Progress in Whole Genome Sequencing (WGS) will soon allow a large number of individuals to have their genome fully sequenced. This lays the foundations to improve modern healthcare, enabling a new era of personalized medicine where diagnosis and treatment is tailored to the patient's genetic makeup. It also allows individuals motivated by personal curiosity to have access to their genetic information, and use it, e.g., to trace their ancestry. However, the very same progress also amplifies a number of ethical and privacy concerns, that stem from the unprecedented sensitivity of genomic information and that are not well studied. This paper presents an exploratory ethnographic study of users' perception of privacy and ethical issues with WGS, as well as their attitude toward different WGS programs. We report on a series of semi-structured interviews, involving 16 participants, and analyze the results both quantitatively and qualitatively. Our analysis shows that users exhibit common trust concerns and fear of discrimination, and demand to retain strict control over their genetic information. Finally, we highlight the need for further research in the area and follow-up studies that build on our initial findings.
Also, prior work analyzed the issue of labor discrimination in relationship to disease predisposition, mostly from the legal standpoint. We refer to the work by @cite_39 for details, along with a review of well-known rulings.
@cite_39
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Purpose: The impact of laws restricting health insurers' use of genetic information has been assessed from two main vantage points: (1) whether they reduce the extent of genetic discrimination and (2) whether they reduce the fear of discrimination and the resulting deterrence to undergo genetic testing. A previous report from this study concluded that there are almost no well-documented cases of health insurers either asking for or using presymptomatic genetic test results in their underwriting decisions, either before or after these laws, or in states with or without these laws. This report evaluates the perceptions and the resulting behavior by patients and clinicians. Methods: A comparative case study analysis was performed in seven states with different laws respecting health insurers' use of genetic information (no law, new prohibition, mature prohibition). Semistructured interviews were conducted in person with five patient advocates and with 30 experienced genetic counselors or medical geneticists, most of whom deal with adult-onset disorders. Also, multiple informed consent forms and patient information brochures were collected and analyzed using qualitative methods. Results: Patients' and clinicians' fear of genetic discrimination greatly exceeds reality, at least for health insurance. It is uncertain how much this fear actually deters genetic testing. The greatest deterrence is to those who do not want to submit the costs of testing for reimbursement and who cannot afford to pay for testing. There appears to be little deterrence for tests that are more easily affordable or when the need for the information is much greater. Fear of discrimination plays virtually no role in testing decisions in pediatric or prenatal situations, but is significant for adult-onset genetic conditions. Conclusion: Existing laws have not greatly reduced the fear of discrimination. This may be due, in part, to clinicians' lack of confidence that these laws can prevent discrimination until there are test cases of actual enforcement. Ironically, there may be so little actual discrimination that it may not be possible to initiate good test cases. Recently, the following discrimination aware classification problem was introduced: given a labeled dataset and an attribute B, find a classifier with high predictive accuracy that at the same time does not discriminate on the basis of the given attribute B. This problem is motivated by the fact that often available historic data is biased due to discrimination, e.g., when B denotes ethnicity. Using the standard learners on this data may lead to wrongfully biased classifiers, even if the attribute B is removed from training data. Existing solutions for this problem consist in “cleaning away” the discrimination from the dataset before a classifier is learned. In this paper we study an alternative approach in which the non-discrimination constraint is pushed deeply into a decision tree learner by changing its splitting criterion and pruning strategy. Experimental evaluation shows that the proposed approach advances the state-of-the-art in the sense that the learned decision trees have a lower discrimination than models provided by previous methods, with little loss in accuracy. Recently, the following Discrimination-Aware Classification Problem was introduced: Suppose we are given training data that exhibit unlawful discrimination; e.g., toward sensitive attributes such as gender or ethnicity. The task is to learn a classifier that optimizes accuracy, but does not have this discrimination in its predictions on test data. This problem is relevant in many settings, such as when the data are generated by a biased decision process or when the sensitive attribute serves as a proxy for unobserved features. In this paper, we concentrate on the case with only one binary sensitive attribute and a two-class classification problem. We first study the theoretically optimal trade-off between accuracy and non-discrimination for pure classifiers. Then, we look at algorithmic solutions that preprocess the data to remove discrimination before a classifier is learned. We survey and extend our existing data preprocessing techniques, being suppression of the sensitive attribute, massaging the dataset by changing class labels, and reweighing or resampling the data to remove discrimination without relabeling instances. These preprocessing techniques have been implemented in a modified version of Weka and we present the results of experiments on real-life data. Most of the decisions in the today's knowledge society are taken on the basis of historical data by extracting models, patterns, profiles, and rules of human behavior in support of (automated) decision making. There is then the need of developing models, methods and technologies for modelling the processes of discrimination analysis in order to discover and prevent discrimination phenomena. In this respect, discrimination analysis from data should build over the large body of existing legal and economic studies. This paper intends to provide a multi-disciplinary survey of the literature on discrimination data analysis, including methods for data collection, empirical studies, controlled experiments, statistical evidence, and their legal requirements and grounds. We cover the following mainstream research lines: labour economic models, (quasi-)experimental approaches such as auditing and controlled experiments, profiling-based approaches such as racial profiling and credit markets, and the recently blooming research on knowledge discovery approaches. In a study of the perceptions of 332 members of genetic support groups with one or more of 101 different genetic disorders in the family, it was found that as a result of a genetic disorder 25 percent of the respondents or affected family members believed they were refused life insurance, 22 percent believed they were refused health insurance, and 13 percent believed they were denied or let go from a job. Fear of genetic discrimination resulted in 9 percent of respondents or family members refusing to be tested for genetic conditions, 18 percent not revealing genetic information to insurers, and 17 percent not revealing information to employers. The level of perceived discrimination points to the need for more information to determine the extent and scope of the problem. With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect people's lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be socially and legally fair from a viewpoint of social responsibility, namely, it must be unbiased and nondiscriminatory in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. From a privacy-preserving viewpoint, this can be interpreted as hiding sensitive information when classification results are observed. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency. In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Rules extracted from databases by data mining techniques, such as classification or association rules, when used for decision tasks such as benefit or credit approval, can be discriminatory in the above sense. In this paper, the notion of discriminatory classification rules is introduced and studied. Providing a guarantee of non-discrimination is shown to be a non trivial task. A naive approach, like taking away all discriminatory attributes, is shown to be not enough when other background knowledge is available. Our approach leads to a precise formulation of the redlining problem along with a formal result relating discriminatory rules with apparently safe ones by means of background knowledge. An empirical assessment of the results on the German credit dataset is also provided. With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency. With the support of the legally-grounded methodology of situation testing, we tackle the problems of discrimination discovery and prevention from a dataset of historical decisions by adopting a variant of k-NN classification. A tuple is labeled as discriminated if we can observe a significant difference of treatment among its neighbors belonging to a protected-by-law group and its neighbors not belonging to it. Discrimination discovery boils down to extracting a classification model from the labeled tuples. Discrimination prevention is tackled by changing the decision value for tuples labeled as discriminated before training a classifier. The approach of this paper overcomes legal weaknesses and technical limitations of existing proposals. Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about our model of the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them. Nowadays, many decisions are made using predictive models built on historical data.Predictive models may systematically discriminate groups of people even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies how to make predictive models free from discrimination, when historical data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. Discrimination refers to disadvantageous treatment of a person based on belonging to a category rather than on individual merit. In this survey we review and organize various discrimination measures that have been used for measuring discrimination in data, as well as in evaluating performance of discrimination-aware predictive models. We also discuss related measures from other disciplines, which have not been used for measuring discrimination, but potentially could be suitable for this purpose. We computationally analyze properties of selected measures. We also review and discuss measuring procedures, and present recommendations for practitioners. The primary target audience is data mining, machine learning, pattern recognition, statistical modeling researchers developing new methods for non-discriminatory predictive modeling. In addition, practitioners and policy makers would use the survey for diagnosing potential discrimination by predictive models. Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them. Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on defining conditions for fairness, but does not define satisfactory measures of algorithmic unfairness. In this paper, we focus on the following question: Given two unfair algorithms, how should we determine which of the two is more unfair? Our core idea is to use existing inequality indices from economics to measure how unequally the outcomes of an algorithm benefit different individuals or groups in a population. Our work offers a justified and general framework to compare and contrast the (un)fairness of algorithmic predictors. This unifying approach enables us to quantify unfairness both at the individual and the group level. Further, our work reveals overlooked tradeoffs between different fairness notions: using our proposed measures, the overall individual-level unfairness of an algorithm can be decomposed into a between-group and a within-group component. Earlier methods are typically designed to tackle only between-group un- fairness, which may be justified for legal or other reasons. However, we demonstrate that minimizing exclusively the between-group component may, in fact, increase the within-group, and hence the overall unfairness. We characterize and illustrate the tradeoffs between our measures of (un)fairness and the prediction accuracy. Les principes de l'ethique biomedicale constituent, par leur influence, l’ouvrage majeur de l’ethique medicale contemporaine. Au-dela de son contexte nord-americain d’elaboration, de la deontologie medicale traditionnelle et des theories morales classiques, la reflexion proposee a reconfigure l’analyse des questions ethiques liees a la relation de soin et au monde de la sante. Depuis la premiere edition de ce texte (1979), les auteurs n’ont eu de cesse de le remanier et d’en presenter des versions integrant toujours davantage leurs reponses aux objections et critiques qu’ils recevaient ou se formulaient eux-memes. C’est la 5e edition (dans l’attente de la 6e), datant de 2001, qui est ici traduite pour la premiere fois. Livre en debat, ne lui-meme des discussions engagees aux Etats-Unis dans les annees 1970, notamment sur l’ethique des essais cliniques, il vise a determiner des principes-reperes susceptibles d’eclairer les pratiques medicales et les argumentations qui les accompagnent des lors qu’elles engagent un rapport aux normes et aux valeurs : le principe d’autonomie, le principe de non-malfaisance, le principe de bienfaisance et le principe de justice. Le statut de la theorie dans la vie morale, les rapports entre les principes et les elements factuels, les rapports des differents principes entre eux sont les problemes qui structurent continument la reflexion des auteurs. Ainsi s’edifie une approche se voulant fine, souple et rigoureuse de ce qui peut justifier et guider le raisonnement ethique dans les prestations de sante et les relations de soin. Couples looking for jobs in the same labor market may cause instabilities. We deter- mine a natural preference domain, the domain of weakly responsive preferences, that guarantees stability. Under a restricted unemployment aversion condition we show that this domain is maximal for the existence of stable matchings. We illustrate how small deviations from (weak) responsiveness, that model the wish of couples to be closer together, cause instability, even when we use a weaker stability notion that excludes myopic blocking. Our remaining results deal with various properties of the set of stable matchings for "responsive couples markets," viz., optimal- ity, filled positions, and manipulation. Journal of Economic Literature Classification Numbers: C78; J41. The concept of classification without discrimination is a new area of research. (Kamiran & Calders, ) introduced the idea of Classification with No Discrimination (CND)and proposed a solution based on “massaging” the data to remove the discrimination from it with the least possible changes. In this paper, we propose a new solution to the CND problem by introducing a sampling scheme for making the data discrimination free instead of relabeling the dataset. On the resulting non-discriminatory dataset we then learn a classifier. This new method is not only less intrusive as compared to the “massaging” but also outperforms the “reweighing” approach of (, 2009). The proposed method has been implemented and experimental results on the Census Income dataset show promising results: in all experiments our method performs onpar with the state-of-the art non-discriminatory techniques. Abstract : The discrimination problem (two population case) may be defined as follows: e random variable Z, of observed value z, is distributed over some space (say, p-dimensional) either according to distribution F, or according to distribution G. The problem is to decide, on the basis of z, which of the two distributions Z has. In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data. We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand; (2) an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly. We also present an adaptation of our approach to achieve the complementary goal of "fair affirmative action," which guarantees statistical parity (i.e., the demographics of the set of individuals receiving any classification are the same as the demographics of the underlying population), while treating similar individuals as similarly as possible. Finally, we discuss the relationship of fairness to privacy: when fairness implies privacy, and how tools developed in the context of differential privacy may be applied to fairness. We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand; (2) an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly. We also present an adaptation of our approach to achieve the complementary goal of "fair affirmative action," which guarantees statistical parity (i.e., the demographics of the set of individuals receiving any classification are the same as the demographics of the underlying population), while treating similar individuals as similarly as possible. Finally, we discuss the relationship of fairness to privacy: when fairness implies privacy, and how tools developed in the context of differential privacy may be applied to fairness.