Bélisle-Pipon Jean-Christophe, Victor Gavin
Health Sciences Department, Simon Fraser University, Burnaby, BC, Canada.
Philosophy Department, Simon Fraser University, Burnaby, BC, Canada.
Front Artif Intell. 2024 Nov 8;7:1426761. doi: 10.3389/frai.2024.1426761. eCollection 2024.
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（AI）系统不仅编码了旨在处理和分析数据的统计模型和复杂算法，还承载着重要的规范性包袱。这种源于底层代码和训练数据的伦理维度，塑造了人工智能给出的建议、表现出的行为以及产生的认知。这些因素影响着人工智能的监管、使用、滥用方式以及对终端用户的影响。人工智能影响的多面性引发了科学技术研究（STS）、伦理、法律和社会影响（ELSI）研究、公共政策分析以及负责任创新等跨学科领域的广泛讨论，凸显了审视人工智能伦理影响的必要性。虽然人工智能伦理的第一波浪潮侧重于阐明原则和指导方针，但最近的学术研究越来越强调伦理原则的实际实施、监管监督以及减轻不可预见的负面后果。借鉴研究伦理中的“伦理倾销”概念，本文认为，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，不恰当地将伦理责任从开发者和监管者转移到准备不足的用户和宿主环境身上。确定了说明这种伦理倾销的四个关键趋势：（1）人工智能开发者通过编码的价值假设嵌入伦理；（2）人工智能伦理准则推广与当地背景脱节的宽泛或无法实施的原则；（3）机构在未评估伦理影响的情况下实施人工智能系统；（4）决策者制定与实践脱节的伦理治理框架。减轻人工智能伦理倾销需要赋予用户权力，促进利益相关者参与规范制定，在统一伦理准则的同时允许因地制宜的灵活性，并在人工智能生态系统中建立明确的问责机制。
