Diversity Reading List

Comment: This paper asserts a right to explanation grounded in an interest in informed self-advocacy, the term the author uses to describe a cluster of abilities to represent one's interests and values to decision-makers and to further those interests and values within an institution. Vredenburgh also argues that such form of self-advocacy are necessary for hierarchical, non-voluntary institutions to be legitimate and fair - and it is on these grounds that a person may reasonably reject insitutional set-ups that prevent them from engaging in these abilities. In this sense, Vredenburgh's argument applies to a broader set of problems then simply algorithmic opacity - they may feasibly be applied to cases in which systems (such as bureacratic ones) deny an individual this right to explanation. Therefore, this paper presents an argument which would be useful as further or specialised reading in a variety of classroom contexts, including courses or reading groups addressing technological and algorithmic ethics, basic political rights, bureacratic ethics, as well as more general social and political philosophical courses. It might be interesting, for example, to use it to in an introductory social/political course to discuss with students some of the ethical questions that are particular to a 21st century context. As systems become more complex and individuals become further removed from the institutional decision-making that guides/rules/directs their lives, what right do we have to understand the processes that condition our experience? In what other situations might these rights become challenged?

Comment (from this Blueprint): This is a written transcript of the James Baldwin lecture, delivered by the computer scientist Arvind Narayanan, at Princeton in 2022. Narayanan's prior research has examined algorithmic bias and standards of fairness with respect to algorithmic decision making. Here, he engages critically with his own discipline, suggesting that there are serious limits to the sorts of quantitative methods that computer scientists recruit to investigate the potential biases in their own tools. Narayanan acknowledges that in voicing this critique, he is echoing claims by feminist researchers from fields beyond computer science. However, his own arguments, centered as they are on the details of the quantitative methods he is at home with, home in on exactly why these prior criticisms hold up in a way that seeks to speak more persuasively to Narayanan's own peers in computer science and other quantitative fields.