Responsible Agency Through Answerability
Responsible Agency Through Answerability
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责任机构通过责任性
DOI：
10.1145/3597512.3597529
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Hatherall L
中科院分区：
文献类型：
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作者：
Hatherall L
最近，机器学习技术的快速进步重新点燃了关于智能系统中所谓的“责任缺口”的长达数十年的辩论，机器学习技术提供了马蒂亚斯最初预期的许多机器自主能力。智能学习系统的新兴能力突出并加剧了现有的挑战，人类对这类系统的行动和影响进行了有意义的控制和问责。在最近关于责任差距的文献[2，3]中，人类对系统行为和危害的“责任”的相关挑战已经成为焦点。我们描述了一种建议的跨学科方法来设计自主系统中的可回答问题，该方法基于从道德哲学和认知科学中提取的工具主义框架，以及来自健康、金融和政府应用领域的结构化访谈和焦点小组的经验结果。我们概述了一个原型对话代理，它受到这些新出现的结果的启发，旨在帮助弥合组织中的结构性差距，这些差距通常会阻碍负责自主社会技术系统的人类代理对负责的脆弱患者负责。
The decades-old debate over so-called ‘responsibility gaps’ in intelligent systems has recently been reinvigorated by rapid advances in machine learning techniques that are delivering many of the capabilities of machine autonomy that Matthias [1] originally anticipated. The emerging capabilities of intelligent learning systems highlight and exacerbate existing challenges with meaningful human control of, and accountability for, the actions and effects of such systems. The related challenge of human ‘answerability’ for system actions and harms has come into focus in recent literature on responsibility gaps [2, 3]. We describe a proposed interdisciplinary approach to designing for answerability in autonomous systems, grounded in an instrumentalist framework of ‘responsible agency cultivation’ drawn from moral philosophy and cognitive sciences as well as empirical results from structured interviews and focus groups in the application domains of health, finance and government. We outline a prototype dialogue agent informed by these emerging results and designed to help bridge the structural gaps in organisations that typically impede the human agents responsible for an autonomous sociotechnical system from answering to vulnerable patients of responsibility.