Racial categories in machine learning

Abstract

Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled "Black" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage.

References in corpus (5)

• Mixing patterns in networks
• Equality of Opportunity in Supervised Learning
• Counterfactual Fairness
• Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment
• Causal Interventions for Fairness

Cited by in corpus (27)

• Fairness in Machine Learning: A Survey
• Towards a Critical Race Methodology in Algorithmic Fairness
• Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
• Interactive Model Cards: A Human-Centered Approach to Model Documentation
• Conservative AI and social inequality: Conceptualizing alternatives to bias through social theory
• Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation
• Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
• Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency
• Gaps in Information Access in Social Networks
• One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision
• About Face: A Survey of Facial Recognition Evaluation
• Computer Vision and Conflicting Values: Describing People with Automated Alt Text
• From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation
• On Consequentialism and Fairness
• Robustness Disparities in Commercial Face Detection
• Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI
• "What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness
• An Empirical Analysis of Racial Categories in the Algorithmic Fairness Literature
• Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models
• The Progression of Disparities within the Criminal Justice System: Differential Enforcement and Risk Assessment Instruments
• Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers
• Addressing multiple metrics of group fairness in data-driven decision making
• Disaggregated Interventions to Reduce Inequality
• Simplicity Bias Leads to Amplified Performance Disparities
• Continuous Subject-in-the-Loop Integration: Centering AI on Marginalized Communities
• Theory In, Theory Out: The uses of social theory in machine learning for social science
• A Sociotechnical View of Algorithmic Fairness