Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI - Bird, Sarah; Barocas, Solon; Crawford, Kate; Wallach, Hanna (Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT-ML), New York University, 2016)
In the field of computer science, large-scale experimentation on users is not new. However, driven by advances in artificial intelligence, novel autonomous systems for experimentation are emerging that raise complex, unanswered questions for the field. Some of these questions are computational, while others relate to the social and ethical implications of these systems. We see these normative questions as urgent because they pertain to critical infrastructure upon which large populations depend, such as transportation and healthcare. Although experimentation on widely used online platforms like Facebook has stoked controversy in recent years, the unique risks posed by autonomous experimentation have not received sufficient attention, even though such techniques are being trialled on a massive scale. In this paper, we identify several questions about the social and ethical implications of autonomous experimentation systems. These questions concern the design of such systems, their effects on users, and their resistance to some common mitigations.