Diversity in Sociotechnical Machine Learning Systems
- URL: http://arxiv.org/abs/2107.09163v1
- Date: Mon, 19 Jul 2021 21:26:38 GMT
- Title: Diversity in Sociotechnical Machine Learning Systems
- Authors: Sina Fazelpour, Maria De-Arteaga
- Abstract summary: There has been a surge of recent interest in sociocultural diversity in machine learning (ML) research.
We present a taxonomy of different diversity concepts from philosophy of science, and explicate the distinct rationales underlying these concepts.
We provide an overview of mechanisms by which diversity can benefit group performance.
- Score: 2.9973947110286163
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: There has been a surge of recent interest in sociocultural diversity in
machine learning (ML) research, with researchers (i) examining the benefits of
diversity as an organizational solution for alleviating problems with
algorithmic bias, and (ii) proposing measures and methods for implementing
diversity as a design desideratum in the construction of predictive algorithms.
Currently, however, there is a gap between discussions of measures and benefits
of diversity in ML, 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. In this
work, we draw on research in philosophy, psychology, and social and
organizational sciences to make three contributions: First, we introduce a
taxonomy of different diversity concepts from philosophy of science, and
explicate the distinct epistemic and political rationales underlying these
concepts. Second, we provide an overview of mechanisms by which diversity can
benefit group performance. Third, we situate these taxonomies--of concepts and
mechanisms--in the lifecycle of sociotechnical ML systems and make a case for
their usefulness in fair and accountable ML. We 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.
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