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.