This article examines the business case for diversity, according to which diversity should be promoted because diverse groups outperform nondiverse groups. Philosophers who defend BCD usually...

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model that captures the role of ‘ability’ in a meaningful way, and we use it to explore the trade-offs between diversity and (...) ability in scientific problem solving. (shrink)

In this introduction to the Topical Collection on Social and Cognitive Diversity in Science, we map the questions that have guided social epistemological approaches to diversity in science. Both social and cognitive diversity of different types is claimed to be epistemically beneficial. The challenge is to understand how an increase in a group’s diversity can bring about epistemic benefits and whether there are limits beyond which diversity can no longer improve a group’s epistemic performance. The contributions to the Topical Collection (...) discuss various proposals to maintain an appropriate amount of cognitive diversity in science, for instance, by recruiting and retaining practitioners from underrepresented social groups, providing incentives for explorative and risky research, encouraging interdisciplinary collaborations and stakeholder participation in research, requiring industry scientists to share their evidence, and developing strategies to encounter politically motivated attempts to manufacture doubt. To be successful, efforts to promote diversity in science should anticipate risks related to institutional interventions, navigate trade-offs between different types of epistemically good outcomes, and identify hidden costs that such policies may cause for various actors. Such efforts need to be assessed not only from an epistemic perspective but also from the point of view of fairness and the political legitimacy of scientific institutions. (shrink)

Demographic diversity might often be present in a group without group members noticing it. What are the epistemic effects if they do? Several philosophers and social scientists have recently argued that when individuals detect demographic diversity in their group, this can result in epistemic benefits even if that diversity doesn’t involve cognitive differences. Here I critically discuss research advocating this proposal, introduce a distinction between two types of detection of demographic diversity, and apply this distinction to the theorizing on diversity (...) in science. Focusing on ‘invisible’ diversity, I argue that in one common kind of group in science, if group members have full insight into their group’s diversity, this is likely to create epistemic costs. These costs can be avoided and epistemic benefits gained if group members only partly detect their group’s diversity. There is thus an epistemic reason for context-dependent limitations on scientists’ insight into the diversity of their group. (shrink)

The social epistemology of science would benefit from paying more attention to the nature of argumentative exchanges. Argumentation is not only a cognitive activity but a collaborative social activity whose functioning needs to be understood from a psychological and communicative perspective. Thus far, social and organizational psychology has been used to discuss how social diversity affects group deliberation by changing the mindset of the participants. Argumentative exchanges have comparable effects, but they depend on cognitive diversity and emerge through critical interaction. (...) An example of a cognitive psychological theory is discussed that explains how mutual reasoning affects how we think, make decisions, and solve problems, as well as how cognitive biases may facilitate an efficient division of cognitive labor. These observations are compared with the existing results in the social epistemology of science. Moreover, I explicate the conceptual differences between the distributed and social processing of information. While argumentative exchanges belong to the latter domain, most existing simulations model distributed processing, which may compromise their real-world relevance and proper conceptual interpretation. However, I aim not to criticize the existing simulation methods but to promote an approach from the cognitive psychology of reasoning that complements the current use of organizational psychology and computer simulations by investigating a different set of mechanisms relating to similar phenomena of interest in the social epistemology of science. (shrink)

A lack of diversity remains a significant problem in many STEM communities. According to the epistemic approach to addressing these diversity problems, it is in a community’s interest to improve diversity because doing so can enhance the rigor and creativity of its work. However, we draw on empirical and theoretical evidence illustrating that this approach can trade on the epistemic exploitation of diverse community members. Our concept of epistemic exploitation holds when there is a relationship between two parties in which (...) one party accrues epistemic benefits from another party’s knowledge and epistemic location and, in doing so, harms the second party or sets back their interests. We demonstrate that the ironic outcome of this nominal application of the epistemic approach is that it undermines the epistemic benefits which it promises. Indeed, we show that epistemic exploitation undermines the relationships and interactions among community members that produce rigor and creativity. Our central argument is that for communities to reap the benefits of an epistemic approach to diversity, to implement a genuine epistemic approach, they need to develop cultures that ameliorate the harms faced by, and protect the interests of, their diverse community members. (shrink)

Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distinctive task-related cognitive resources, demographic diversity can, in certain circumstances, benefit collective performance by counteracting two types of conformity (...) that can arise in homogeneous groups: those relating to group-based trust and those connected to normative expectations towards in-groups. (shrink)

There has been a surge of recent interest in sociocultural diversity in machine learning research. Currently, however, there is a gap between discussions of measures and benefits of diversity in machine learning, 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 machine learning systems and make a case for their usefulness in fair and accountable machine learning. We do so by illustrating how they clarify the discourse around diversity in the context of machine learning systems, promote the formulation of more precise research questions about diversity’s impact, and provide conceptual tools to further advance research and practice. (shrink)

. Given the importance of the issue of scientific objectivity in our democratic societies and the significant development of citizen science, it is crucial to investigate how citizen science may either undermine or foster scientific objectivity. This paper identifies a variety of epistemic risks and benefits that participation of lay citizens in scientific inquiries may bring. It also discusses concrete actions and pending issues that should be addressed in order to foster objectivity in citizen science programs.

Two types of formal models - landscape search tasks and two-armed bandit models - are often used to study the effects that various social factors have on epistemic performance. I argue that they can be understood within a single framework. In this unified framework, I develop a model that may be used to understand the effects of functional and demographic diversity and their interaction. Using the unified model, I find that the benefit of demographic diversity is most pronounced in a (...) functionally homogeneous group, and decreases with the increase of functional diversity. (shrink)

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model which captures the role of ‘ability’ in a meaningful way, and use it to explore the trade-offs between diversity and ability (...) in scientific problem solving. (shrink)