Towards a Theory of AI Personhood

• URL: http://arxiv.org/abs/2501.13533v1
• Date: Thu, 23 Jan 2025 10:31:26 GMT
• Title: Towards a Theory of AI Personhood
• Authors: Francis Rhys Ward,
• Abstract summary: We outline necessary conditions for AI personhood, focusing on agency, theory-of-mind, and self-awareness. If AI systems can be considered persons, then typical framings of AI alignment may be incomplete.
• Score: 1.6317061277457001
• License:
• Abstract: I am a person and so are you. Philosophically we sometimes grant personhood to non-human animals, and entities such as sovereign states or corporations can legally be considered persons. But when, if ever, should we ascribe personhood to AI systems? In this paper, we outline necessary conditions for AI personhood, focusing on agency, theory-of-mind, and self-awareness. We discuss evidence from the machine learning literature regarding the extent to which contemporary AI systems, such as language models, satisfy these conditions, finding the evidence surprisingly inconclusive. If AI systems can be considered persons, then typical framings of AI alignment may be incomplete. Whereas agency has been discussed at length in the literature, other aspects of personhood have been relatively neglected. AI agents are often assumed to pursue fixed goals, but AI persons may be self-aware enough to reflect on their aims, values, and positions in the world and thereby induce their goals to change. We highlight open research directions to advance the understanding of AI personhood and its relevance to alignment. Finally, we reflect on the ethical considerations surrounding the treatment of AI systems. If AI systems are persons, then seeking control and alignment may be ethically untenable.

Related papers

• The Societal Response to Potentially Sentient AI [0.0]
  Currently, public skepticism about AI sentience remains high. As AI systems advance and become increasingly skilled at human-like interactions, public attitudes may shift. A key question is whether public beliefs about AI sentience will diverge from expert opinions.
  arXiv  Detail & Related papers  (2025-02-01T10:22:04Z)
• Taking AI Welfare Seriously [0.5617572524191751]
  We argue that there is a realistic possibility that some AI systems will be conscious and/or robustly agentic in the near future. It is an issue for the near future, and AI companies and other actors have a responsibility to start taking it seriously.
  arXiv  Detail & Related papers  (2024-11-04T17:57:57Z)
• 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)
• Rolling in the deep of cognitive and AI biases [1.556153237434314]
  We argue that there is urgent need to understand AI as a sociotechnical system, inseparable from the conditions in which it is designed, developed and deployed. We address this critical issue by following a radical new methodology under which human cognitive biases become core entities in our AI fairness overview. We introduce a new mapping, which justifies the humans to AI biases and we detect relevant fairness intensities and inter-dependencies.
  arXiv  Detail & Related papers  (2024-07-30T21:34:04Z)
• Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety [2.3572498744567127]
  We argue that alignment to human intent is insufficient for safe AI systems. We argue that preservation of long-term agency of humans may be a more robust standard.
  arXiv  Detail & Related papers  (2023-05-30T17:14:01Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Some Critical and Ethical Perspectives on the Empirical Turn of AI Interpretability [0.0]
  We consider two issues currently faced by Artificial Intelligence development: the lack of ethics and interpretability of AI decisions. We experimentally show that the empirical and liberal turn of the production of explanations tends to select AI explanations with a low denunciatory power. We propose two scenarios for the future development of ethical AI: more external regulation or more liberalization of AI explanations.
  arXiv  Detail & Related papers  (2021-09-20T14:41:50Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)

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.