Jackie Kay: Fairness for Unobserved Characteristics: Insights from Technological Impacts on Que​​​​​​​er Communities


Recent advances in algorithmic fairness have largely omitted sexual orientation and gender identity. We explore the concerns of the queer community in privacy, censorship, language, online safety, health, and employment to study the positive and negative effects of artificial intelligence on queer communities. These issues highlight a multiplicity of considerations for fairness research, such as privacy preservation, context sensitivity and process fairness, to an awareness of sociotechnical impact and the increasingly important role of inclusive and participatory research processes. Sexual orientation and gender identity are often instances of unobserved characteristics, breaking the assumptions of many approaches to algorithmic fairness. This talk will summarize our recent paper on the importance of developing new approaches for algorithmic fairness that break away from the prevailing assumption of observed characteristics.

February 25, 2021 18:00 — 19:00
LGBTQ-History Month Talk (AI Centre)


Jackie (they/them) is a research engineer at DeepMind and a PhD student in the Statistical Machine Learning Group co-advised by Marc Deisenroth and Raia Hadsell. Their research interests include machine learning applied to the robotic manipulation setting and combining transferable learned representations with data-efficient control techniques.
Marc Deisenroth
DeepMind Chair of Machine Learning and Artificial Intelligence