There has been a surge of recent interest in systematically incorporating considerations of participation and diversity in the design and evaluation of AI/ML systems. Currently, however, there is a gap between discussions of measures and benefits of diversity in AI research, on the one hand, and the broader research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, different concepts of diversity are based on distinct rationales that should inform how we measure diversity in a given context. Similarly, the lack of specificity about the precise mechanisms underpinning diversity's potential benefits can result in uninformative generalities, invalid experimental designs, and illicit interpretations of findings. To address these challenges, in this talk, I draw on research in philosophy, psychology, and social and organisational sciences to make three contributions: First, I introduce a taxonomy of different diversity concepts from the philosophy of science, and explicate the distinct epistemic and political rationales underlying these concepts. Second, I provide an overview of mechanisms by which diversity can benefit group performance. Third, I situate these taxonomies—of concepts and mechanisms—in the lifecycle of supervised ML (SML) systems and make a case for their usefulness in responsible AI/ML. I do so by illustrating how they clarify the discourse around diversity in the context of ML systems, promote the formulation of more precise research questions about diversity's impact, and provide conceptual tools to further advance research and practice. I end this talk by discussing some of our ongoing projects in this space that focus on human-AI collaboration and the evaluation of generative AI systems.