From Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research
From Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research
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DOI：
10.1145/3600211.3604661
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Michael Feffer;M. Skirpan;Z. Lipton;Hoda Heidari
中科院分区：
文献类型：
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作者：
Michael Feffer;M. Skirpan;Z. Lipton;Hoda Heidari
人工智能伦理社区面临着赋予利益相关者和受影响的社区成员权力的紧迫性，以便他们能够审查和影响高风险领域人工智能系统的设计，开发和使用。虽然最近越来越多的论文引起了人们对所谓的“参与式ML”方法的兴趣，但参与应该采取什么形式以及如何实现这些目标却很少有人提出。我们对相关文献的调查表明，在许多论文中，参与被简化为高度结构化的计算机制，旨在引出数学上易于处理的狭义道德价值观的近似值。在实际与真实的人接触的论文中，这些接触通常包括与通常不代表相关利益相关者的个人的一次性互动。出于这些明显的局限性，我们引入了一套统一的轴来评估和改善参与式方法。我们使用这些轴来分析这个领域的当代工作，并概述未来的人工智能研究方向，这些方向可以为实现参与的理想做出有意义的贡献。
The AI Ethics community faces an imperative to empower stakeholders and impacted community members so that they can scrutinize and influence the design, development, and use of AI systems in high-stakes domains. While a growing chorus of recent papers has kindled interest in so-called “participatory ML” methods, precisely what form participation ought to take and how to operationalize these ambitions are seldom addressed. Our survey of the relevant literature shows that in many papers, participation is reduced to highly structured, computational mechanisms designed to elicit mathematically tractable approximations of narrowly-defined moral values. Of papers that actually engage with real people, these engagements typically consist of one-time interactions with individuals that are often unrepresentative of the relevant stakeholders. Motivated by these clear limitations, we introduce a consolidated set of axes to evaluate and improve participatory approaches. We use these axes to analyze contemporary work in this space and outline future AI research directions that could meaningfully contribute to operationalizing the ideal of participation.