Several competing narratives drive the contemporary AI ethics discourse. At the two extremes are sociotechnical dogmatism, which holds that society is full of inefficiencies and imperfections that can only be solved by better technology; and sociotechnical skepticism, which highlights the unacceptable risks AI systems pose. While both narratives have their merits, they are ultimately reductive and limiting. As a constructive synthesis, we introduce and defend sociotechnical pragmatism—a narrative that emphasizes the central role of context and human agency in designing and (...) evaluating emerging technologies. In doing so, we offer two novel contributions. First, we demonstrate how ethical and epistemological considerations are intertwined in the AI ethics discourse by tracing the dialectical interplay between dogmatic and skeptical narratives across disciplines. Second, we show through examples how sociotechnical pragmatism does more to promote fair and transparent AI than dogmatic or skeptical alternatives. By spelling out the assumptions that underpin sociotechnical pragmatism, we articulate a robust stance for policymakers and scholars who seek to enable societies to reap the benefits of AI while managing the associated risks through feasible, effective, and proportionate governance. (shrink)

Scientists increasingly approach the world through machine learning techniques, but philosophers of science often question their epistemic status. Some philosophers have argued that the use of unsupervised clustering algorithms is more justified than the use of supervised classification, because supervised classification is more biased, and because (parametric) simplicity plays a different and more interesting role in unsupervised clustering. I call these arguments the No-Bias Argument and the Simplicity-Truth Argument. I show how both arguments are fallacious and how, on the contrary, (...) the use of supervised classification is at least as justified as the use of unsupervised clustering. (shrink)

In Philosophy and Technology 36, David Watson discusses the epistemological and metaphysical implications of unsupervised machine learning (ML) algorithms. Watson is sympathetic to the epistemological comparison of unsupervised clustering, abstraction and generative algorithms to human cognition and sceptical about ML’s mechanisms having ontological implications. His epistemological commitments are that we learn to identify “natural kinds through clustering algorithms”, “essential properties via abstraction algorithms”, and “unrealized possibilities via generative models” “or something very much like them.” The same issue contains a commentary (...) on Watson’s paper in which Tom Sterkenburg fiercely opposes the epistemological claim that clustering algorithms can identify natural kinds. Sterkenburg argues there’s nothing about clustering arguments themselves that enables natural kind identification. What’s at stake in the Watson-Sterkenburg debate is whether it is possible for clustering algorithms to identify natural kinds and whether they underly human identification of natural kinds. My contribution to the Watson-Sterkenburg debate is twofold. First, I argue Sterkenburg’s criticism of Watson’s claim is too severe because it denies any clustering algorithm to identify any natural kind. Secondly, I argue Watson’s reply overestimates both clustering algorithms’ and humans’ access to natural kinds and his claim has to be restricted accordingly to ‘We identify kinds via clustering algorithms, or something very much like them’. (shrink)