The issue of algorithmic bias is closely related to the interpretability of algorithmic predictions. We single out three aspects of ML algorithms that can lead to discrimination: the data-mining process and categorization, their automaticity, and their opacity. However, it turns out that this requirement overwhelmingly affects a historically disadvantaged racial minority because members of this group are less likely to complete a high school education. Before we consider their reasons, however, it is relevant to sketch how ML algorithms work. English Language Arts. Is discrimination a bias. Relationship between Fairness and Predictive Performance. For instance, males have historically studied STEM subjects more frequently than females so if using education as a covariate, you would need to consider how discrimination by your model could be measured and mitigated.
While a human agent can balance group correlations with individual, specific observations, this does not seem possible with the ML algorithms currently used. From there, a ML algorithm could foster inclusion and fairness in two ways. Bechmann, A. and G. C. Bowker. 2011) formulate a linear program to optimize a loss function subject to individual-level fairness constraints. Second, data-mining can be problematic when the sample used to train the algorithm is not representative of the target population; the algorithm can thus reach problematic results for members of groups that are over- or under-represented in the sample. While situation testing focuses on assessing the outcomes of a model, its results can be helpful in revealing biases in the starting data. For instance, it is theoretically possible to specify the minimum share of applicants who should come from historically marginalized groups [; see also 37, 38, 59]. It's therefore essential that data practitioners consider this in their work as AI built without acknowledgement of bias will replicate and even exacerbate this discrimination. Insurance: Discrimination, Biases & Fairness. However, before identifying the principles which could guide regulation, it is important to highlight two things. We then discuss how the use of ML algorithms can be thought as a means to avoid human discrimination in both its forms. Inputs from Eidelson's position can be helpful here.
For many, the main purpose of anti-discriminatory laws is to protect socially salient groups Footnote 4 from disadvantageous treatment [6, 28, 32, 46]. Such labels could clearly highlight an algorithm's purpose and limitations along with its accuracy and error rates to ensure that it is used properly and at an acceptable cost [64]. 2018) use a regression-based method to transform the (numeric) label so that the transformed label is independent of the protected attribute conditioning on other attributes. For her, this runs counter to our most basic assumptions concerning democracy: to express respect for the moral status of others minimally entails to give them reasons explaining why we take certain decisions, especially when they affect a person's rights [41, 43, 56]. ICA 2017, 25 May 2017, San Diego, United States, Conference abstract for conference (2017). Selection Problems in the Presence of Implicit Bias. For an analysis, see [20]. In addition, Pedreschi et al. They define a distance score for pairs of individuals, and the outcome difference between a pair of individuals is bounded by their distance. Bias is to fairness as discrimination is to help. 3 Opacity and objectification. A Reductions Approach to Fair Classification.
Hence, anti-discrimination laws aim to protect individuals and groups from two standard types of wrongful discrimination. Calders, T., Karim, A., Kamiran, F., Ali, W., & Zhang, X. For instance, it is doubtful that algorithms could presently be used to promote inclusion and diversity in this way because the use of sensitive information is strictly regulated. 2017) demonstrates that maximizing predictive accuracy with a single threshold (that applies to both groups) typically violates fairness constraints. AI’s fairness problem: understanding wrongful discrimination in the context of automated decision-making. Theoretically, it could help to ensure that a decision is informed by clearly defined and justifiable variables and objectives; it potentially allows the programmers to identify the trade-offs between the rights of all and the goals pursued; and it could even enable them to identify and mitigate the influence of human biases. The idea that indirect discrimination is only wrongful because it replicates the harms of direct discrimination is explicitly criticized by some in the contemporary literature [20, 21, 35]. Emergence of Intelligent Machines: a series of talks on algorithmic fairness, biases, interpretability, etc.
First, we identify different features commonly associated with the contemporary understanding of discrimination from a philosophical and normative perspective and distinguish between its direct and indirect variants. First, equal means requires the average predictions for people in the two groups should be equal. Therefore, the use of algorithms could allow us to try out different combinations of predictive variables and to better balance the goals we aim for, including productivity maximization and respect for the equal rights of applicants. Harvard University Press, Cambridge, MA (1971). Mitigating bias through model development is only one part of dealing with fairness in AI. 2010) propose to re-label the instances in the leaf nodes of a decision tree, with the objective to minimize accuracy loss and reduce discrimination. In addition to the issues raised by data-mining and the creation of classes or categories, two other aspects of ML algorithms should give us pause from the point of view of discrimination. ICDM Workshops 2009 - IEEE International Conference on Data Mining, (December), 13–18. One may compare the number or proportion of instances in each group classified as certain class. Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. Human decisions and machine predictions. Proceedings - IEEE International Conference on Data Mining, ICDM, (1), 992–1001. As a consequence, it is unlikely that decision processes affecting basic rights — including social and political ones — can be fully automated. A paradigmatic example of direct discrimination would be to refuse employment to a person on the basis of race, national or ethnic origin, colour, religion, sex, age or mental or physical disability, among other possible grounds.
Murphy, K. : Machine learning: a probabilistic perspective. Neg can be analogously defined. We hope these articles offer useful guidance in helping you deliver fairer project outcomes. 2014) adapt AdaBoost algorithm to optimize simultaneously for accuracy and fairness measures. We come back to the question of how to balance socially valuable goals and individual rights in Sect. Keep an eye on our social channels for when this is released.
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