What knowers know well: standpoint theory and gender archeology

Wylie, Alison

doi:10.11606/51678-31662017000100002

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…To be sure, whether, how, and to what extent some marginalized standpoints afford epistemic advantages is a contextdependent question, open to empirical investigation (Grasswick, 2018). The thesis has found empirical support in various aspects of biomedical and social scientific research (Wylie, 2017).…”

Section: Comparative Family Of Conceptsmentioning

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: Comparative Family Of Conceptsmentioning

confidence: 99%

Diversity in sociotechnical machine learning systems

Fazelpour

De‐Arteaga

2022

Big Data & Society

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“…Rather, whether, how, and to what extent some marginalized standpoints a ord epistemic advantages is a context-dependent question, open to empirical investigation [46]. The thesis has found empirical support in various aspects of biomedical and social scienti c research [64,123,72].…”

Section: Comparative Family Of Conceptsmentioning

confidence: 98%

Diversity in Sociotechnical Machine Learning Systems

Fazelpour¹,

De‐Arteaga²

2021

Preprint

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There has been a surge of recent interest in sociocultural diversity in machine learning (ML) research, with researchers (i) examining the bene ts 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 bene ts 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 bene ts, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, di erent concepts of diversity are based on distinct rationales that should inform how we measure diversity in a given context. Similarly, the lack of speci city about the precise mechanisms underpinning diversity's potential bene ts can result in uninformative generalities, invalid experimental designs, and illicit interpretations of ndings. 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 di erent 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 bene t 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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Indigenous Archaeologies and the (Re)Action of Women Archaeologists: An Overview of the Brazilian Archaeology Context

Silva

2023

Women in Engineering and Science

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What knowers know well: standpoint theory and gender archeology

Cited by 7 publications

References 9 publications

Diversity in sociotechnical machine learning systems

Diversity in sociotechnical machine learning systems

Diversity in Sociotechnical Machine Learning Systems

Indigenous Archaeologies and the (Re)Action of Women Archaeologists: An Overview of the Brazilian Archaeology Context

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