- AI ethics
- AI fairness
- Responsible AI
My research interests are primarily located within AI ethics, with a focus on fairness in AI. I am interested in understanding the social implications of unfair AI algorithms, and understanding the nuances of how the unfairness is encoded explicitly or implicitly within such algorithms. These range from domains such as news recommenders, crime surveillance, hiring fairness and unfair AI within the gig-economy. My research focuses on how to build fair and responsible AI algorithms to mitigate the risks and dangers of unfair AI, especially in domains where AI is used to make decisions affecting humans and societies substantively. In my research, I strive to identify and use best-of-breed and fit-for-purpose philosophical principles to be embedded in meaningful ways within AI algorithms.
Key Recent Publications:
- FairLOF: Fairness in Outlier Detection, Data Sc. and Engg. J. 2021,
- On Fairness and Interpretability, Workshop on AI for Social Good (AI4SG), 2021,
- Ethical Considerations in Data-driven Fake News Detection, in Data Science for Fake News, Springer 2021,
- Whither Fair Clustering?, AI for Social Good Workshop (AI4SG), 2020
- Representativity Fairness in Clustering}, ACM WebSci 2020,
- Fairness in Clustering with Multiple Sensitive Attributes, EDBT 2020.
Full publication list: http://dpadmanabhan.public.cs.qub.ac.uk/publications.html .