Exploring the Impact of Rewards on Developers' Proactive AI Accountability Behavior

• URL: http://arxiv.org/abs/2411.18393v1
• Date: Wed, 27 Nov 2024 14:34:44 GMT
• Title: Exploring the Impact of Rewards on Developers' Proactive AI Accountability Behavior
• Authors: L. H. Nguyen, S. Lins, G. Du, A. Sunyaev,
• Abstract summary: We develop a theoretical model grounded in Self-Determination Theory to uncover the potential impact of rewards and sanctions on AI developers. We identify typical sanctions and bug bounties as potential reward mechanisms by surveying related research from various domains.
• Score: 0.0
• License:
• Abstract: The rapid integration of Artificial Intelligence (AI)-based systems offers benefits for various domains of the economy and society but simultaneously raises concerns due to emerging scandals. These scandals have led to the increasing importance of AI accountability to ensure that actors provide justification and victims receive compensation. However, AI accountability has a negative connotation due to its emphasis on penalizing sanctions, resulting in reactive approaches to emerging concerns. To counteract the prevalent negative view and offer a proactive approach to facilitate the AI accountability behavior of developers, we explore rewards as an alternative mechanism to sanctions. We develop a theoretical model grounded in Self-Determination Theory to uncover the potential impact of rewards and sanctions on AI developers. We further identify typical sanctions and bug bounties as potential reward mechanisms by surveying related research from various domains, including cybersecurity.

Related papers

• AI-Enabled Rent-Seeking: How Generative AI Alters Market Transparency and Efficiency [7.630624512225164]
  generative artificial intelligence (AI) has transformed the information environment, creating both opportunities and challenges. This paper explores how generative AI influences economic rent-seeking behavior and its broader impact on social welfare.
  arXiv  Detail & Related papers  (2025-02-18T15:40:22Z)
• Predicting the Impact of Generative AI Using an Agent-Based Model [0.0]
  Generative artificial intelligence (AI) systems have transformed industries by autonomously generating content that mimics human creativity. This paper employs agent-based modeling (ABM) to explore these implications. The ABM integrates individual, business, and governmental agents to simulate dynamics such as education, skills acquisition, AI adoption, and regulatory responses.
  arXiv  Detail & Related papers  (2024-08-30T13:13:56Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• 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)
• 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)
• AI Liability Insurance With an Example in AI-Powered E-diagnosis System [22.102728605081534]
  We use an AI-powered E-diagnosis system as an example to study AI liability insurance. We show that AI liability insurance can act as a regulatory mechanism to incentivize compliant behaviors and serve as a certificate of high-quality AI systems.
  arXiv  Detail & Related papers  (2023-06-01T21:03:47Z)
• 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)
• Accountability in AI: From Principles to Industry-specific Accreditation [4.033641609534416]
  Recent AI-related scandals have shed a spotlight on accountability in AI. This paper draws on literature from public policy and governance to make two contributions.
  arXiv  Detail & Related papers  (2021-10-08T16:37:11Z)
• 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)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

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.