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.

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.

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.