Participatory Artificial Intelligence in Public Social Services - From Bias to Fairness in Assessing Beneficiaries

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This open access edited volume focuses on fairness issues concerning the use of artificial intelligence (AI) for social service provision in national welfare systems. With this, it touches upon important questions in the innovation agenda of countries across continents about the ethics, justice, quality, responsibility, accountability, and transparency to use AI for state functions. The volume shows that in many countries, AI, or at least data analytics methods, are already in place to support the assessment of beneficiaries for deciding on the value criteria to distinguish between legal /fraudulent, deserving/non-deserving, or needy/non-needy recipients. The book provides a cross-cultural comparison of AI-based social assessment among national welfare systems of 9 countries across 4 continents: Spain, Estonia, Germany, Iran, India, Nigeria, Ukraine, China and USA. Based on participatory research results from multi-stakeholder inputs, especially those from vulnerable groups, the chapters in this volume show that value criteria for fairness and social justice are context-bound and vary across the globe. Furthermore, they are in constant flux, aligned to social change. Thus, the volume looks at pathways to developing culture-sensitive, responsive and participatory AI for social assessment in public service provision. The contributions are interdisciplinary and introduce perspectives from the fields of sociology, computational social science, computer science and public policy. This topical volume is of interest to a wide readership.

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Participatory Artificial Intelligence in Public Social Services - From Bias to Fairness in Assessing Beneficiaries This open access edited volume focuses on fairness issues concerning the use of artificial intelligence (AI) for social service provision in national welfare systems. With this, it touches upon important questions in the innovation agenda of countries across continents about the ethics, justice, quality, responsibility, accountability, and transparency to use AI for state functions. The volume shows that in many countries, AI, or at least data analytics methods, are already in place to support the assessment of beneficiaries for deciding on the value criteria to distinguish between legal /fraudulent, deserving/non-deserving, or needy/non-needy recipients. The book provides a cross-cultural comparison of AI-based social assessment among national welfare systems of 9 countries across 4 continents: Spain, Estonia, Germany, Iran, India, Nigeria, Ukraine, China and USA. Based on participatory research results from multi-stakeholder inputs, especially those from vulnerable groups, the chapters in this volume show that value criteria for fairness and social justice are context-bound and vary across the globe. Furthermore, they are in constant flux, aligned to social change. Thus, the volume looks at pathways to developing culture-sensitive, responsive and participatory AI for social assessment in public service provision. The contributions are interdisciplinary and introduce perspectives from the fields of sociology, computational social science, computer science and public policy. This topical volume is of interest to a wide readership.