Abstract: Data science has long considered learning better models and making better decisions based on existing data. With data science systems penetrating into every aspect of daily life, whether it be navigation systems that influence which route you pick for your commute or personalized news delivery systems that decide what kind of news you would like to read, it is increasingly critical to consider questions on fairness. For example, would you be more prone to pre-emptive checks at the hands of an AI system if you were of a different gender, race or are a native of a different region?. There are various kinds of biases that data driven systems can internalize based on the data they train over, the assumptions they use, and the task that they try to optimize for. Two prominent doctrines are those of disparate treatment and disparate impact, and have been subject to much study in the context of classification systems. The scenario of unsupervised learning poses a deeper challenge, where the detection of biases is often trickier given the absence of labels in the data. This talk will introduce doctrines of fairness from political philosophy, and cover streams of research on incorporating notions of fairness within retrieval and clustering tasks, including a recent work by the speaker on fairness in clustering. This will also briefly outline some fairness research directions which may be of interest for future data science research.
Bio: Dr. Deepak Padmanabhan holds a faculty position in Computer Science at Queen’s University Belfast, United Kingdom. He received his B.Tech from CUSAT and his M.Tech and PhD from Indian Institute of Technology Madras, all in Computer Science. His current research interests include data analytics, natural language processing, machine learning, similarity search and information retrieval. Deepak has published over 70 research papers across major venues in information and knowledge management. Prior to taking up the current role, he was a researcher at IBM Research – India for ten years. His work has led to twelve patents from the USPTO. A Senior Member of the IEEE and the ACM and a Fellow of the UK Higher Education Authority, he is the recipient of the INAE Young Engineer Award 2015, an award recognizing scientific work by researchers across engineering disciplines in India. He has also authored multiple books on various areas of data science. He may be reached at firstname.lastname@example.org (preferred) or email@example.com .