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.

Iñaki’s work investigates the role of dialogue design in shaping, discourse, normative content, and outcomes of public engagement with emerging technologies.

Yiping’s work focuses on the introduction of big data-driven technology — exploring the context, differences and process of the technology development, in addition to the performativity realized through stakeholders’ and partitioners’ engagement, discussion, cooperation and negotiation during the implementation process.

This project explores the responsible usage of AI, in particular, learning to identify and mitigate bias and algorithmic (un)fairness. It looks to prevent the potential reinforcement and amplification of harmful existing human biases with applications to credit access and the financial industry.

This project explores the ethical and political implications of digitalisation and datafication in higher education. In particular, this research investigates the changing experiences and subjectivities of students in contemporary UK universities amid the growing importance of digital technologies, data and platforms.

This project explores what the philosophy of science and in particular, the history and philosophy of measurement, can contribute to our understanding of the social and ethical implications of machine learning.

This research investigates models of collective data governance in agricultural ecosystems, evaluating them from a lens of power and inclusion, and their broader implications for responses to the climate crisis.