445 research outputs found
Racial categories in machine learning
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
50 Years of Test (Un)fairness: Lessons for Machine Learning
Quantitative definitions of what is unfair and what is fair have been
introduced in multiple disciplines for well over 50 years, including in
education, hiring, and machine learning. We trace how the notion of fairness
has been defined within the testing communities of education and hiring over
the past half century, exploring the cultural and social context in which
different fairness definitions have emerged. In some cases, earlier definitions
of fairness are similar or identical to definitions of fairness in current
machine learning research, and foreshadow current formal work. In other cases,
insights into what fairness means and how to measure it have largely gone
overlooked. We compare past and current notions of fairness along several
dimensions, including the fairness criteria, the focus of the criteria (e.g., a
test, a model, or its use), the relationship of fairness to individuals,
groups, and subgroups, and the mathematical method for measuring fairness
(e.g., classification, regression). This work points the way towards future
research and measurement of (un)fairness that builds from our modern
understanding of fairness while incorporating insights from the past.Comment: FAT* '19: Conference on Fairness, Accountability, and Transparency
(FAT* '19), January 29--31, 2019, Atlanta, GA, US
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