迈向价值一致系统的步骤

Osonde A. Osoba, Benjamin Boudreaux, Douglas Yeung
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引用次数: 4

摘要

算法(包括AI/ML)决策工件是我们决策生态系统中已建立并不断发展的一部分。它们现在是不可或缺的工具,帮助我们管理海量信息,在这个复杂的世界里做出有效的决策。当前的文献中充满了个体人工制品如何违反社会规范和期望的例子(例如,违反公平、隐私或安全规范)。在此背景下,本讨论强调了研究主体中一个未被强调的观点,该研究侧重于评估配备人工智能的社会技术系统中的价值错位。到目前为止,对价值偏差的研究主要集中在单个技术工件的行为上。这一讨论主张在社会技术系统中采用一种更结构化的系统级方法来评估价值一致性。我们主要依靠对公平的研究来使我们的论点更加具体。并且我们利用这个机会强调采用系统视角如何提高我们更好地解释和处理价值偏差的能力。我们的讨论以优先级问题的探索结束,如果我们要确保整个系统的价值一致性,而不仅仅是单个工件,就需要注意这些优先级问题。
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Steps Towards Value-Aligned Systems
Algorithmic (including AI/ML) decision-making artifacts are an established and growing part of our decision-making ecosystem. They are now indispensable tools that help us manage the flood of information we use to try to make effective decisions in a complex world. The current literature is full of examples of how individual artifacts violate societal norms and expectations (e.g. violations of fairness, privacy, or safety norms). Against this backdrop, this discussion highlights an under-emphasized perspective in the body of research focused on assessing value misalignment in AI-equipped sociotechnical systems. The research on value misalignment so far has a strong focus on the behavior of individual tech artifacts. This discussion argues for a more structured systems-level approach for assessing value-alignment in sociotechnical systems. We rely primarily on the research on fairness to make our arguments more concrete. And we use the opportunity to highlight how adopting a system perspective improves our ability to explain and address value misalignments better. Our discussion ends with an exploration of priority questions that demand attention if we are to assure the value alignment of whole systems, not just individual artifacts.
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