School of Social and Political Science

Description

Over the past few years, we have witnessed a rise in the development of AI-based tools providing various forms of personalized healthcare and, particularly, applications that make use of machine learning techniques to track and predict mental health and affective behaviors. These solutions are implemented both as clinical aids meant to support clinician decision-making and as commercial tools promoting cultures of “self-care” and “wellness”. These systems use a range of techniques, from the analysis of input data, such as clicking on a range of emojis representing affective or mental states, to the analysis of data captured through smartphone sensors (facial expressions, keyboard interactions, voice and speech recognition), and aim at mapping the user’s affective state and providing some form of behavioral recommendation. Based on interviews with industry and academic practitioners, I investigate the contexts informing the design and development of face-based applications meant to measure and assess a range of mental health and neurodevelopmental conditions. What assumptions, expectations, and notions of illness and care, are encoded in these systems? What forms of knowledge do practitioners draw on and how do organizational structures and boundaries contribute to the process of ‘making-sense’ of affective data? By bringing these social dimensions of technology development to the fore, this paper aims to promote critical reflection around wider machine learning practices.

Speaker Bios:

Benedetta Catanzariti is a PhD candidate in Science, Technology and Innovation Studies at the University of Edinburgh. She is interested in the social and political dimensions shaping the design and development of AI systems, including the data practices (data collection, cleaning, and annotation) that underlie machine learning models. Her research sits at the intersection of STS and feminist studies and explores the material practices, histories, and epistemologies of computer vision technology.

Asli Ates is a doctoral researcher at the Science Policy Research Unit (SPRU), University of Sussex and working for the ERC funded EMPOCI project. Her PhD project, looks at the role of data (and policies) in an increasingly connected electricity and mobility systems for accelerating sustainability transitions in the UK.