The Tragedy of the AI Commons

• URL: http://arxiv.org/abs/2006.05203v2
• Date: Mon, 18 Jan 2021 19:07:13 GMT
• Title: The Tragedy of the AI Commons
• Abstract summary: We use evolutionary game dynamics to model a social dilemma in the context of the ethical development of artificial intelligence. We show how effects can help make cooperation viable in such a scenario. They suggest that coordination for a common good should be attempted in smaller groups in which the cost for cooperation is low, and the perceived risk of failure is high.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Policy and guideline proposals for ethical artificial-intelligence research have proliferated in recent years. These are supposed to guide the socially-responsible development of AI for the common good. However, there typically exist incentives for non-cooperation (i.e., non-adherence to such policies and guidelines); and, these proposals often lack effective mechanisms to enforce their own normative claims. The situation just described constitutes a social dilemma; namely, a situation where no one has an individual incentive to cooperate, though mutual cooperation would lead to the best outcome for all involved. In this paper, we use stochastic evolutionary game dynamics to model this social dilemma in the context of the ethical development of artificial intelligence. This formalism allows us to isolate variables that may be intervened upon, thus providing actionable suggestions for increased cooperation amongst numerous stakeholders in AI. Our results show how stochastic effects can help make cooperation viable in such a scenario. They suggest that coordination for a common good should be attempted in smaller groups in which the cost for cooperation is low, and the perceived risk of failure is high. This provides insight into the conditions under which we should expect such ethics proposals to be successful with regard to their scope, scale, and content.

Related papers

• Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI [49.05972944310475]
  We show that when contracts cannot distinguish all relevant future contingencies, there is a strictly positive welfare loss that no realistic mechanism can eliminate.<n>We show that prosocial agents, who weigh others' welfare alongside their own, can close this gap and achieve outcomes that are socially superior and individually beneficial.
  arXiv  Detail & Related papers  (2026-05-08T19:37:07Z)
• Altruism and Fair Objective in Mixed-Motive Markov games [0.0]
  In game theory, social dilemmas entail this dichotomy between individual interest and collective outcome.<n>This paper proposes a novel framework to foster fairer cooperation by replacing the standard utilitarian objective with Proportional Fairness.
  arXiv  Detail & Related papers  (2026-02-09T08:40:52Z)
• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment. We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• A Safe Harbor for AI Evaluation and Red Teaming [124.89885800509505]
  Some researchers fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. We propose that major AI developers commit to providing a legal and technical safe harbor. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.
  arXiv  Detail & Related papers  (2024-03-07T20:55:08Z)
• 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)
• 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)
• Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems [2.4555276449137042]
  Problems of cooperation are omnipresent within human society. As the use of AI becomes more pervasive throughout society, the need for socially intelligent agents is becoming increasingly evident. This paper presents a comprehensive analysis and evaluation of the behaviors and learning dynamics associated with direct punishment, third-party punishment, partner selection, and reputation.
  arXiv  Detail & Related papers  (2023-01-19T19:33:54Z)
• Tackling Asymmetric and Circular Sequential Social Dilemmas with Reinforcement Learning and Graph-based Tit-for-Tat [0.0]
  Social dilemmas offer situations where multiple actors should all cooperate to achieve the best outcome but greed and fear lead to a worst self-interested issue. Recently, the emergence of Deep Reinforcement Learning has generated revived interest in social dilemmas with the introduction of Sequential Social Dilemma (SSD) This paper extends SSD with Circular Sequential Social Dilemma (CSSD), a new kind of Markov games that better generalizes the diversity of cooperation between agents.
  arXiv  Detail & Related papers  (2022-06-26T15:42:48Z)
• 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)
• The art of compensation: how hybrid teams solve collective risk dilemmas [6.081979963786028]
  We study the evolutionary dynamics of cooperation in a hybrid population made of both adaptive and fixed-behavior agents. We show how the first learn to adapt their behavior to compensate for the behavior of the latter.
  arXiv  Detail & Related papers  (2022-05-13T13:23:42Z)
• Normative Disagreement as a Challenge for Cooperative AI [56.34005280792013]
  We argue that typical cooperation-inducing learning algorithms fail to cooperate in bargaining problems. We develop a class of norm-adaptive policies and show in experiments that these significantly increase cooperation.
  arXiv  Detail & Related papers  (2021-11-27T11:37:42Z)
• Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RL [20.22747008079794]
  We study the dynamics of a second-order social dilemma resulting from incentivizing mechanisms. We find that a typical tendency of humans, called homophily, can solve the problem. We propose a novel learning framework to encourage incentive homophily.
  arXiv  Detail & Related papers  (2021-04-23T08:00:45Z)
• Descriptive AI Ethics: Collecting and Understanding the Public Opinion [10.26464021472619]
  This work proposes a mixed AI ethics model that allows normative and descriptive research to complement each other. We discuss its implications on bridging the gap between optimistic and pessimistic views towards AI systems' deployment.
  arXiv  Detail & Related papers  (2021-01-15T03:46:27Z)
• Learning from Learning Machines: Optimisation, Rules, and Social Norms [91.3755431537592]
  It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making. Recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.
  arXiv  Detail & Related papers  (2019-12-29T17:42:06Z)

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.