Diversity, Trust, and Conformity: A Simulation Study

Fazelpour, Sina; Steel, Daniel

doi:10.1017/psa.2021.25

Cited by 22 publications

(41 citation statements)

References 56 publications

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“…Importantly, socioculturally homogeneous groups are particularly susceptible to group influences that undermine efficient information elaboration. By reducing these detrimental effects of homogeneity, therefore, sociocultural diversity can epistemically benefit groups, even when not correlated with cognitive diversity in context (Phillips, 2017; Fazelpour and Steel, forthcoming).…”

Section: Potential Epistemic Benefits Of Diversitymentioning

confidence: 99%

Diversity in sociotechnical machine learning systems

Fazelpour

De‐Arteaga

2022

Big Data & Society

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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.

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Section: Potential Epistemic Benefits Of Diversitymentioning

confidence: 99%

Diversity in sociotechnical machine learning systems

Fazelpour

De‐Arteaga

2022

Big Data & Society

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“…Further, they highlight how the best outcomes for an individual do not straightforwardly translate into the best outcomes for a group. In general, such models aid theory building (Guest & Martin, 2021), though we note recent calls for empirically sensitive robustness in simulation studies (Fazelpour & Steel, 2022).…”

Section: The Benefits Of Diversitymentioning

confidence: 99%

“…For example, beliefs were better calibrated for diverse groups than for homogeneous groups in an experimental stock-market task, in which people had to gauge the value of some commodities (Levine et al, 2014), and in a murder-mystery task, in which people had to gather and evaluate evidence (Phillips et al, 2009). Although distrust can slow the spread of information, it can also slow the spread of pernicious misinformation (Fazelpour & Steel, 2022). It is not even necessary that this effect be driven by distrust, because homogeneity can make people less aware that different perspectives exist (Apfelbaum et al, 2014).…”

Section: Interactional Costs Sometimes Have Epistemic Benefitsmentioning

confidence: 99%

The Diversity Gap: When Diversity Matters for Knowledge

Sulik

Bahrami

Deroy

2021

Perspect Psychol Sci

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Can diversity make for better science? Although diversity has ethical and political value, arguments for its epistemic value require a bridge between normative and mechanistic considerations, demonstrating why and how diversity beneﬁts collective intelligence. However, a major hurdle is that the beneﬁts themselves are rather mixed: Quantitative evidence from psychology and behavioral sciences sometimes shows a positive epistemic effect of diversity, but often shows a null effect, or even a negative effect. Here we argue that to make progress with these why and how questions, we need ﬁrst to rethink when one ought to expect a beneﬁt of cognitive diversity. In doing so, we highlight that the beneﬁts of cognitive diversity are not equally distributed about collective intelligence tasks and are best seen for complex, multistage, creative problem solving, during problem posing and hypothesis generation. Throughout, we additionally outline a series of mechanisms relating diversity and problem complexity, and show how this perspective can inform metascience questions.

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“…Less than a decade later, things have changed: not only has the theme of network epistemology received a growing interest of philosophers, but the method of formal modeling of scientific inquiry has become a well‐established approach in social epistemology of science. In particular, different types of computer simulations in the form of agent‐based models (ABMs) have been developed to address various aspects of scientific interaction, often too complex to be tractable via analytic means, such as optimal forms of collaboration (Zollman, 2017), factors leading to scientific polarization (O’Connor & Weatherall, 2018; Weatherall & O’Connor, 2021b), effects of conformity (Weatherall & O’Connor, 2021a), effects of demographic diversity (Fazelpour & Steel, 2022), optimal regulations of dual‐use research (Wagner & Herington, 2020), etc.…”

Section: Introductionmentioning

confidence: 99%

“…aspects of scientific interaction, often too complex to be tractable via analytic means, such as optimal forms of collaboration (Zollman, 2017), factors leading to scientific polarization Weatherall & O'Connor, 2021b), effects of conformity (Weatherall & O'Connor, 2021a), effects of demographic diversity (Fazelpour & Steel, 2022), optimal regulations of dual-use research (Wagner & Herington, 2020), etc.…”

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confidence: 99%

Agent‐based models of scientific interaction

Šešelja

2022

Philosophy Compass

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The paper examines recent developments in agent‐based modeling of scientific inquiry with a special focus on network epistemology. It provides a survey of different types of ABMs studying network effects in scientific inquiry: ABMs based on bandit problems, ABMs based on epistemic landscapes and ABMs based on argumentative dynamics. It further presents models that study the impact of biased and deceptive researchers on the success of collective inquiry. The paper concludes with a discussion on the contribution of ABMs to the broader field of philosophy of science given their highly idealized nature.

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Diversity, Trust, and Conformity: A Simulation Study

Cited by 22 publications

References 56 publications

Diversity in sociotechnical machine learning systems

Diversity in sociotechnical machine learning systems

The Diversity Gap: When Diversity Matters for Knowledge

Agent‐based models of scientific interaction

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