Reasons to Doubt the Impact of AI Risk Evaluations

• URL: http://arxiv.org/abs/2408.02565v1
• Date: Mon, 5 Aug 2024 15:42:51 GMT
• Title: Reasons to Doubt the Impact of AI Risk Evaluations
• Authors: Gabriel Mukobi,
• Abstract summary: This paper asks whether evaluations significantly improve our understanding of AI risks and our ability to mitigate those risks. It concludes with considerations for improving evaluation practices and 12 recommendations for AI labs, external evaluators, regulators, and academic researchers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI safety practitioners invest considerable resources in AI system evaluations, but these investments may be wasted if evaluations fail to realize their impact. This paper questions the core value proposition of evaluations: that they significantly improve our understanding of AI risks and, consequently, our ability to mitigate those risks. Evaluations may fail to improve understanding in six ways, such as risks manifesting beyond the AI system or insignificant returns from evaluations compared to real-world observations. Improved understanding may also not lead to better risk mitigation in four ways, including challenges in upholding and enforcing commitments. Evaluations could even be harmful, for example, by triggering the weaponization of dual-use capabilities or invoking high opportunity costs for AI safety. This paper concludes with considerations for improving evaluation practices and 12 recommendations for AI labs, external evaluators, regulators, and academic researchers to encourage a more strategic and impactful approach to AI risk assessment and mitigation.

Related papers

• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• Risks and NLP Design: A Case Study on Procedural Document QA [52.557503571760215]
  We argue that clearer assessments of risks and harms to users will be possible when we specialize the analysis to more concrete applications and their plausible users. We conduct a risk-oriented error analysis that could then inform the design of a future system to be deployed with lower risk of harm and better performance.
  arXiv  Detail & Related papers  (2024-08-16T17:23:43Z)
• EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Implications for Governance in Public Perceptions of Societal-scale AI Risks [0.29022435221103454]
  Voters perceive AI risks as both more likely and more impactful than experts, and also advocate for slower AI development. Policy interventions may best assuage collective concerns if they attempt to more carefully balance mitigation efforts across all classes of societal-scale risks.
  arXiv  Detail & Related papers  (2024-06-10T11:52:25Z)
• Control Risk for Potential Misuse of Artificial Intelligence in Science [85.91232985405554]
  We aim to raise awareness of the dangers of AI misuse in science. We highlight real-world examples of misuse in chemical science. We propose a system called SciGuard to control misuse risks for AI models in science.
  arXiv  Detail & Related papers  (2023-12-11T18:50:57Z)
• An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI [10.550015825854837]
  A regulatory gap has permitted AI labs to conduct research, development, and deployment activities with minimal oversight. frontier AI system evaluations have been proposed as a way of assessing risks from the development and deployment of frontier AI systems. This paper proposes a solution in the form of an international consortium for AI risk evaluations, comprising both AI developers and third-party AI risk evaluators.
  arXiv  Detail & Related papers  (2023-10-22T23:37:48Z)
• Model evaluation for extreme risks [46.53170857607407]
  Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks.
  arXiv  Detail & Related papers  (2023-05-24T16:38:43Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Quantitative AI Risk Assessments: Opportunities and Challenges [9.262092738841979]
  AI-based systems are increasingly being leveraged to provide value to organizations, individuals, and society. Risks have led to proposed regulations, litigation, and general societal concerns. This paper explores the concept of a quantitative AI Risk Assessment.
  arXiv  Detail & Related papers  (2022-09-13T21:47:25Z)
• Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks [12.927021288925099]
  Artificial intelligence (AI) systems can present risks of events with very high or catastrophic consequences at societal scale. NIST is developing the NIST Artificial Intelligence Risk Management Framework (AI RMF) as voluntary guidance on AI risk assessment and management. We provide detailed actionable-guidance recommendations focused on identifying and managing risks of events with very high or catastrophic consequences.
  arXiv  Detail & Related papers  (2022-06-17T18:40:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.