日益自主的算法系统带来的危害
Harms from Increasingly Agentic Algorithmic Systems
AI总结：
本文针对日益自主的算法系统，提出通过规格不足、影响直接性、目标导向性和长期规划四个特征来预见其危害，并强调系统性及长期影响，同时指出自主性不减轻人类责任。
摘要：
公平性、问责制、透明度和伦理（FATE）领域的研究已经确定了算法危害的多种来源和形式，涉及医疗保健、金融、警务和推荐系统等多个领域。要减轻这些系统的严重危害，尤其是那些不成比例地影响边缘化社区的危害，仍有大量工作要做。尽管这些危害持续存在，新的系统仍在被开发和部署，这可能会使同样的危害持续存在，并产生新的危害。为此，FATE社区强调了预见危害的重要性。我们的工作聚焦于对日益自主的系统的危害预见。我们并未将自主性定义为二元属性，而是识别出4个关键特征，这些特征尤其组合在一起时，往往会增加给定算法系统的自主性：规格不足、影响的直接性、目标导向性和长期规划。我们还讨论了因自主性增强而产生的重要危害——特别是，这些危害包括系统性和/或长期影响，往往对边缘化利益相关者造成影响。我们强调，承认算法系统的自主性并不会免除或转移人类对算法危害的责任。相反，我们使用“自主性”一词是为了强调一个日益明显的事实：机器学习系统并非完全处于人类控制之下。我们的工作分三部分探讨日益自主的算法系统。首先，我们在跨学科对自主性的多样视角背景下，解释了算法系统自主性增强的概念。其次，我们论证了预见日益自主系统危害的必要性。第三，我们讨论了日益自主系统带来的重要危害以及解决这些危害的前进方向。最后，我们反思了我们的工作对预见新兴系统算法危害的启示。
英文摘要：
Research in Fairness, Accountability, Transparency, and Ethics (FATE) has established many sources and forms of algorithmic harm, in domains as diverse as health care, finance, policing, and recommendations. Much work remains to be done to mitigate the serious harms of these systems, particularly those disproportionately affecting marginalized communities. Despite these ongoing harms, new systems are being developed and deployed which threaten the perpetuation of the same harms and the creation of novel ones. In response, the FATE community has emphasized the importance of anticipating harms. Our work focuses on the anticipation of harms from increasingly agentic systems. Rather than providing a definition of agency as a binary property, we identify 4 key characteristics which, particularly in combination, tend to increase the agency of a given algorithmic system: underspecification, directness of impact, goal-directedness, and long-term planning. We also discuss important harms which arise from increasing agency -- notably, these include systemic and/or long-range impacts, often on marginalized stakeholders. We emphasize that recognizing agency of algorithmic systems does not absolve or shift the human responsibility for algorithmic harms. Rather, we use the term agency to highlight the increasingly evident fact that ML systems are not fully under human control. Our work explores increasingly agentic algorithmic systems in three parts. First, we explain the notion of an increase in agency for algorithmic systems in the context of diverse perspectives on agency across disciplines. Second, we argue for the need to anticipate harms from increasingly agentic systems. Third, we discuss important harms from increasingly agentic systems and ways forward for addressing them. We conclude by reflecting on implications of our work for anticipating algorithmic harms from emerging systems.
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