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We kindly invite you to join the ‘Meet the Jury’ seminar, featuring international speakers to discuss their research on 'Beyond models and data: a sociotechnical approach to explain machine learning decision making'.
This seminar will be given by:
Beyond models and data: a sociotechnical approach to explain machine learning decision making. The widespread use of machine learning (ML) models for decision-making raises critical concerns about transparency and accountability – to which an increasingly popular solution is ‘Explainable AI’ (XAI). Here, the object of explanation is technically complex models which are difficult or even impossible to explain. In contrast, our research makes a call to de-centre models as the object of explanation, and look towards the network of practice that bring models into use. We explore this approach through an ethnographic study, conducted in collaboration with a financial services company in the UK. Our empirical analysis shows an ‘ecology’ of multiple, situated explanations for machine learning across a range of actors in the company. From this perspective, organizational complexity is what makes explanation such a challenge. We argue that while XAI cannot deliver on its solutionist promise, it raises important questions about how decisions are made in contexts where ML is used in decision-making. In this talk, we explore how our sociotechnical approach unsettles binary divisions such as experts/non-experts and opaque/transparent models, emphasizing the range of knowledge practices needed to explain machine learning across the organization.
Overall, our research suggests a need to widen and deepen the search for explanation, and explore the opportunities for provisional, relational and collective interrogations over what can (and can’t) be explained about machine learning systems.
This is a Meet the Jury seminar. Additional information on how to apply for these seminars can be found here.
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