The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning

The nascent field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last several years, three formal definitions of fairness have gained prominence: (1) anti-classification, meaning that protected attributes---like race, gender, and their proxies---are not explicitly used to make decisions; (2) classification parity, meaning that common measures of predictive performance (e.g., false positive and false negative rates) are equal across groups defined by the protected attributes; and (3) calibration, meaning that conditional on risk estimates, outcomes are independent of protected attributes. Here we show that all three of these fairness definitions suffer from significant statistical limitations. Requiring anti-classification or classification parity can, perversely, harm the very groups they were designed to protect; and calibration, though generally desirable, provides little guarantee that decisions are equitable. In contrast to these formal fairness criteria, we argue that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce. Such a strategy, while not universally applicable, often aligns well with policy objectives; notably, this strategy will typically violate both anti-classification and classification parity. In practice, it requires significant effort to construct suitable risk estimates. One must carefully define and measure the targets of prediction to avoid retrenching biases in the data. But, importantly, one cannot generally address these difficulties by requiring that algorithms satisfy popular mathematical formalizations of fairness. By highlighting these challenges in the foundation of fair machine learning, we hope to help researchers and practitioners productively advance the area

Choosing a Repository Platform: Open Source vs. Hosted Solutions

Discusses selection of a locally hosted, open-source system (DSpace/Fedora) versus a cloud-hosted, proprietary system (Digital Commons), it is important to note that these examples are merely illustrative. Libraries have a range of choices for repository software that includes open source and proprietary in any number of support environments, and exemplary repositories are flourishing on a variety of systems, both open source and proprietary. This chapter focuses on the differences between proprietary and open-source solutions, but also demonstrates how and why libraries choose a repository system. In writing about this process, we realized that it was important to acknowledge that there are two different audiences for this chapter: those who may just be starting out with building a repository at their institution, and those with an established repository who are considering a platform change. Thus, this chapter addresses the challenges and opportunities of platform selection in both circumstances

The Effect of Mycobacterium avium Complex Infections on Routine Mycobacterium bovis Diagnostic Tests

Bovine tuberculosis (bTB) is diagnosed in naturally infected populations exposed to a wide variety of other pathogens. This study describes the cell-mediated immune responses of cattle exposed to Mycobacterium avium subspecies paratuberculosis (Map) and Mycobacterium avium subspecies avium with particular reference to routine antefmortem Mycobacterium bovis diagnostic tests. The IFN-γ released in response to stimulated blood was found to peak later in the Map-exposed group and was more sustained when compared to the Maa-exposed group. There was a very close correlation between the responses to the purified protein derivatives (PPD) used for stimulation (PPDa, PPDb, and PPDj) with PPDa and PPDj most closely correlated. On occasion, in the Map-infected cattle, PPDb-biased responses were seen compared to PPDa suggesting that some Map-infected cattle could be misclassified as M. bovis infected using this test with these reagents. This bias was not seen when PPDj was used. SICCT results were consistent with the respective infections and all calves would have been classed skin test negative

Inhibition of insulin-degrading enzyme in human neurons promotes amyloid-β deposition

Alzheimer’s disease (AD) is characterised by the aggregation and deposition of amyloid-β (Aβ) peptides in the human brain. In age-related late-onset AD, deficient degradation and clearance, rather than enhanced production, of Aβ contributes to disease pathology. In the present study, we assessed the contribution of the two key Aβ-degrading zinc metalloproteases, insulin-degrading enzyme (IDE) and neprilysin (NEP), to Aβ degradation in human induced pluripotent stem cell (iPSC)-derived cortical neurons. Using an Aβ fluorescence polarisation assay, inhibition of IDE but not of NEP, blocked the degradation of Aβ by human neurons. When the neurons were grown in a 3D extracellular matrix to visualise Aβ deposition, inhibition of IDE but not NEP, increased the number of Aβ deposits. The resulting Aβ deposits were stained with the conformation-dependent, anti-amyloid antibodies A11 and OC that recognise Aβ aggregates in the human AD brain. Inhibition of the Aβ-forming β-secretase prevented the formation of the IDE-inhibited Aβ deposits. These data indicate that inhibition of IDE in live human neurons grown in a 3D matrix increased the deposition of Aβ derived from the proteolytic cleavage of the amyloid precursor protein. This work has implications for strategies aimed at enhancing IDE activity to promote Aβ degradation in AD

Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data

Recent advances in neuroscientific experimental techniques have enabled us to simultaneously record the activity of thousands of neurons across multiple brain regions. This has led to a growing need for computational tools capable of analyzing how task-relevant information is represented and communicated between several brain regions. Partial information decompositions (PIDs) have emerged as one such tool, quantifying how much unique, redundant and synergistic information two or more brain regions carry about a task-relevant message. However, computing PIDs is computationally challenging in practice, and statistical issues such as the bias and variance of estimates remain largely unexplored. In this paper, we propose a new method for efficiently computing and estimating a PID definition on multivariate Gaussian distributions. We show empirically that our method satisfies an intuitive additivity property, and recovers the ground truth in a battery of canonical examples, even at high dimensionality. We also propose and evaluate, for the first time, a method to correct the bias in PID estimates at finite sample sizes. Finally, we demonstrate that our Gaussian PID effectively characterizes inter-areal interactions in the mouse brain, revealing higher redundancy between visual areas when a stimulus is behaviorally relevant

Matched Pair Calibration for Ranking Fairness

We propose a test of fairness in score-based ranking systems called matched pair calibration. Our approach constructs a set of matched item pairs with minimal confounding differences between subgroups before computing an appropriate measure of ranking error over the set. The matching step ensures that we compare subgroup outcomes between identically scored items so that measured performance differences directly imply unfairness in subgroup-level exposures. We show how our approach generalizes the fairness intuitions of calibration from a binary classification setting to ranking and connect our approach to other proposals for ranking fairness measures. Moreover, our strategy shows how the logic of marginal outcome tests extends to cases where the analyst has access to model scores. Lastly, we provide an example of applying matched pair calibration to a real-word ranking data set to demonstrate its efficacy in detecting ranking bias.Comment: 19 pages, 8 figure