Diversity and Inclusion in Artificial Intelligence
It addresses the problem of ensuring diversity and inclusion in AI for practitioners, but it is incremental as it builds on existing discussions without introducing new methods or data.
The paper tackles the lack of concrete practical advice for embedding diversity and inclusion in AI systems and the global AI ecosystem by presenting a clear definition and conceptual framing, resulting in a set of practical guidelines for AI technologists, data scientists, and project leaders.
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.