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Exploring the future of Artificial Intelligence (AI) in public social services, this chapter outlines a vision for “Better AI”—systems that go beyond technological performance to embody ethical integrity, transparency, equity, and human-centred design. Drawing on the AI FORA initiative’s participatory modelling and simulation practices, this work emphasizes deep stakeholder involvement throughout the AI lifecycle. We argue that integrating technical insights with collaborative methodologies is essential for aligning AI development with the public good. This chapter contributes the following: (1) a conceptual framework for “Better AI” grounded in participatory principles, (2) technical strategies including Inverse Generative Social Science (IGSS) and simulation-based foresight, and (3) a discussion of challenges and future research directions for ethically robust AI in social services. These contributions are positioned to inform both policy and technical communities striving for responsible AI innovation.

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Better AI for Public Good: Participatory Modelling and Simulation in Social Services

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Mahesh Sasikumar,
  • Ashly Ann Jo,
  • Ebin Deni Raj

摘要

Exploring the future of Artificial Intelligence (AI) in public social services, this chapter outlines a vision for “Better AI”—systems that go beyond technological performance to embody ethical integrity, transparency, equity, and human-centred design. Drawing on the AI FORA initiative’s participatory modelling and simulation practices, this work emphasizes deep stakeholder involvement throughout the AI lifecycle. We argue that integrating technical insights with collaborative methodologies is essential for aligning AI development with the public good. This chapter contributes the following: (1) a conceptual framework for “Better AI” grounded in participatory principles, (2) technical strategies including Inverse Generative Social Science (IGSS) and simulation-based foresight, and (3) a discussion of challenges and future research directions for ethically robust AI in social services. These contributions are positioned to inform both policy and technical communities striving for responsible AI innovation.