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Empirical risk minimization for metric learning using privileged information
Traditional metric learning methods usually make decisions based on a fixed threshold, which may result in a suboptimal metric when the inter-class and inner-class variations are complex. To address this issue, in this paper we propose an effective metric learning method by exploiting privileged information to relax the fixed threshold under the empirical risk minimization framework. Privileged information describes useful high-level semantic information that is only available during training. Our goal is to improve the performance by incorporating privileged information to design a locally adaptive decision function. We jointly learn two distance metrics by minimizing the empirical loss penalizing the difference between the distance in the original space and that in the privileged space. The distance in the privileged space functions as a locally adaptive decision threshold, which can guide the decision making like a teacher. We optimize the objective function using the Accelerated Proximal Gradient approach to obtain a global optimum solution. Experiment results show that by leveraging privileged information, our proposed method can achieve satisfactory performance
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
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