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Algorithmic discrimination is an important aspect when data is used for predictive purposes. This paper analyzes the relationships between discrimination and classification, data set partitioning, and decision models, as well as correlation. The paper uses real world data sets to demonstrate the existence of discrimination and the independence between the discrimination of data sets and the discrimination of classification models.
The most popular face recognition benchmarks assume a distribution of subjects without much attention to their demographic attributes. In this work, we perform a comprehensive discrimination-aware experimentation of deep learning-based face recogniti
Conventional algorithmic fairness is Western in its sub-groups, values, and optimizations. In this paper, we ask how portable the assumptions of this largely Western take on algorithmic fairness are to a different geo-cultural context such as India.
Using the concept of principal stratification from the causal inference literature, we introduce a new notion of fairness, called principal fairness, for human and algorithmic decision-making. The key idea is that one should not discriminate among in
All learning algorithms for recommendations face inevitable and critical trade-off between exploiting partial knowledge of a users preferences for short-term satisfaction and exploring additional user preferences for long-term coverage. Although expl
Traditional approaches to ensure group fairness in algorithmic decision making aim to equalize ``total error rates for different subgroups in the population. In contrast, we argue that the fairness approaches should instead focus only on equalizing e