A feminist data ethics of care framework for machine learning: The what, why, who and how

Abstract

This article conceptualises and provides an initial roadmap for operationalising a feminist data ethics of care framework for the subfield of artificial intelligence (‘AI’) known as ‘machine learning’. After outlining the principles and praxis that comprise our framework, and then using it to evaluate the current state of mainstream AI ethics content, we argue that this literature tends to be overly abstract and founded on a heteropatriarchal world view. We contend that because most AI ethics content fails to equitably and explicitly assign responsibility to actors in the machine learning economy, there is a risk of implicitly reinforcing the status quo of gender power relations and other substantive inequalities, which contribute to the significant gap between AI ethics principles and applied AI ethics more broadly. We argue that our feminist data ethics of care framework can help to fill this gap by paying particular attention to both the ‘who’ and the ‘how’, as well as by outlining a range of methods, approaches, and best practices that societal actors can use now to make interventions into the machine learning economy. Critically, feminist data ethics of care is unlikely to be achieved in this context unless all stakeholders, including women, men, and non-binary and transgender people, take responsibility for this much needed work.