Under the Guise of Machine Neutrality : Machine Learning Uncertainty Exploration as Design Material to Identify Gender Bias in AI Systems

Sammanfattning: Structural gendered inequality permeates intelligent systems, shaping everyday lives and reinforcing gender oppression. This study investigates how uncertainty, as an inherent characteristic of Machine Learning (ML) models, can be translated as a design material to highlight gender bias in Artificial Intelligence (AI) systems. It follows an HCI feminist methodology with a threefold horizon: the re-conceptualisation of the design space that considers human and non-human perspectives (Giaccardi & Redström, 2020); the exploration of ML uncertainty as design materiality (Benjamin et al., 2020) to underscore imbued gender inequality in intelligent systems; and the disputed relations of ML uncertainty as materiality with unpredictability in Explainable AI systems, more specifically Graspable AI (Ghajargar et al., 2021, 2022). As a critical exploratory process, the knowledge contribution is the development of a set of guidelines for the design of better and more equal ML systems.