Representativeness in Statistics, Politics, and Machine Learning

Chasalow, Kyla; Levy, Karen

doi:10.1145/3442188.3445872

Cited by 32 publications

(17 citation statements)

References 95 publications

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“…All data are unrepresentative in some way (e.g., relative to some targets or for some variables); all data have limitations (Chasalow & Levy 2021). The task becomes to make those limitations explicit and investigate whether they matter for the goal at hand.…”

Section: Problem Formalizationmentioning

confidence: 99%

Algorithms and Decision-Making in the Public Sector

Levy

Chasalow

Riley

2021

Annu. Rev. Law. Soc. Sci.

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This article surveys the use of algorithmic systems to support decision-making in the public sector. Governments adopt, procure, and use algorithmic systems to support their functions within several contexts—including criminal justice, education, and benefits provision—with important consequences for accountability, privacy, social inequity, and public participation in decision-making. We explore the social implications of municipal algorithmic systems across a variety of stages, including problem formulation, technology acquisition, deployment, and evaluation. We highlight several open questions that require further empirical research. Expected final online publication date for the Annual Review of Law and Social Science, Volume 17 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Problem Formalizationmentioning

confidence: 99%

Algorithms and Decision-Making in the Public Sector

Levy

Chasalow

Riley

2021

Annu. Rev. Law. Soc. Sci.

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“…Chasalow and Levy (2021) argue that like co-design, ‘representation’ and ‘inclusion’ are ‘suitcase’ words that can carry many different meanings which are not merely semantic, but normative (e.g. political legitimacy) and epistemic (e.g.…”

Section: Avoiding the Pitfalls: Opportunities For Critical Research A...mentioning

confidence: 99%

Co-design and ethical artificial intelligence for health: An agenda for critical research and practice

Donia

Shaw

2021

Big Data & Society

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Applications of artificial intelligence/machine learning (AI/ML) in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ML for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ML introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical data studies and critical digital health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ML for health care. We suggest that AI/ML technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ML for health care and conclude with suggestions for addressing these practical and conceptual challenges.

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“…Alternately, one can assess the diversity of a focal group in terms of the extent to which it matches an ideal distribution over other attribute categories (e.g., a distribution that is skewed towards the historically marginalized group 4 ) (e.g., [52]). While there are many potential ways 8 For a broader discussion of the manifold meanings of representativeness, see Chasalow and Levy [25].…”

Section: Comparative Family Of Conceptsmentioning

confidence: 99%

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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Representativeness in Statistics, Politics, and Machine Learning

Cited by 32 publications

References 95 publications

Algorithms and Decision-Making in the Public Sector

Algorithms and Decision-Making in the Public Sector

Co-design and ethical artificial intelligence for health: An agenda for critical research and practice

Diversity in Sociotechnical Machine Learning Systems

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