Advancing Responsible AI in Public Sector Application

Responsible use of AI in public sector applications requires engagement with various technical and non-technical areas such as human rights, inclusion, diversity, innovation and economic growth. The book covers topics spanning the technological socio-economic spectrum, including the potential of AI/ML technologies to address social and political inequities, privacy-enhancing technologies for datasets, friction-less data sharing and data stewardship models, regional/geographical inequities in extraction and so forth.
Features: - Focuses on technical aspects of responsible AI in the public sector - Covers a wide range of topics spanning the technological socio-economic spectrum - Presents viewpoints from public sector agencies as well as from practitioners - Discusses privacy-enhancing technologies for collecting, processing and storing datasets, and friction - Reviews frameworks to identify and address biased AI outcomes in the design, development and use of AI
This book is aimed at professionals, researchers and students in artificial intelligence, computer science and engineering, policy-makers, social scientists, economists and lawyers.

Abhishek Singh is a distinguished officer of the 1995 batch of the Indian Administrative Service (IAS), and currently serves as Additional Secretary in the Ministry of Electronics and Information Technology (MeitY), Government of India. With nearly three decades of experience in governance, policy formulation and administration, he specializes in leveraging technology to enhance governance and public service delivery. As Additional Secretary, he holds responsibilities including Artificial Intelligence, Emerging Technologies, Cybersecurity, and Digital Skilling with additional charge of CEO of IndiaAI Mission.
An alumnus of IIT Kanpur, where he earned a B.Tech in Mechanical Engineering, Singh furthered his education with a Master's in Public Administration from the Harvard Kennedy School as a Mason Fellow. Over the years, he has held several key positions, including CEO of Karmayogi Bharat, MyGov and the National e-Governance Division (NeGD). His leadership has been instrumental in implementing landmark initiatives, such as CoWIN, DigiLocker and DIKSHA. At the state level, he was with the Government of Nagaland and Uttar Pradesh and was responsible for implementing development schemes at the grassroots level, ensuring law and
order as also for collecting revenues for the States.
Balaraman Ravindran heads the Robert Bosch Centre for Data Science & Artificial Intelligence and the Centre for Responsible AI at IIT Madras, the leading interdisciplinary AI research centre in India. He is the Mindtree Faculty Fellow and Professor in the Department of Computer Science and Engineering at IIT Madras. He is the premier Deep Reinforcement Learning Expert and among the top three Machine Learning Experts in India. He has been elected as ACM Distinguished Member (2021) for his significant contributions to computing. He has been recognized, in 2020, as a senior member of the Association for Advancement of AI (AAAI) for his long-standing contributions to AI. He is also the co-director of the Prathap Subrahmanyam Centre for Digital Intelligence, Secure Hardware and Architecture (PSC-DISHA) and the Reconfigurable and Intelligent Systems Engineering (RISE) group at IIT Madras.
He has been closely collaborating with various industrial research labs, such as Google Research, Intel Research, Ericsson R&D, and many more, working on applications of AI techniques to solve difficult real-world problems. He received his PhD from the University of Massachusetts, Amherst and his Master's in research degree from the Indian Institute of Science, Bangalore. He has more than two decades of research experience in AI and ML, specifically, Reinforcement Learning. He has held visiting positions at the Indian Institute of Science, Bangalore, India; University of Technology, Sydney, Australia; and Google Research. Currently, his research interests are centred on learning from and through interactions and span the areas of geometric deep learning and reinforcement learning.