Vernacularizing Taxonomies of Harm is Essential for Operationalizing Holistic AI Safety

• URL: http://arxiv.org/abs/2410.16562v1
• Date: Mon, 21 Oct 2024 22:47:48 GMT
• Title: Vernacularizing Taxonomies of Harm is Essential for Operationalizing Holistic AI Safety
• Authors: Wm. Matthew Kennedy, Daniel Vargas Campos,
• Abstract summary: Operationalizing AI ethics and safety principles and frameworks is essential to realizing potential benefits and mitigating potential harms caused by AI systems. We argue that taxonomy must also be transferred into local categories to be readily implemented in sector-specific AI safety operationalization efforts. Drawing from emerging anthropological theories of human rights, we propose that the process of "vernacularization" can help bridge this gap.
• Score: 0.0
• License:
• Abstract: Operationalizing AI ethics and safety principles and frameworks is essential to realizing the potential benefits and mitigating potential harms caused by AI systems. To that end, actors across industry, academia, and regulatory bodies have created formal taxonomies of harm to support operationalization efforts. These include novel holistic methods that go beyond exclusive reliance on technical benchmarking. However, our paper argues that such taxonomies must also be transferred into local categories to be readily implemented in sector-specific AI safety operationalization efforts, and especially in underresourced or high-risk sectors. This is because many sectors are constituted by discourses, norms, and values that "refract" or even directly conflict with those operating in society more broadly. Drawing from emerging anthropological theories of human rights, we propose that the process of "vernacularization"--a participatory, decolonial practice distinct from doctrinary "translation" (the dominant mode of AI safety operationalization)--can help bridge this gap. To demonstrate this point, we consider the education sector, and identify precisely how vernacularizing a leading holistic taxonomy of harm leads to a clearer view of how harms AI systems may cause are substantially intensified when deployed in educational spaces. We conclude by discussing the generalizability of vernacularization as a useful AI safety methodology.

Related papers

• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• Crossing the principle-practice gap in AI ethics with ethical problem-solving [0.0]
  How to bridge the principle-practice gap separating ethical discourse from the technical side of AI development remains an open problem. EPS is a methodology promoting responsible, human-centric, and value-oriented AI development. We utilize EPS as a blueprint to propose the implementation of Ethics as a Service Platform.
  arXiv  Detail & Related papers  (2024-04-16T14:35:13Z)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• Learning Machine Morality through Experience and Interaction [3.7414804164475983]
  Increasing interest in ensuring safety of next-generation Artificial Intelligence (AI) systems calls for novel approaches to embedding morality into autonomous agents. We argue that more hybrid solutions are needed to create adaptable and robust, yet more controllable and interpretable agents.
  arXiv  Detail & Related papers  (2023-12-04T11:46:34Z)
• Regulation and NLP (RegNLP): Taming Large Language Models [51.41095330188972]
  We argue how NLP research can benefit from proximity to regulatory studies and adjacent fields. We advocate for the development of a new multidisciplinary research space on regulation and NLP.
  arXiv  Detail & Related papers  (2023-10-09T09:22:40Z)
• Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond [0.0]
  This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks.
  arXiv  Detail & Related papers  (2023-08-31T18:12:12Z)
• Tensions Between the Proxies of Human Values in AI [20.303537771118048]
  We argue that the AI community needs to consider all the consequences of choosing certain formulations of these pillars. We point towards sociotechnical research for frameworks for the latter, but push for broader efforts into implementing these in practice.
  arXiv  Detail & Related papers  (2022-12-14T21:13:48Z)
• Liability regimes in the age of AI: a use-case driven analysis of the burden of proof [1.7510020208193926]
  New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. But there is growing concerns about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties.
  arXiv  Detail & Related papers  (2022-11-03T13:55:36Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [54.478842696269304]
  The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. Our paper addresses both machine learning experts and safety engineers.
  arXiv  Detail & Related papers  (2021-04-29T09:54:54Z)
• Overcoming Failures of Imagination in AI Infused System Development and Deployment [71.9309995623067]
  NeurIPS 2020 requested that research paper submissions include impact statements on "potential nefarious uses and the consequences of failure" We argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances, as well as viable proxies for assessing harms in the widest sense.
  arXiv  Detail & Related papers  (2020-11-26T18:09:52Z)

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.