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、透明度和伦理（FATE）领域的研究已确定了算法危害的多种来源和形式，涵盖医疗、金融、警务和推荐等众多领域。尽管仍需大量工作来减轻这些系统造成的严重危害，尤其是那些对边缘化社区造成不成比例影响的危害，但新的系统仍在开发和部署中，这可能会延续原有的危害并催生新的危害。为此，FATE社区强调了预见危害的重要性。我们的工作聚焦于预见那些自主性日益增强的系统的危害。我们并非将自主性定义为一种二元属性，而是识别出四个关键特征，这些特征尤其是在组合出现时，往往会增强特定算法系统的自主性：规定不明确性、影响的直接性、目标导向性以及长期规划性。我们还讨论了因自主性增强而产生的重大危害——值得注意的是，这些危害通常包括对边缘化利益相关者造成的系统性影响和/或长期影响。我们强调，承认算法系统的自主性并不能免除或转移人类对算法危害的责任。相反，我们使用“自主性”这一术语是为了突显一个日益明显的事实：机器学习系统并未完全受人类控制。我们的工作分三部分探讨日益自主的算法系统。首先，我们结合不同学科对自主性的多元视角，阐释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的系统所带来的危害的必要性。第三，我们讨论了日益自主的系统所造成的重大危害以及应对这些危害的可行途径。最后，我们反思了我们的工作对预见新兴系统算法危害的意义。