PHILOS Institut für Philosophie Institut News und Veranstaltungen News
Philosophy of Science Student Wins International Office Prize and Presents First Paper at AIES 2025

Philosophy of Science Student Wins International Office Prize and Presents First Paper at AIES 2025

Rahul Nagshi, Iqra Aslam, and Donal Khosrowi standing in front of their poster at AIES conference Rahul Nagshi, Iqra Aslam, and Donal Khosrowi standing in front of their poster at AIES conference Rahul Nagshi, Iqra Aslam, and Donal Khosrowi standing in front of their poster at AIES conference
Iqra Aslam (middle), winner of the Hochschulpreis, together with her co-authors Rahul Nagshi and Donal Khosrowi in front of their poster at the AIES 2025 conference.
Iqra Aslam pointing at her poster while talking to conference participant Iqra Aslam pointing at her poster while talking to conference participant Iqra Aslam pointing at her poster while talking to conference participant
© Donal Khosrowi
Iqra Aslam discussed her poster with interested participants at the AIES conference.

M.A. student Iqra Aslam has been awarded the International Office Prize of Leibniz University Hannover (LUH).

Iqra Aslam, who is enrolled in the Philosophy of Science programme at the Institute of Philosophy, won the Hochschulpreis of Leibniz University's International Office in recognition of her outstanding academic achievement and social commitment. She was nominated by Dr. Donal Khosrowi, Dr. Mathias Frisch, and Dr. Philippe van Basshuysen. 

This honour comes at a moment of academic distinction for Iqra: her first full-length research paper, “Learning to Unlearn, Failing to Forget? Assessing Machine Unlearning Through Ethics and Epistemology”, co-authored with Dr. Donal Khosrowi of the Centre for Ethics and Law in the Life Sciences (CELLS) and Rahul Nagshi (independent researcher), was accepted for presentation at the prestigious AAAI/ACM Conference on AI, Ethics, and Society (AIES 2025). Iqra Aslam, together with Dr. Khosrowi and Rahul Nagshi, presented the work as a poster at the AIES conference in Madrid in October.

The paper offers a critical examination of Machine Unlearning (MU), an emerging area of AI research concerned with removing unwanted information from trained models. MU is presented as a solution to a range of pressing issues, including the presence of sensitive personal data in training datasets, unauthorised reproduction of protected material, and the persistence of biased outputs. The authors argue, however, that important gaps remain between what MU can currently achieve and what various stakeholders expect from it. Drawing on social epistemology and the ethics of forgetting, the paper distinguishes three modes of forgetting that help clarify these gaps. This framework, they argue, can help stakeholders better articulate their requirements, guide more robust policy around privacy and bias, and integrate the ethics of gaining, retaining, and deleting knowledge into responsible AI development from the outset.