consistent format with other executive documents (e.g., adding the issuing office).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the evaluative criteria are anchored in EU/US legal principles and existing AI definitions, and the proposed risk-based categories are assessed against those same independent criteria.

full rationale

The paper's central claim is a normative legal argument, not a derivation from fitted data or from the authors' own prior results. Section 2.1 derives six requirements (over/under-inclusiveness, precision, understandability, practicability, flexibility) from external legal sources: EU proportionality (TEU Art. 5(4)), EU legal certainty (CJEU Case C-345/06), US vagueness doctrine (Connally v. General Construction Co.), and good legislative practice. These requirements are independent of the proposed alternative definitions. Section 2.3 then applies the requirements to existing AI definitions; Section 3.4 applies the same requirements to definitions of technical approaches, applications, and capabilities. The proposed definitions were not constructed by equating them with the requirements—indeed Table 3 explicitly records failures on over- and under-inclusiveness for all three categories. That the paper's conclusion may overstate the comparative strength of the new categories (since the categories fail the two requirements the paper calls most important) is a correctness or persuasiveness objection, not a circularity. There are no self-citations used as load-bearing evidence, no uniqueness theorem imported from prior work, and no renamed empirical pattern. The author's own caveat that the requirement list is not exhaustive and is limited to EU/US sources is an acknowledged limitation rather than a circular step.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on normative choices about legal-definition criteria and the categorization of AI risks. No numeric free parameters are used. The paper's own caveats about jurisdiction-specific requirements are acknowledged.

assumptions (4)
  • domain assumption The six requirements in Table 1 (over-inclusiveness, under-inclusiveness, precision, understandability, practicability, flexibility) are appropriate criteria for evaluating legal definitions and are grounded in EU/US law.
    Section 2.1 derives these from proportionality, legal certainty, and good legislative practice, but notes the list is not exhaustive and jurisdiction-specific.
  • ad hoc to paper AI risks can be categorized into technical approaches, applications, and capabilities.
    Section 3 introduces this categorization as the paper's own, without a formal justification of exhaustiveness.
  • domain assumption The quoted definitions of reinforcement learning, supervised learning, facial recognition, etc., accurately represent these concepts and are suitable legal definitions.
    Sections 3.1 to 3.3 adopt definitions from technical literature and EU working parties; their precision is asserted, not empirically verified.
  • domain assumption The evaluation of existing AI definitions in Table 2 applies the requirements objectively.
    The author states the evaluation is 'necessarily subjective' but proceeds to categorical yes/no results.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Defining the scope of AI regulations." pith.science (2026). https://pith.science/paper/XDLU2Y6X

@misc{pith2026190901095,
  author       = {Pith},
  title        = {Pith review of: Defining the scope of AI regulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XDLU2Y6X}},
  note         = {Machine review of arXiv:1909.01095}
}
read the original abstract

The paper argues that the material scope of AI regulations should not rely on the term "artificial intelligence (AI)". The argument is developed by proposing a number of requirements for legal definitions, surveying existing AI definitions, and then discussing the extent to which they meet the proposed requirements. It is shown that existing definitions of AI do not meet the most important requirements for legal definitions. Next, the paper argues that a risk-based approach would be preferable. Rather than using the term AI, policy makers should focus on the specific risks they want to reduce. It is shown that the requirements for legal definitions can be better met by defining the main sources of relevant risks: certain technical approaches (e.g. reinforcement learning), applications (e.g. facial recognition), and capabilities (e.g. the ability to physically interact with the environment). Finally, the paper discusses the extent to which this approach can also be applied to more advanced AI systems.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

99 extracted references · 69 canonical work pages

  1. [1]

    , Crump, T

    afsar:2021 APACrefauthors Afsar, M M. , Crump, T. \ Far, B. APACrefauthors \ 2021 . Reinforcement Learning Based Recommender Systems: A Survey Reinforcement learning based recommender systems: A survey . arXiv preprint arXiv:2101.06286 https://arxiv.org/abs/2101.06286

    arXiv 2021

  2. [2]

    , Olah, C

    amodei:2016 APACrefauthors Amodei, D. , Olah, C. , Steinhardt, J. , Christiano, P. , Schulman, J. \ Mané, D. APACrefauthors \ 2016 . Concrete Problems in AI Safety Concrete problems in AI safety . arXiv preprint arXiv:1606.06565 https://arxiv.org/abs/1606.06565

    arXiv 2016

  3. [3]

    , Larson, J

    angwin:2016 APACrefauthors Angwin, J. , Larson, J. , Mattu, S. \ Kirchner, L. APACrefauthors \ 2016 . Machine Bias. Machine bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

    2016

  4. [4]

    APACrefauthors \ 2012

    article29:2012 APACrefauthors Article 29 Data Protection Working Party . APACrefauthors \ 2012 . Opinion 02/2012 on Facial Recognition in Online and Mobile Services. Opinion 02/2012 on facial recognition in online and mobile services. WP192

    2012

  5. [5]

    APACrefauthors \ 2018

    article29:2018 APACrefauthors Article 29 Data Protection Working Party . APACrefauthors \ 2018 . Guidelines on Automated Individual Decision-Making and Profiling for the Purposes of Regulation 2016/679. Guidelines on automated individual decision-making and profiling for the purposes of regulation 2016/679. WP251rev.01

    2018

  6. [6]

    , Cave, M

    baldwin:2011 APACrefauthors Baldwin, R. , Cave, M. \ Lodge, M. APACrefauthors \ 2011 . Understanding Regulation: Theory, Strategy, and Practice Understanding regulation: Theory, strategy, and practice \ ( 2 \ ). Oxford University Press

    2011

  7. [7]

    APACrefauthors \ 2018

    baum:2018 APACrefauthors Baum, S D. APACrefauthors \ 2018 . Reconciliation between Factions Focused on Near-Term and Long-Term Artificial Intelligence Reconciliation between factions focused on near-term and long-term artificial intelligence . AI & Society 33 565--572

    2018

  8. [8]

    , Goertzel, B

    baum:2011 APACrefauthors Baum, S D. , Goertzel, B. \ Goertzel, T G. APACrefauthors \ 2011 . How Long Until Human-Level AI ? Results from an Expert Assessment How long until human-level AI ? Results from an expert assessment . Technological Forecasting and Social Change 78(1) 185--195

    2011

Show all 99 references
  1. [9]

    , Alexandrova, A

    bhatnagar:2018 APACrefauthors Bhatnagar, S. , Alexandrova, A. , Avin, S. , Cave, S. , Cheke, L. , Crosby, M. \ . APACrefauthors \ 2018 . Mapping Intelligence: Requirements and Possibilities Mapping intelligence: Requirements and possibilities . Philosophy and Theory of Artific...

    2018

  2. [10]

    APACrefauthors \ 2010

    black:2010 APACrefauthors Black, J. APACrefauthors \ 2010 . Risk-Based Regulation: Choices, Practices and Lessons Being Learnt Risk-based regulation: Choices, practices and lessons being learnt . OECD Reviews of Regulatory Reform: Risk and Regulatory OECD reviews of regulatory...

    2010

  3. [11]

    , Chang, K W

    bolukbasi:2016 APACrefauthors Bolukbasi, T. , Chang, K W. , Zou, J. , Saligrama, V. \ Kalai, A. APACrefauthors \ 2016 . Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Man is to computer programmer as woman is to homemaker? Debiasing word embe...

    2016

  4. [12]

    APACrefauthors \ 2014

    bostrom:2014 APACrefauthors Bostrom, N. APACrefauthors \ 2014 . Superintelligence: Paths, Dangers, Strategies Superintelligence: Paths, dangers, strategies . Oxford University Press

    2014

  5. [13]

    , Mann, B

    brown:2020 APACrefauthors Brown, T B. , Mann, B. , Ryder, N. , Subbiah, M. , Kaplan, J. , Dhariwal, P. \ . APACrefauthors \ 2020 . Language Models are Few-Shot Learners Language models are few-shot learners . arXiv preprint arXiv:2005.14165 https://arxiv.org/abs/2005.14165

    2020 arXiv

  6. [14]

    , Avin, S

    brundage:2018 APACrefauthors Brundage, M. , Avin, S. , Clark, J. , Toner, H. , Eckersley, P. , Garfinkel, B. \ . APACrefauthors \ 2018 . Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation Malicious use of artificial intelligence: Forecasting, pre...

    2018 arXiv

  7. [15]

    APACrefauthors \ 2019

    buiten:2019 APACrefauthors Buiten, M C. APACrefauthors \ 2019 . Towards Intelligent Regulation of Artificial Intelligence Towards intelligent regulation of artificial intelligence . European Journal of Risk Regulation 10(1) 41--59

    2019

  8. [16]

    \ Gebru, T

    buolamwini:2018 APACrefauthors Buolamwini, J. \ Gebru, T. APACrefauthors \ 2018 . Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification Gender shades: Intersectional accuracy disparities in commercial gender classification . Proceedings of the ...

    2018

  9. [17]

    \ Hernández-Orallo, J

    burden:2020 APACrefauthors Burden, J. \ Hernández-Orallo, J. APACrefauthors \ 2020 . Exploring AI Safety in Degrees: Generality, Capability and Control Exploring AI safety in degrees: Generality, capability and control . Proceedings of the Workshop on Artificial Intelligence S...

    2020

  10. [18]

    APACrefauthors \ 1950

    carnap:1950 APACrefauthors Carnap, R. APACrefauthors \ 1950 . Logical Foundations of Probability Logical foundations of probability . University of Chicago Press

    1950

  11. [19]

    APACrefauthors \ 2021

    carpenter:2021 APACrefauthors Carpenter, B. APACrefauthors \ 2021 . EU Proposing to Regulate the Use of Bayesian Estimation. EU proposing to regulate the use of Bayesian estimation. Statistical Modeling, Causal Inference, and Social Science. https://statmodeling.stat.columbia....

    2021

  12. [20]

    \ Lemley, M A

    casey:2019 APACrefauthors Casey, B. \ Lemley, M A. APACrefauthors \ 2019 . You Might be a Robot You might be a robot . Cornell Law Review 105 287--362

    2019

  13. [21]

    \ Ó hÉigeartaigh , S S

    cave:2019 APACrefauthors Cave, S. \ Ó hÉigeartaigh , S S. APACrefauthors \ 2019 . Bridging Near- and Long-Term Concerns About AI Bridging near- and long-term concerns about AI . Nature Machine Intelligence 1(1) 5--6

    2019

  14. [22]

    APACrefauthors \ 2014

    cellanjones:2014 APACrefauthors Cellan-Jones, R. APACrefauthors \ 2014 . Stephen Hawking Warns Artificial Intelligence Could End Mankind. Stephen Hawking warns artificial intelligence could end mankind. BBC. https://www.bbc.com/news/technology-30290540

    2014

  15. [23]

    APACrefauthors \ 2016

    christiano:2016 APACrefauthors Christiano, P. APACrefauthors \ 2016 . Prosaic AI Alignment. Prosaic AI alignment. https://ai-alignment.com/prosaic-ai-control-b959644d79c2

    2016

  16. [24]

    \ Amodei, D

    clark:2016 APACrefauthors Clark, J. \ Amodei, D. APACrefauthors \ 2016 . Faulty Reward Functions in the Wild. Faulty reward functions in the wild. OpenAI. https://openai.com/blog/faulty-reward-functions

    2016

  17. [25]

    , Fausset, R

    conger:2019 APACrefauthors Conger, K. , Fausset, R. \ Kovaleski, S F. APACrefauthors \ 2019 . San Francisco Bans Facial Recognition Technology. San Francisco bans facial recognition technology. The New York Times. https://www.nytimes.com/2019/05/14/us/facial-recognition-ban-sa...

    2019

  18. [26]

    APACrefauthors \ 2009

    ecj:2009 APACrefauthors Court of Justice of the European Union . APACrefauthors \ 2009 . Gottfried Heinrich . Gottfried Heinrich . Case C-345/06

    2009

  19. [27]

    APACrefauthors \ 2018

    dafoe:2018 APACrefauthors Dafoe, A. APACrefauthors \ 2018 . AI Governance: A Research Agenda. AI governance: A research agenda. Centre for the Governance of AI, Future of Humanity Institute, University of Oxford. https://www.fhi.ox.ac.uk/wp-content/uploads/GovAIAgenda.pdf

    2018

  20. [28]

    APACrefauthors \ 2018

    dastin:2018 APACrefauthors Dastin, J. APACrefauthors \ 2018 . Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-...

    2018

  21. [29]

    APACrefauthors \ 2018

    debenedetto:2018 APACrefauthors De Benedetto , M. APACrefauthors \ 2018 . Effective Law from a Regulatory and Administrative Law Perspective Effective law from a regulatory and administrative law perspective . European Journal of Risk Regulation 9(3) 391--415

    2018

  22. [30]

    APACrefauthors \ 2021

    engler:2021 APACrefauthors Engler, A. APACrefauthors \ 2021 . 6 Developments That Will Define AI Governance in 2021. 6 developments that will define AI governance in 2021. Brookings Institution. https://www.brookings.edu/research/6-developments-that-will-define-ai-governance-in-2021

    2021

  23. [31]

    , Franz, M

    erkin:2009 APACrefauthors Erkin, Z. , Franz, M. , Guajardo, J. , Katzenbeisser, S. , Lagendijk, I. \ Toft, T. APACrefauthors \ 2009 . Privacy-Preserving Face Recognition Privacy-preserving face recognition . Proceedings of the 9th International Symposium on Privacy Enhancing T...

    2009

  24. [32]

    APACrefauthors \ 2016

    etzioni:2016 APACrefauthors Etzioni, O. APACrefauthors \ 2016 . No, the Experts Don’t Think Superintelligent AI is a Threat to Humanity. No, the experts don’t think superintelligent AI is a threat to humanity. MIT Technology Review. https://www.technologyreview.com/2016/09/20/...

    2016

  25. [33]

    APACrefauthors \ 2017

    ec:2017 APACrefauthors European Commission . APACrefauthors \ 2017 . Better Regulation ‘Toolbox’. Better regulation ‘toolbox’. https://ec.europa.eu/info/sites/info/files/better-regulation-toolbox.pdf

    2017

  26. [34]

    APACrefauthors \ 2018

    ec:2018 APACrefauthors European Commission . APACrefauthors \ 2018 . On the Road to Automated Mobility: An EU Strategy for Mobility of the Future. On the road to automated mobility: An EU strategy for mobility of the future. COM(2018) 283 final

    2018

  27. [35]

    APACrefauthors \ 2020

    ec:2020 APACrefauthors European Commission . APACrefauthors \ 2020 . White Paper on Artificial Intelligence: A European Approach to Excellence and Trust. White paper on artificial intelligence: A European approach to excellence and trust. COM(2020) 65 final

    2020

  28. [36]

    APACrefauthors \ 2021

    ec:2021 APACrefauthors European Commission . APACrefauthors \ 2021 . Proposal for a Regulation Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) . Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial ...

    2021

  29. [37]

    \ Almeida, V A F

    gasser:2017 APACrefauthors Gasser, U. \ Almeida, V A F. APACrefauthors \ 2017 . A Layered Model for AI Governance A layered model for AI governance . IEEE Internet Computing 21(6) 58--62

    2017

  30. [38]

    APACrefauthors \ 2019

    germandata:2019 APACrefauthors German Data Ethics Commission . APACrefauthors \ 2019 . Opinion of the Data Ethics Commission . Opinion of the Data Ethics Commission . https://datenethikkommission.de/wp-content/uploads/DEK\_Gutachten\_engl\_bf\_200121.pdf

    2019

  31. [39]

    APACrefauthors \ 2014

    gibbs:2014 APACrefauthors Gibbs, S. APACrefauthors \ 2014 . Elon Musk : Artificial Intelligence is our Biggest Existential Threat. Elon Musk : Artificial intelligence is our biggest existential threat. The Guardian. https://www.theguardian.com/technology/2014/oct/27/elon-musk-...

    2014

  32. [40]

    \ Pennachin, C

    goertzel:2007 APACrefauthors Goertzel, B. \ Pennachin, C. APACrefauthors \ 2007 . Artificial General Intelligence Artificial general intelligence . Springer

    2007

  33. [41]

    APACrefauthors \ 1966

    good:1966 APACrefauthors Good, I J. APACrefauthors \ 1966 . Speculations Concerning the First Ultraintelligent Machine Speculations concerning the first ultraintelligent machine . Advances in Computers 6 31--88

    1966

  34. [42]

    , Salvatier, J

    grace:2018 APACrefauthors Grace, K. , Salvatier, J. , Dafoe, A. , Zhang, B. \ Evans, O. APACrefauthors \ 2018 . When Will AI Exceed Human Performance? Evidence from AI Experts When will AI exceed human performance? Evidence from AI experts . Journal of Artificial Intelligence ...

    2018

  35. [43]

    APACrefauthors \ 2016

    hellgardt:2016 APACrefauthors Hellgardt, A. APACrefauthors \ 2016 . Regulierung und Privatrecht Regulierung und privatrecht . Mohr Siebeck

    2016

  36. [44]

    , Fernando Martínez-Plumed, S A

    hernandezorallo:2019 APACrefauthors Hernández-Orallo, J. , Fernando Martínez-Plumed, S A. \ Ó hÉigeartaigh , S S. APACrefauthors \ 2019 . Surveying Safety-relevant AI Characteristics Surveying safety-relevant AI characteristics . Proceedings of the AAAI Workshop on Artificial ...

    2019

  37. [45]

    APACrefauthors \ 2019

    hlegai:2019 APACrefauthors High-Level Expert Group on AI . APACrefauthors \ 2019 . Ethics Guidelines for Trustworthy AI . Ethics guidelines for trustworthy AI . https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

    2019

  38. [46]

    APACrefauthors \ 2015

    jackson:2015 APACrefauthors Jackson, V C. APACrefauthors \ 2015 . Constitutional Law in an Age of Proportionality Constitutional law in an age of proportionality . Yale Law Journal 124(8) 2680--3203

    2015

  39. [47]

    APACrefauthors \ 2020

    johnson:2020 APACrefauthors Johnson, K. APACrefauthors \ 2020 . Boston Bans Facial Recognition Due to Concern About Racial Bias. Boston bans facial recognition due to concern about racial bias. VentureBeat. https://venturebeat.com/2020/06/24/boston-bans-facial-recognition-due-...

    2020

  40. [48]

    APACrefauthors \ 2019

    kokotajlo:2019 APACrefauthors Kokotajlo, D. APACrefauthors \ 2019 . A Dilemma for Prosaic AI Alignment. A dilemma for prosaic AI alignment. AI Alignment Forum. https://www.alignmentforum.org/posts/jYdAxH8BarPT4fqnb/a-dilemma-for-prosaic-ai-alignment

    2019

  41. [49]

    \ Marcel, S

    korshunov:2018 APACrefauthors Korshunov, P. \ Marcel, S. APACrefauthors \ 2018 . DeepFakes: A New Threat to Face Recognition? Assessment and Detection Deepfakes: A new threat to face recognition? Assessment and detection . arXiv preprint arXiv:1812.08685 https://arxiv.org/abs/...

    2018 arXiv

  42. [50]

    APACrefauthors \ 1990

    kurzweil:1990 APACrefauthors Kurzweil, R. APACrefauthors \ 1990 . The Age of Intelligent Machines The age of intelligent machines . MIT Press

    1990

  43. [51]

    APACrefauthors \ 2015

    lea:2015 APACrefauthors Lea, G. APACrefauthors \ 2015 . Why We Need a Legal Definition of Artificial Intelligence. Why we need a legal definition of artificial intelligence. The Conversation. https://theconversation.com/why-we-need-a-legal-definition-of-artificial-intelligence-46796

    2015

  44. [52]

    \ Hutter, M

    legg:2007 APACrefauthors Legg, S. \ Hutter, M. APACrefauthors \ 2007 . A Collection of Definitions of Intelligence A collection of definitions of intelligence . Proceedings of the 2007 Conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algor...

    2007

  45. [53]

    , Krueger, D

    leike:2018 APACrefauthors Leike, J. , Krueger, D. , Everitt, T. , Martic, M. , Maini, V. \ Legg, S. APACrefauthors \ 2018 . Scalable Agent Alignment via Reward Modeling: A Research Direction Scalable agent alignment via reward modeling: A research direction . arXiv preprint ar...

    2018 arXiv

  46. [54]

    APACrefauthors \ 2019

    martinez:2019 APACrefauthors Martinez, R. APACrefauthors \ 2019 . Artificial Intelligence: Distinguishing between Types & Definitions Artificial intelligence: Distinguishing between types & definitions . Nevada Law Journal 19(3) 1015--1042

    2019

  47. [55]

    APACrefauthors \ 2013

    mattila:2013 APACrefauthors Mattila, H E S. APACrefauthors \ 2013 . Comparative Legal Linguistics: Language of Law, Latin and Modern Lingua Francas Comparative legal linguistics: Language of law, latin and modern lingua francas \ ( 2 \ ). Routledge

    2013

  48. [56]

    APACrefauthors \ 2007

    mccarthy:2007 APACrefauthors McCarthy, J. APACrefauthors \ 2007 . What is Artificial Intelligence? What is artificial intelligence? http://jmc.stanford.edu/articles/whatisai/whatisai.pdf

    2007

  49. [57]

    , Minsky, M L

    mccarthy:1955 APACrefauthors McCarthy, J. , Minsky, M L. , Rochester, N. \ Shannon, C E. APACrefauthors \ 1955 . A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. A proposal for the Dartmouth summer research project on artificial intelligence. ht...

    1955

  50. [58]

    APACrefauthors \ 2011

    meyer:2011 APACrefauthors Meyer, B. APACrefauthors \ 2011 . John McCarthy . John McCarthy . Communications of the ACM. https://cacm.acm.org/blogs/blog-cacm/138907-johnmccarthy/fulltext

    2011

  51. [59]

    APACrefauthors \ 1969

    minsky:1969 APACrefauthors Minsky, M L. APACrefauthors \ 1969 . Semantic Information Processing Semantic information processing . MIT Press

    1969

  52. [60]

    APACrefauthors \ 2021

    mitchell:2021 APACrefauthors Mitchell, M. APACrefauthors \ 2021 . Why AI is Harder than We Think Why AI is harder than we think . arXiv preprint arXiv:2104.12871 https://arxiv.org/abs/2104.12871

    2021 arXiv

  53. [61]

    APACrefauthors \ 2007

    moses:2007 APACrefauthors Moses, L B. APACrefauthors \ 2007 . Recurring Dilemmas: The Law's Race to Keep up with Technological Change Recurring dilemmas: The law's race to keep up with technological change . Journal of Law, Technology & Policy 2 239--285

    2007

  54. [62]

    \ Bostrom, N

    mueller:2016 APACrefauthors Müller, V C. \ Bostrom, N. APACrefauthors \ 2016 . Future Progress in Artificial Intelligence: A Survey of Expert Opinion Future progress in artificial intelligence: A survey of expert opinion . Fundamental Issues of Artificial Intelligence Fundamen...

    2016

  55. [63]

    APACrefauthors \ 2020

    ngo:2020 APACrefauthors Ngo, R. APACrefauthors \ 2020 . AGI Safety from First Principles. AGI safety from first principles. AI Alignment Forum. https://www.alignmentforum.org/s/mzgtmmTKKn5MuCzFJ

    2020

  56. [64]

    APACrefauthors \ 2009

    nilsson:2009 APACrefauthors Nilsson, N J. APACrefauthors \ 2009 . The Quest for Artificial Intelligence: A History of Ideas and Achievements The quest for artificial intelligence: A history of ideas and achievements . Cambridge University Press

    2009

  57. [65]

    APACrefauthors \ 2019 1

    oecd:2019b APACrefauthors OECD . APACrefauthors \ 2019 1 . Artificial Intelligence in Society. Artificial intelligence in society. https://doi.org/10.1787/eedfee77-en

    2019 doi

  58. [66]

    APACrefauthors \ 2019 2

    oecd:2019a APACrefauthors OECD . APACrefauthors \ 2019 2 . Recommendation of the Council on Artificial Intelligence . Recommendation of the Council on Artificial Intelligence . OECD/LEGAL/0449

    2019

  59. [67]

    APACrefauthors \ 2020

    oecd:2020 APACrefauthors OECD . APACrefauthors \ 2020 . A First Look at the OECD’s Framework for the Classification of AI Systems, Designed to Give Policymakers Clarity. A first look at the OECD’s framework for the classification of AI systems, designed to give policymakers cl...

    2020

  60. [68]

    APACrefauthors \ 2019

    officeforai:2019 APACrefauthors Office for AI . APACrefauthors \ 2019 . A Guide to Using Artificial Intelligence in the Public Sector. A guide to using artificial intelligence in the public sector. https://www.gov.uk/government/publications/understanding-artificial-intelligenc...

    2019

  61. [69]

    , Andrychowicz, M

    openai:2019 APACrefauthors OpenAI , Akkaya, I. , Andrychowicz, M. , Chociej, M. , Litwin, M. , McGrew, B. \ . APACrefauthors \ 2019 . Solving Rubik's Cube with a Robot Hand Solving rubik's cube with a robot hand . arXiv preprint arXiv:1910.07113 https://arxiv.org/abs/1910.07113

    2019 arXiv

  62. [70]

    , Baker, B

    openai:2018 APACrefauthors OpenAI , Andrychowicz, M. , Baker, B. , Chociej, M. , Jozefowicz, R. , McGrew, B. \ . APACrefauthors \ 2018 . Learning Dexterous In-Hand Manipulation Learning dexterous in-hand manipulation . arXiv preprint arXiv:1808.00177 https://arxiv.org/abs/1808.00177

    2018 arXiv

  63. [71]

    APACrefauthors \ 2020

    ord:2020 APACrefauthors Ord, T. APACrefauthors \ 2020 . The Precipice: Existential Risk and the Future of Humanity The precipice: Existential risk and the future of humanity . Hachette Books

    2020

  64. [72]

    \ Armstrong, S

    orseau:2016 APACrefauthors Orseau, L. \ Armstrong, S. APACrefauthors \ 2016 . Safely Interruptible Agents Safely interruptible agents . Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence Proceedings of the thirty-second conference on uncertai...

    2016

  65. [73]

    \ Maini, V

    ortega:2018 APACrefauthors Ortega, P A. \ Maini, V. APACrefauthors \ 2018 . Building Safe Artificial Intelligence: Specification, Robustness, and Assurance. Building safe artificial intelligence: Specification, robustness, and assurance. Medium. https://medium.com/@deepmindsaf...

    2018

  66. [74]

    APACrefauthors \ 2013

    price:2013 APACrefauthors Price, J. APACrefauthors \ 2013 . Wagging, Not Barking: Statutory Definitions Wagging, not barking: Statutory definitions . Cleveland State Law Review 60 999--1055

    2013

  67. [75]

    \ Whittlestone, J

    prunkl:2020 APACrefauthors Prunkl, C. \ Whittlestone, J. APACrefauthors \ 2020 . Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society Beyond near- and long-term: Towards a clearer account of research priorities in AI ethics and ...

    2020

  68. [76]

    APACrefauthors \ 2015

    rawlinson:2015 APACrefauthors Rawlinson, K. APACrefauthors \ 2015 . Microsoft's Bill Gates Insists AI is a Threat. Microsoft's Bill Gates insists AI is a threat. BBC. https://www.bbc.com/news/31047780

    2015

  69. [77]

    APACrefauthors \ 2018

    reed:2018 APACrefauthors Reed, C. APACrefauthors \ 2018 . How Should We Regulate Artificial Intelligence? How should we regulate artificial intelligence? Philosophical Transactions of the Royal Society A 376

    2018

  70. [78]

    APACrefauthors \ 2019

    russell:2019 APACrefauthors Russell, S. APACrefauthors \ 2019 . Human Compatible: Artificial Intelligence and the Problem of Control Human compatible: Artificial intelligence and the problem of control . Penguin Random House

    2019

  71. [79]

    \ Norvig, P

    russell:2020 APACrefauthors Russell, S. \ Norvig, P. APACrefauthors \ 2020 . Artificial Intelligence: A Modern Approach Artificial intelligence: A modern approach \ ( 4 \ ). Pearson Education

    2020

  72. [80]

    APACrefauthors \ 2021

    sae:2021 APACrefauthors SAE International . APACrefauthors \ 2021 . Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. J3016\_202104

    2021

  73. [81]

    , Evers, K

    salles:2020 APACrefauthors Salles, A. , Evers, K. \ Farisco, M. APACrefauthors \ 2020 . Anthropomorphism in AI Anthropomorphism in AI . AJOB Neuroscience 11(2) 88--95

    2020

  74. [82]

    , López-Cobo, M

    samoili:2020 APACrefauthors Samoili, S. , López-Cobo, M. , Gómez, E. , Prato, G D. , Martínez-Plumed, F. \ Delipetrev, B. APACrefauthors \ 2020 . AI Watch: Defining Artificial Intelligence. AI watch: Defining artificial intelligence. European Commission. https://doi.org/10.2760/382730

    2020 doi

  75. [83]

    APACrefauthors \ 2016

    scherer:2016 APACrefauthors Scherer, M U. APACrefauthors \ 2016 . Regulating Artificial Intelligence systems: Risks, Challenges, Competencies, and Strategies Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies . Harvard Journal of Law & ...

    2016

  76. [84]

    , Antonoglou, I

    schrittwieser:2020 APACrefauthors Schrittwieser, J. , Antonoglou, I. , Hubert, T. , Simonyan, K. , Sifre, L. , Schmitt, S. \ . APACrefauthors \ 2020 . Mastering Atari , Go, Chess and Shogi by Planning with a Learned Model Mastering Atari , go, chess and shogi by planning with ...

    2020

  77. [85]

    , Evans, R

    senior:2020 APACrefauthors Senior, A W. , Evans, R. , Jumper, J. , Kirkpatrick, J. , Sifre, L. , Green, T. \ . APACrefauthors \ 2020 . Improved Protein Structure Prediction Using Potentials from Deep Learning Improved protein structure prediction using potentials from deep lea...

    2020

  78. [86]

    , Singh, S

    silver:2021 APACrefauthors Silver, D. , Singh, S. , Precup, D. \ Sutton, R S. APACrefauthors \ 2021 . Reward is Enough Reward is enough . Artificial Intelligence 299

    2021

  79. [87]

    APACrefauthors \ 2021

    smuha:2021 APACrefauthors Smuha, N A. APACrefauthors \ 2021 . From a ‘Race to AI ’ to a ‘Race to AI Regulation’: Regulatory Competition for Artificial Intelligence From a ‘race to AI ’ to a ‘race to AI regulation’: Regulatory competition for artificial intelligence . Law, Inno...

    2021

  80. [88]

    APACrefauthors \ 2017

    statecouncil:2017 APACrefauthors State Council of the People’s Republic of China . APACrefauthors \ 2017 . New Generation of Artificial Intelligence Development Plan. New generation of artificial intelligence development plan. https://www.newamerica.org/cybersecurity-initiativ...

    2017

  81. [89]

    , Brooks, R

    stone:2016 APACrefauthors Stone, P. , Brooks, R. , Brynjolfsson, E. , Calo, R. , Etzioni, O. , Hager, G. \ . APACrefauthors \ 2016 . Artificial Intelligence and Life in 2030. Artificial intelligence and life in 2030. Stanford University. http://ai100.stanford.edu/2016-report

    2016

  82. [90]

    \ Barto, A G

    sutton:2018 APACrefauthors Sutton, R S. \ Barto, A G. APACrefauthors \ 2018 . Reinforcement Learning: An Introduction Reinforcement learning: An introduction \ ( 2 \ ). MIT Press

    2018

  83. [91]

    APACrefauthors \ 1950

    turing:1950 APACrefauthors Turing, A M. APACrefauthors \ 1950 . Computing Machinery and Intelligence Computing machinery and intelligence . Mind 59(236) 433--460

    1950

  84. [92]

    APACrefauthors \ 2019

    turner:2019 APACrefauthors Turner, J. APACrefauthors \ 2019 . Robot Rules: Regulating Artificial Intelligence Robot rules: Regulating artificial intelligence . Palgrave Macmillan

    2019

  85. [93]

    APACrefauthors \ 2018

    usdepartment:2018 APACrefauthors US Department of Transportation . APACrefauthors \ 2018 . Preparing for the Future of Transportation. Preparing for the future of transportation. https://www.transportation.gov/av/3/preparing-future-transportation-automated-vehicles-3

    2018

  86. [94]

    APACrefauthors \ 1926

    supremecourt:1926 APACrefauthors US Supreme Court . APACrefauthors \ 1926 . Connally v. General Construction Co. Connally v. General Construction Co. 269 U.S. 385

    1926

  87. [95]

    APACrefauthors \ 2019

    wang:2019 APACrefauthors Wang, P. APACrefauthors \ 2019 . On Defining Artificial Intelligence On defining artificial intelligence . Journal of Artificial General Intelligence 10(2)

    2019

  88. [96]

    APACrefauthors \ 2019

    whitehouse:2019 APACrefauthors White House . APACrefauthors \ 2019 . Maintaining American Leadership in Artificial Intelligence. Maintaining American leadership in artificial intelligence. Executive Order 13859

    2019

  89. [97]

    APACrefauthors \ 2020

    whitehouse:2020 APACrefauthors White House . APACrefauthors \ 2020 . Guidance for Regulation of Artificial Intelligence Applications. Guidance for regulation of artificial intelligence applications. https://www.whitehouse.gov/wp-content/uploads/2020/11/M-21-06.pdf

    2020

  90. [98]

    , Chopra, R

    yim:2020 APACrefauthors Yim, J. , Chopra, R. , Spitz, T. , Winkens, J. , Obika, A. , Kelly, C. \ . APACrefauthors \ 2020 . Predicting Conversion to Wet Age-Related Macular Degeneration Using Deep Learning Predicting conversion to wet age-related macular degeneration using deep...

    2020

  91. [99]

    \ Dafoe, A

    zwetsloot:2019 APACrefauthors Zwetsloot, R. \ Dafoe, A. APACrefauthors \ 2019 . Thinking About Risks from AI : Accidents, Misuse and Structure. Thinking about risks from AI : Accidents, misuse and structure. Lawfare. https://www.lawfareblog.com/thinking-about-risks-ai-accident...

    2019

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.