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.

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 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

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 project aims to synthesise philosophical bioethics and public deliberative processes, to arrive at recommendations for the ethical use of AI in healthcare resource allocation.