FairnessBiasAccountabilitycs.AI
Diversity and Inclusion in Artificial Intelligence
Didar Zowghi, Francesca da Rimini
May 22, 2023
arXiv.org
57 citations
3 influential
Key Takeaways
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Diversity and inclusion in AI requires a systematic, ecosystem-wide approach rather than isolated fixes to individual systems. The paper emphasizes that responsible AI depends on embedding these considerations throughout the entire AI development pipeline and broader industry structures.
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Practical guidelines exist for AI technologists and leaders to implement diversity and inclusion. The paper provides concrete, actionable advice specifically designed for those building AI systems and managing AI projects, moving beyond abstract principles to real implementation strategies.
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A clear definition of diversity and inclusion in AI is essential for progress. The paper establishes a shared vocabulary and conceptual framework, which is foundational since "little concrete practical advice" had existed previously on this topic.
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Responsibility for diversity and inclusion extends beyond data scientists to project leaders and organizational decision-makers. The guidelines target multiple roles in AI development, recognizing that technologists alone cannot solve these challenges without support from leadership and structural changes.
Abstract
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.