Certifying and removing disparate impact

What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender, religious practice) and an explicit description of the process. When the process is implemented using computers, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the algorithm, we propose making inferences based on the data the algorithm uses. We make four contributions to this problem. First, we link the legal notion of disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on analyzing the information leakage of the protected class from the other data attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.Comment: Extended version of paper accepted at 2015 ACM SIGKDD Conference on Knowledge Discovery and Data Minin

The effect of the Z = 64 subshell on IBA calculations

A recently proposed scheme for IBA calculations near Z = 64 which involves a drastic change in the proton boson number is critically examined. It is shown that this scheme is in disagreement with the experimental trends observed for the E2 transition rates

Optimal transport: Fast probabilistic approximation with exact solvers.

We propose a simple subsampling scheme for fast randomized approximate computation of optimal transport distances on finite spaces. This scheme operates on a random subset of the full data and can use any exact algorithm as a black-box back-end, including state-of-the-art solvers and entropically penalized versions. It is based on averaging the exact distances between empirical measures generated from independent samples from the original measures and can easily be tuned towards higher accuracy or shorter computation times. To this end, we give non-asymptotic deviation bounds for its accuracy in the case of discrete optimal transport problems. In particular, we show that in many important instances, including images (2D-histograms), the approximation error is independent of the size of the full problem. We present numerical experiments that demonstrate that a very good approximation in typical applications can be obtained in a computation time that is several orders of magnitude smaller than what is required for exact computation of the full problem

Bone Health in a Nonjaundiced Population of Children with Biliary Atresia

Objectives. To assess bone health in a cohort of nonjaundiced children with biliary atresia (BA) and the effect of growth and development on bone outcomes. Methods. Children ages one to eighteen years receiving care from Children's Hospital of Philadelphia were recruited. Each child was seen once and assessed for growth, pubertal development, concurrent medications, bilirubin, ALT, albumin, vitamin D status, bone mineral density (BMD), and bone mineral content (BMC) of the lumbar spine and whole body. Results. BMD declined significantly with age, and upon further analysis with a well-phenotyped control cohort, it was found that BMC was significantly decreased for both lumbar spine and whole body, even after adjustment for confounding variables. An age interaction was identified, with older subjects having a significantly greater impairment in BMC. Conclusions. These preliminary results demonstrate that children with BA, including those without jaundice, are likely to have compromised bone health even when accounting for height and puberty, which are common confounding factors in chronic disease. Further investigation is needed to identify the determinants of poor bone mineral status and to develop strategies to prevent osteoporosis later in life

Dairy attentuates oxidative and inflammatory stress in metabolic syndrome123

Background: Oxidative and inflammatory stress are elevated in obesity and are further augmented in metabolic syndrome. We showed previously that dairy components suppress the adipocyte- and macrophage-mediated generation of reactive oxygen species and inflammatory cytokines and systemic oxidative and inflammatory biomarkers in obesity

Fairness Testing: Testing Software for Discrimination

This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior. Evidence of software discrimination has been found in modern software systems that recommend criminal sentences, grant access to financial products, and determine who is allowed to participate in promotions. Our approach, Themis, generates efficient test suites to measure discrimination. Given a schema describing valid system inputs, Themis generates discrimination tests automatically and does not require an oracle. We evaluate Themis on 20 software systems, 12 of which come from prior work with explicit focus on avoiding discrimination. We find that (1) Themis is effective at discovering software discrimination, (2) state-of-the-art techniques for removing discrimination from algorithms fail in many situations, at times discriminating against as much as 98% of an input subdomain, (3) Themis optimizations are effective at producing efficient test suites for measuring discrimination, and (4) Themis is more efficient on systems that exhibit more discrimination. We thus demonstrate that fairness testing is a critical aspect of the software development cycle in domains with possible discrimination and provide initial tools for measuring software discrimination.Comment: Sainyam Galhotra, Yuriy Brun, and Alexandra Meliou. 2017. Fairness Testing: Testing Software for Discrimination. In Proceedings of 2017 11th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), Paderborn, Germany, September 4-8, 2017 (ESEC/FSE'17). https://doi.org/10.1145/3106237.3106277, ESEC/FSE, 201