Abstract : Explainable AI (or XAI) has garnered a lot of interest in recent years across academia, industry and government. Although many methods have been proposed it is still unclear what XAI truly entails and why it is hard to formalize as opposed to other areas of machine learning such as causality, adversarial robustness, amongst others. In this talk, I will try to explicitly point out what XAI is trying to do, thus making it clear why formalization is difficult. The disentangled perspective also motivates new type of promising XAI approaches that are currently underexplored due to the largely intermingled view.
Bio : Amit Dhurandhar is Research Staff Member in the Trusted AI department at IBM T.J. Watson Research, where he leads the Explainable AI theme. His current research includes proposing various methods for enhancing trust in systems by developing methods that try to explain or understand their behaviors. His recent work was featured in Forbes and PC magazine with corresponding technical contribution in leading AI research venues such as NeurIPS, ICML and JMLR. His work has helped uncover interesting insights in fields such as Olfaction with papers in reputed journals such as Science and Nature Communications with extensive media coverage (Quartz, New Yorker, Atlantic, Biological Scene, Science News) . His research also has received the AAAI deployed application award as well as being selected as Best of IEEE ICDM. For his work on explainability he was invited to attend a Schloss Dagstuhl seminar in 2019. He was also one of the co-leads in the creation of the AI Explainability 360 open source toolkit. Besides research impact, his work has also gone into IBM products and his project work has received Outstanding Technical Achievement as well as a nomination for Corporate award. He has been an Area Chair and PC member for top AI conferences as well as has served on National Science Foundation (NSF) panels for the small business innovative research (SBIR) program.
Bio : Goda Ramkumar ranked as one of "Top 10 Data Scientists in India" by Analytics India Magazine in 2018, is currently Principal Data Scientist at Swiggy focusing on core logistics. Prior to this, she was GM, Data Science at xto10x establishing data science journey and practice in the start-up ecosystem. Previously she was at Ola for 2 years as Principal Data Scientist where she lead the machine learning and optimization algorithms behind pricing and matching. She worked at Sabre for 10 years before Ola on a variety of problems in Airline Pricing and Revenue Management. She has several publications and presentations in industry journals and academic conferences. She holds an undergraduate and masters degree from IIT Madras.
Bio : Harshad Khadilkar is a scientist with the research division of Tata consultancy services, where he leads the Planning & Control team in Mumbai. He is also a visiting associate professor with the aerospace engineering department at IIT Bombay. He holds a bachelors degree from the same department, in addition to masters and PhD degrees from the Massachusetts Institute of Technology. His research interests are in control and reinforcement learning algorithms for networked systems.
Bio : Srujana is a machine learning researcher (currently consulting with Google research & Wadhwani AI) with a keen interest in platform dynamics, data governance, human behavior, education, healthcare, and financial inclusion. Prior to 2017, she was employed with the machine learning groups at Yahoo Research, IBM Research, Amazon, and Flipkart. Post-2017, she has mostly been involved in consulting engagements with startups in education, health-care, CRM, SaaS, and Fintech domains along with volunteering on social causes. Since March of this year, she has been assisting with the COVID response of a few highly impacted cities and states as part of the COVID-19 Data Science Consortium. She has published her research in several top-tier conferences and journals on machine learning and is the recipient of multiple best paper awards. She received her M.S. and Ph.D. from the University of Texas at Austin and her B. Tech. degree from IIT Madras.
Bio : Mitesh M. Khapra is an Assistant Professor in the Department of Computer Science and Engineering at IIT Madras and is affiliated with the Robert Bosch Centre for Data Science and AI. He is also a co-founder One Fourth Labs, a startup whose mission is to design and deliver affordable hands-on courses on AI and related topics. He is also a co-founder of AI4Bharat, a voluntary community with an aim to provide AI-based solutions to India-specific problems. His research interests span the areas of Deep Learning, Multimodal Multilingual Processing, Natural Language Generation, Dialog systems, Question Answering and Indic Language Processing. He has publications in several top conferences and journals including TACL, ACL, NeurIPS, TALLIP, EMNLP, EACL, AAAI, etc. He has also served as Area Chair or Senior PC member in top conferences such as ICLR and AAAI. Prior to IIT Madras, he was a Researcher at IBM Research India for four and a half years, where he worked on several interesting problems in the areas of Statistical Machine Translation, Cross Language Learning, Multimodal Learning, Argument Mining and Deep Learning. Prior to IBM, he completed his PhD and M.Tech from IIT Bombay in Jan 2012 and July 2008 respectively. His PhD thesis dealt with the important problem of reusing resources for multilingual computation. During his PhD he was a recipient of the IBM PhD Fellowship (2011) and the Microsoft Rising Star Award (2011). He is also a recipient of the Google Faculty Research Award (2018), the IITM Young Faculty Recognition Award (2019) and the Prof. B. Yegnanarayana Award for Excellence in Research and Teaching (2020).