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Diversity and Inclusion in Artificial Intelligence

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arxiv 2305.12728 v1 pith:5JRJMZD2 submitted 2023-05-22 cs.AI cs.SE

Didar Zowghi , Francesca da Rimini This is my paper · ORCID
classification cs.AIcs.SE
keywords diversityinclusionartificialdefinitionecosystemintelligencepracticaladvice
verification ladder T0 review T1 audit T2 compute T3 formal
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To date, there has been little concrete practical advice about how to ensure that diversity and inclusion considerations should be embedded within both specific Artificial Intelligence (AI) systems and the larger global AI ecosystem. In this chapter, we present a clear definition of diversity and inclusion in AI, one which positions this concept within an evolving and holistic ecosystem. We use this definition and conceptual framing to present a set of practical guidelines primarily aimed at AI technologists, data scientists and project leaders.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence

    cs.AI 2025-06 reject novelty 5.0 of 10

    A 253-question bank for assessing AI inclusivity, organized into five pillars, built from guidelines, literature, and LLM assistance, but validated only through AI-generated personas.

  2. LLMs as mirrors of societal moral standards: reflection of cultural divergence and agreement across ethical topics

    cs.AI 2024-12 conditional novelty 4.0 of 10

    Across five LLMs and two global surveys, model-generated moral judgments poorly matched cross-cultural patterns of agreement and disagreement.

  3. The Global AI Vibrancy Tool

    cs.CY 2024-11 conditional novelty 4.0 of 10

    The Global AI Vibrancy Tool ranks 36 countries on AI activity from 2017 to 2023, with the US leading, and adds Innovation, Economic Competitiveness, and Policy Governance sub-indices.

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