Overview

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.

Full Product Details

Author:   Abhishek Singh (MeitY, Government of India, Electronics Niketan, New Delhi) ,  Balaraman Ravindran (IIT Madras, Chennai, India)
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   0.590kg
ISBN:  

9781032703930

ISBN 10:   1032703938
Pages:   218
Publication Date:   18 September 2025
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Not yet available  
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Table of Contents

1. Raising the Standard in AI Procurement: Global Opportunity and Challenges 2. Data Empowerment and Protection Architecture (DEPA) for Training ML Models 3. Generative AI Governance: Technological Monoculture, Market Structure and the Risk of Correlated Failures 4. Empowering citizens through responsible AI governance : policy recommendations for public algorithm registers 5. Responsible Adoption of Cloud-Based Artificial Intelligence in Healthcare: A Validation Case Study of Multiple Artificial Intelligence Algorithms for Diabetic Retinopathy Screening in Public Health Settings 6. Developing Community Led AI: Notes from The Trenches 7. Risk Assessment methodology for AI regulation and navigating liability determination in AI driven world 8. Harnessing the potential of AI for Indian agriculture: Leveraging “Bhashini” as a tool for deploying responsible AI and increasing uptake of AI applications among farmers 9. Regional inequities in extraction and flow of resources that support and power the design, development and access to AI: Experiences from India and Kenya 10. Assessing the Trustworthiness of Generative AI Used for Higher Education 11. Actionable ethics: from philosophical principles to operational initiatives for responsible AI projects in public sector in the French context 12. Supporting AI at Scale in the APEC Region Through International Standards 13. A Policy Framework for Third Party Auditing of AI Systems 14. AI in the Healthcare Sector: Insights from Rwanda’s Mbaze Chatbot Project

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Author Information

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.

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