The project aims to understand how people conceive of privacy in relation to the State in the context of information sharing in reproductive and maternal welfare schemes in India.

This project examines how language attitudes and ideologies inform design decisions in speech technology, reviewing claims from speech science and psychology with an awareness of communicative goals and (speaker and listener) identity as expansive, fluid and intersectional.

This project highlights the importance of understanding AI within the context of established practices, recognizing the cultivated wisdom and accumulated experience they represent.

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 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.