Os Keyes

This commentary responds to the article “What Is the Aim of Pediatric ‘Gender‐Affirming’ Care?,” by Moti Gorin, in the same issue of the journal. Gender‐affirming care is often treated as exceptional and subject to heightened scrutiny. This exceptionalization results in its being held to stricter evidentiary standards than other forms of medical interventions are. But values and value judgments are inextricable from the practice of evidence‐based medicine. For gender‐affirming care, values shape what counts as “strong” evidence, whether the legitimacy of transgender identity is assumed versus treated as something to be investigated, how to characterize the testimonial accounts of trans and gender‐nonconforming patients, and more. We argue that these kinds of questions are part of the practice of medicine, not exceptional to transgender people and gender‐affirming care. However, litigation of evidence for gender‐affirming care in state and national policy underscores the moral urgency of thinking carefully about what values ought to guide evidence.

Efforts to address algorithmic harms have gathered particular steam over the last few years. One area of proposed opportunity is the notion of an “algorithmic audit,” specifically an “internal audit,” a process in which a system’s developers evaluate its construction and likely consequences. These processes are broadly endorsed in theory—but how do they work in practice? In this paper, we conduct not only an audit but an autoethnography of our experiences doing so. Exploring the history and legacy of a facial recognition dataset, we find paradigmatic examples of algorithmic injustices. But we also find that the process of discovery is interwoven with questions of affect and infrastructural brittleness that internal audit processes fail to articulate. For auditing to not only address existing harms but avoid producing new ones in turn, we argue that these processes must attend to the “mess” of engaging with algorithmic systems in practice. Doing so not only reduces the risks of audit processes but—through a more nuanced consideration of the emotive parts of that mess—may enhance the benefits of a form of governance premised entirely on altering future practices.

While feminist critiques of AI are increasingly common in the scholarly literature, they are by no means new. Alison Adam’s Artificial Knowing (1998) brought a feminist social and epistemological stance to the analysis of AI, critiquing the symbolic AI systems of her day and proposing constructive alternatives. In this paper, we seek to revisit and renew Adam’s arguments and methodology, exploring their resonances with current feminist concerns and their relevance to contemporary machine learning. Like Adam, we ask how new AI methods could be adapted for feminist purposes and what role new technologies might play in addressing concerns raised by feminist epistemologists and theorists about algorithmic systems. In particular, we highlight distributed and federated learning as providing partial solutions to the power-oriented concerns that have stymied efforts to make machine learning systems more representative and pluralist.