In this paper, we focus on the growing evidence of unintended harmful societal effects of automated algorithmic decision-making (AADM) in transformative services (e.g., social welfare, healthcare, education, policing and criminal justice), for individuals, communities and society at large. Drawing from the long-established research on social pollution, in particular its contemporary ‘pollution-as-harm’ notion, we put forward a claim, and provide evidence, that these harmful effects constitute a new type of digital social pollution, which we name ‘algorithmic pollution’. Words do matter, and by using the term ‘pollution’, not as a metaphor, but as a transformative redefinition of the digital harm performed by AADM, we seek to make it visible and recognized. By adopting a critical performative perspective, we explain how the execution of AADM produces harm and thus performs algorithmic pollution. Recognition of the potential for unintended harmful effects of algorithmic pollution, and their examination as such, leads us to articulate the need for transformative actions to prevent, detect, redress, mitigate, and educate about algorithmic harm. These actions, in turn, open up new research challenges for the information systems community.

在本文中，我们将重点关注越来越多的证据，证明自动化算法决策（AADM）在变革性服务（例如社会福利，医疗保健，教育，警务和刑事司法）中对个人，社区和社会的意外有害社会影响大。我们从对社会污染，特别是其当代的“污染即危害”概念的悠久研究出发，提出了一种主张，并提供了证据，表明这些有害影响构成了一种新型的数字社会污染，我们将其命名为“算法污染”。言语确实很重要，通过使用“污染”一词，不是将其作为隐喻，而是作为对AADM所造成的数字伤害的变革性重新定义，我们力求使其可见并得到认可。通过采用批判性的表演视角，我们解释了AADM的执行如何产生危害并因此造成算法污染。认识到算法污染可能带来的意料之外的有害影响，并对其进行了检查，这使我们明确指出需要采取预防措施来预防，检测，纠正，减轻和教育算法危害。这些行动反过来给信息系统界带来了新的研究挑战。