Beyond Fairness, Accountability, and Transparency in the Ethics of Algorithms: Contributions and Perspectives from LIS

摘要

Recent debates have revealed the urgent need for work addressing social and ethical implications of the algorithmic and data-driven systems that govern our lives. Focusing largely on machine learning (ML) and artificial intelligence (Al), conversations among scholars, journalists, and advocates have started to address questions of fairness, bias, transparency, access, participation, and discrimination often under the monikers "Al and Ethics" (Al+Ethics) or "fairness, accountability, and transparency in algorithms" (FAT*). Underlying these discourses are concerns about how to mitigate discrimination and bias in data, as well as open questions over whether algorithmic systems can be fair and if their use will help promote equitable futures (or instead further perpetuate existing inequalities). Despite the seeming "newness" of these issues, many of the underlying concerns have a longstanding tradition and intellectual lineage in the library and information science (LIS) field. In this panel, we will discuss and elaborate on these connections, drawing on our research and experiences in a number of contexts and outlining opportunities these relationships present for future research and engagement.