The role of machine learning generated health categories in epistemically (un)just healthcare practices

Sasha’s research aims to explore how newly developed AI and machine learning (ML) tools may impact the epistemic climate that both healthcare users and professionals participate in. Her project aims to identify ways to create and implement measures to combat injustices without rejecting the benefits of ML in healthcare provision. 

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EdTech Engineers And the Making of Machine Learning in Education

Meenakshi’s work aims to explore explore how Indian EdTech engineers incorporate ideas about teaching and learning into the development of AI education technologies for K-12 classrooms. She aims to understand how ML infrastructures interact with and influence engineering practices in AI EdTech development.

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Towards an Adversarial Artistic Inquiry of Generative Computer Vision

Martin’s research project aims to develop and analyse a particular visual practice of artistic inquiry characterised by adversarial interventions with generative AI applications. This project offers a new perspective on the aesthetic, epistemic, evidential and translational value of art and design work that interrogates the ethical and cultural implications of generative AI.

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A Responsibility Framework for Governing Trustworthy Autonomous Systems

This research develops a comprehensive responsibility framework to enable stakeholders involved in the design, development, and deployment of decision-making algorithms and autonomous systems to effectively govern and take responsibility for the outcomes of those systems in fields such as health, robotics, and finance

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