We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e.g., demographic groups). Such biased clusterings can have deleterious implications for human-centric applications such as resource allocation. We present a fair k-means objective and algorithm to choose cluster centers that provide equitable costs for different groups. The algorithm, Fair-Lloyd, is a modification of Lloyd's heuristic for k-means, inheriting its simplicity, efficiency, and stability. In comparison with standard Lloyd's, we find that on benchmark data sets, Fair-Lloyd exhibits unbiased performance by ensuring that all groups have balanced costs in the output k-clustering, while incurring a negligible increase in running time, thus making it a viable fair option wherever k-means is currently used.
We initiate the study of fairness for ordinal regression, or ordinal classification. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that is approximately fair according to either notion. Our predictor consists of a threshold model, composed of a scoring function and a set of thresholds, and our strategy is based on a reduction to fair binary classification for learning the scoring function and local search for choosing the thresholds. We can control the extent to which we care about the accuracy vs the fairness of the predictor via a parameter. In extensive experiments we show that our strategy allows us to effectively explore the accuracy-vs-fairness trade-off and that it often compares favorably to "unfair" state-of-the-art methods for ordinal regression in that it yields predictors that are only slightly less accurate, but significantly more fair. IntroductionAs machine learning (ML) algorithms have become an integral part of numerous human-centric domains, they have been observed showing a range of concerning behaviors: facial recognition systems having higher accuracy on white male faces than on darker-skinned or female ones (Buolamwini and Gebru, 2017); criminal recidivism tools mislabeling black low-risk defendants as high-risk and white high-risk defendants as low-risk (Angwin et al., 2016); word2vec embeddings encoding stereotypes such as "father is to a doctor as a mother is to a nurse" (Bolukbasi et al., 2016); and image search systems answering the query "CEO" with a much higher fraction of images of men compared to the real-world fraction of male CEOs (Kay et al., 2015), to name just the most prominent examples. These observations have led to the study of fairness in ML (Barocas et al., 2019), and in the past years numerous ML tasks have been studied from a fairness perspective. While most works consider (binary) classification (e.g., Hardt et al., 2016), fair algorithms have also been developed for regression (e.g.,
Reusing passwords across multiple websites is a common practice that compromises security. Recently, Blum and Vempala have proposed password strategies to help people calculate, in their heads, passwords for different sites without dependence on third-party tools or external devices. Thus far, the security and efficiency of these "mental algorithms" has been analyzed only theoretically. But are such methods usable? We present the first usability study of humanly computable password strategies, involving a learning phase (to learn a password strategy), then a rehearsal phase (to login to a few websites), and multiple follow-up tests. In our user study, with training, participants were able to calculate a deterministic eight-character password for an arbitrary new website in under 20 seconds.
Reusing passwords across multiple websites is a common practice that compromises security. Recently, Blum and Vempala have proposed password strategies to help people calculate, in their heads, passwords for different sites without dependence on third-party tools or external devices. Thus far, the security and efficiency of these "mental algorithms" has been analyzed only theoretically. But are such methods usable? We present the first usability study of humanly computable password strategies, involving a learning phase (to learn a password strategy), then a rehearsal phase (to login to a few websites), and multiple follow-up tests. In our user study, with training, participants were able to calculate a deterministic eight-character password for an arbitrary new website in under 20 seconds.
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