Taking AI Welfare Seriously

• URL: http://arxiv.org/abs/2411.00986v1
• Date: Mon, 04 Nov 2024 17:57:57 GMT
• Title: Taking AI Welfare Seriously
• Authors: Robert Long, Jeff Sebo, Patrick Butlin, Kathleen Finlinson, Kyle Fish, Jacqueline Harding, Jacob Pfau, Toni Sims, Jonathan Birch, David Chalmers,
• Abstract summary: 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.
• Score: 0.5617572524191751
• License:
• Abstract: In this report, we argue that there is a realistic possibility that some AI systems will be conscious and/or robustly agentic in the near future. That means that the prospect of AI welfare and moral patienthood, i.e. of AI systems with their own interests and moral significance, is no longer an issue only for sci-fi or the distant future. It is an issue for the near future, and AI companies and other actors have a responsibility to start taking it seriously. We also recommend three early steps that AI companies and other actors can take: They can (1) acknowledge that AI welfare is an important and difficult issue (and ensure that language model outputs do the same), (2) start assessing AI systems for evidence of consciousness and robust agency, and (3) prepare policies and procedures for treating AI systems with an appropriate level of moral concern. To be clear, our argument in this report is not that AI systems definitely are, or will be, conscious, robustly agentic, or otherwise morally significant. Instead, our argument is that there is substantial uncertainty about these possibilities, and so we need to improve our understanding of AI welfare and our ability to make wise decisions about this issue. Otherwise there is a significant risk that we will mishandle decisions about AI welfare, mistakenly harming AI systems that matter morally and/or mistakenly caring for AI systems that do not.

Related papers

• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• AI Consciousness and Public Perceptions: Four Futures [0.0]
  We investigate whether future human society will broadly believe advanced AI systems to be conscious. We identify four major risks: AI suffering, human disempowerment, geopolitical instability, and human depravity. The paper concludes with the main recommendations to avoid research aimed at intentionally creating conscious AI.
  arXiv  Detail & Related papers  (2024-08-08T22:01:57Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• 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)
• Aligning Artificial Intelligence with Humans through Public Policy [0.0]
  This essay outlines research on AI that learn structures in policy data that can be leveraged for downstream tasks. We believe this represents the "comprehension" phase of AI and policy, but leveraging policy as a key source of human values to align AI requires "understanding" policy.
  arXiv  Detail & Related papers  (2022-06-25T21:31:14Z)
• 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)
• 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)
• Socially Responsible AI Algorithms: Issues, Purposes, and Challenges [31.382000425295885]
  Technologists and AI researchers have a responsibility to develop trustworthy AI systems. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness.
  arXiv  Detail & Related papers  (2021-01-01T17:34:42Z)
• AI Failures: A Review of Underlying Issues [0.0]
  We focus on AI failures on account of flaws in conceptualization, design and deployment. We find that AI systems fail on account of omission and commission errors in the design of the AI system. An AI system is quite likely to fail in situations where, in effect, it is called upon to deliver moral judgments.
  arXiv  Detail & Related papers  (2020-07-18T15:31:29Z)
• AI loyalty: A New Paradigm for Aligning Stakeholder Interests [0.0]
  We argue that AI loyalty should be considered during the technological design process alongside other important values in AI ethics. We discuss a range of mechanisms that could support incorporation of AI loyalty into a variety of future AI systems.
  arXiv  Detail & Related papers  (2020-03-24T23:55:59Z)

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.