Ethics dumping in artificial intelligence.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2024-11-08 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1426761
Jean-Christophe Bélisle-Pipon, Gavin Victor
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Abstract

Artificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI's influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation-underscoring the need to examine AI's ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of "ethics dumping" in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem.

人工智能中的伦理倾销。
人工智能(AI)系统不仅包含用于处理和分析数据的统计模型和复杂算法,还包含重要的规范包袱。这一道德层面源自底层代码和训练数据,影响着人工智能给出的建议、表现出的行为和产生的看法。这些因素影响着人工智能的监管、使用、误用以及对最终用户的影响。人工智能影响的多面性引发了科学与技术研究(STS)、伦理、法律与社会影响(ELSI)研究、公共政策分析和负责任的创新等学科的广泛讨论,凸显了研究人工智能伦理影响的必要性。人工智能伦理的最初浪潮侧重于阐明原则和指导方针,而近期的学术研究则越来越强调伦理原则的实际执行、监管监督以及减轻不可预见的负面影响。本文借鉴了研究伦理中的 "伦理倾销 "概念,认为围绕人工智能开发和部署的实践可能会以一种非常令人担忧的方式,将伦理责任从开发者和监管者不适当地推卸给条件不足的用户和主机环境。本文指出了说明这种伦理倾销的四个主要趋势:(1) 人工智能开发者通过编码的价值假设嵌入伦理,(2) 人工智能伦理准则提倡与当地环境脱节的宽泛或不可操作的原则,(3) 机构在实施人工智能系统时不评估伦理影响,以及 (4) 决策者颁布与实践脱节的伦理治理框架。要缓解人工智能伦理倾销问题,就必须增强用户的能力,促进利益相关者参与规范制定,在协调伦理准则的同时为地方差异留出灵活性,并在整个人工智能生态系统中建立明确的问责机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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