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These include, but are not necessarily limited to, race, national or ethnic origin, colour, religion, sex, age, mental or physical disability, and sexual orientation. Second, we show how clarifying the question of when algorithmic discrimination is wrongful is essential to answer the question of how the use of algorithms should be regulated in order to be legitimate. The Marshall Project, August 4 (2015). This series of posts on Bias has been co-authored by Farhana Faruqe, doctoral student in the GWU Human-Technology Collaboration group. However, here we focus on ML algorithms. This opacity of contemporary AI systems is not a bug, but one of their features: increased predictive accuracy comes at the cost of increased opacity. Given what was highlighted above and how AI can compound and reproduce existing inequalities or rely on problematic generalizations, the fact that it is unexplainable is a fundamental concern for anti-discrimination law: to explain how a decision was reached is essential to evaluate whether it relies on wrongful discriminatory reasons. What we want to highlight here is that recognizing that compounding and reconducting social inequalities is central to explaining the circumstances under which algorithmic discrimination is wrongful. First, the use of ML algorithms in decision-making procedures is widespread and promises to increase in the future. If fairness or discrimination is measured as the number or proportion of instances in each group classified to a certain class, then one can use standard statistical tests (e. g., two sample t-test) to check if there is systematic/statistically significant differences between groups. As he writes [24], in practice, this entails two things: First, it means paying reasonable attention to relevant ways in which a person has exercised her autonomy, insofar as these are discernible from the outside, in making herself the person she is. Hellman, D. : Discrimination and social meaning. 2018) showed that a classifier achieve optimal fairness (based on their definition of a fairness index) can have arbitrarily bad accuracy performance. Insurance: Discrimination, Biases & Fairness. 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].
As Lippert-Rasmussen writes: "A group is socially salient if perceived membership of it is important to the structure of social interactions across a wide range of social contexts" [39]. Under this view, it is not that indirect discrimination has less significant impacts on socially salient groups—the impact may in fact be worse than instances of directly discriminatory treatment—but direct discrimination is the "original sin" and indirect discrimination is temporally secondary. Add your answer: Earn +20 pts. Roughly, according to them, algorithms could allow organizations to make decisions more reliable and constant. For instance, the question of whether a statistical generalization is objectionable is context dependent. Introduction to Fairness, Bias, and Adverse Impact. Though these problems are not all insurmountable, we argue that it is necessary to clearly define the conditions under which a machine learning decision tool can be used. From hiring to loan underwriting, fairness needs to be considered from all angles. Conversely, fairness-preserving models with group-specific thresholds typically come at the cost of overall accuracy. Pensylvania Law Rev. Write: "it should be emphasized that the ability even to ask this question is a luxury" [; see also 37, 38, 59]. The first is individual fairness which appreciates that similar people should be treated similarly. First, the typical list of protected grounds (including race, national or ethnic origin, colour, religion, sex, age or mental or physical disability) is an open-ended list.
2013): (1) data pre-processing, (2) algorithm modification, and (3) model post-processing. Ethics 99(4), 906–944 (1989). When we act in accordance with these requirements, we deal with people in a way that respects the role they can play and have played in shaping themselves, rather than treating them as determined by demographic categories or other matters of statistical fate. 2 Discrimination, artificial intelligence, and humans. Williams, B., Brooks, C., Shmargad, Y. : How algorightms discriminate based on data they lack: challenges, solutions, and policy implications. Bias is to fairness as discrimination is to read. If this does not necessarily preclude the use of ML algorithms, it suggests that their use should be inscribed in a larger, human-centric, democratic process. Burrell, J. : How the machine "thinks": understanding opacity in machine learning algorithms.
Their definition is rooted in the inequality index literature in economics. Books and Literature. The wrong of discrimination, in this case, is in the failure to reach a decision in a way that treats all the affected persons fairly. Maya Angelou's favorite color? Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Second, however, this case also highlights another problem associated with ML algorithms: we need to consider the underlying question of the conditions under which generalizations can be used to guide decision-making procedures. Second, we show how ML algorithms can nonetheless be problematic in practice due to at least three of their features: (1) the data-mining process used to train and deploy them and the categorizations they rely on to make their predictions; (2) their automaticity and the generalizations they use; and (3) their opacity. 2 AI, discrimination and generalizations. Algorithm modification directly modifies machine learning algorithms to take into account fairness constraints. Bias is to fairness as discrimination is to support. Neg can be analogously defined.
What's more, the adopted definition may lead to disparate impact discrimination. The inclusion of algorithms in decision-making processes can be advantageous for many reasons. The question of if it should be used all things considered is a distinct one. 2017) develop a decoupling technique to train separate models using data only from each group, and then combine them in a way that still achieves between-group fairness.