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.

翻译：公平性、问责制、透明度与伦理（FATE）领域的研究已确定了多种来源和形式的算法危害，涉及医疗保健、金融、警务和推荐等多个领域。要减轻这些系统的严重危害（尤其是对边缘化社区造成的不成比例影响），仍需大量工作。尽管这些危害持续存在，新的系统仍在开发和部署中，可能导致原有危害的延续并催生新型危害。为此，FATE社区强调预见危害的重要性。我们的研究聚焦于日益自主的系统可能带来的危害。我们并非将自主性定义为二元属性，而是识别出四个关键特征——这些特征尤其当组合出现时，往往会增强特定算法系统的自主性：不充分指定、影响直接性、目标导向性以及长期规划。我们还讨论了自主性增强所引发的重要危害——尤其包括对边缘化利益相关者造成的系统性及/或长期影响。我们强调，承认算法系统的自主性并不能减轻或转移人类对算法危害的责任。相反，我们使用"自主性"这一术语，是为了凸显一个日益明显的事实：机器学习系统并非完全受人类控制。我们的研究从三部分探讨日益自主的算法系统：首先，在跨学科对自主性的多元视角背景下，解释算法系统自主性增强的概念；其次，论证预见日益自主的系统带来的危害的必要性；最后，讨论日益自主的系统引发的重要危害及应对方法。我们通过反思这项工作对预见新兴系统算法危害的意义作为结论。