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First, the context and potential impact associated with the use of a particular algorithm should be considered. It's also important to choose which model assessment metric to use, these will measure how fair your algorithm is by comparing historical outcomes and to model predictions. Accordingly, this shows how this case may be more complex than it appears: it is warranted to choose the applicants who will do a better job, yet, this process infringes on the right of African-American applicants to have equal employment opportunities by using a very imperfect—and perhaps even dubious—proxy (i. e., having a degree from a prestigious university). A general principle is that simply removing the protected attribute from training data is not enough to get rid of discrimination, because other correlated attributes can still bias the predictions. 2 Discrimination through automaticity. Bias is to Fairness as Discrimination is to. In the particular context of machine learning, previous definitions of fairness offer straightforward measures of discrimination. 2017) or disparate mistreatment (Zafar et al. As Boonin [11] has pointed out, other types of generalization may be wrong even if they are not discriminatory. The White House released the American Artificial Intelligence Initiative:Year One Annual Report and supported the OECD policy. Considerations on fairness-aware data mining. There is evidence suggesting trade-offs between fairness and predictive performance. This problem is known as redlining. To pursue these goals, the paper is divided into four main sections.
Harvard Public Law Working Paper No. Romei, A., & Ruggieri, S. A multidisciplinary survey on discrimination analysis. If belonging to a certain group directly explains why a person is being discriminated against, then it is an instance of direct discrimination regardless of whether there is an actual intent to discriminate on the part of a discriminator. Bias is to fairness as discrimination is to trust. This is a vital step to take at the start of any model development process, as each project's 'definition' will likely be different depending on the problem the eventual model is seeking to address. Proceedings of the 27th Annual ACM Symposium on Applied Computing. In addition, algorithms can rely on problematic proxies that overwhelmingly affect marginalized social groups. Hence, discrimination, and algorithmic discrimination in particular, involves a dual wrong. 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.
On the other hand, equal opportunity may be a suitable requirement, as it would imply the model's chances of correctly labelling risk being consistent across all groups. Doyle, O. : Direct discrimination, indirect discrimination and autonomy. Caliskan, A., Bryson, J. J., & Narayanan, A. Add your answer: Earn +20 pts. Artificial Intelligence and Law, 18(1), 1–43. 128(1), 240–245 (2017). Take the case of "screening algorithms", i. e., algorithms used to decide which person is likely to produce particular outcomes—like maximizing an enterprise's revenues, who is at high flight risk after receiving a subpoena, or which college applicants have high academic potential [37, 38]. In terms of decision-making and policy, fairness can be defined as "the absence of any prejudice or favoritism towards an individual or a group based on their inherent or acquired characteristics". AI’s fairness problem: understanding wrongful discrimination in the context of automated decision-making. The MIT press, Cambridge, MA and London, UK (2012). Troublingly, this possibility arises from internal features of such algorithms; algorithms can be discriminatory even if we put aside the (very real) possibility that some may use algorithms to camouflage their discriminatory intents [7]. Kleinberg, J., Ludwig, J., Mullainathan, S., Sunstein, C. : Discrimination in the age of algorithms.
18(1), 53–63 (2001). 3 Discriminatory machine-learning algorithms. 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. Princeton university press, Princeton (2022).
Addressing Algorithmic Bias. Even though fairness is overwhelmingly not the primary motivation for automating decision-making and that it can be in conflict with optimization and efficiency—thus creating a real threat of trade-offs and of sacrificing fairness in the name of efficiency—many authors contend that algorithms nonetheless hold some potential to combat wrongful discrimination in both its direct and indirect forms [33, 37, 38, 58, 59]. Test bias vs test fairness. Measurement bias occurs when the assessment's design or use changes the meaning of scores for people from different subgroups. Retrieved from - Chouldechova, A.
In the case at hand, this may empower humans "to answer exactly the question, 'What is the magnitude of the disparate impact, and what would be the cost of eliminating or reducing it? '" Another case against the requirement of statistical parity is discussed in Zliobaite et al. Pensylvania Law Rev. A TURBINE revolves in an ENGINE. While a human agent can balance group correlations with individual, specific observations, this does not seem possible with the ML algorithms currently used. 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]. For a general overview of these practical, legal challenges, see Khaitan [34]. Bias is to fairness as discrimination is to discrimination. This highlights two problems: first it raises the question of the information that can be used to take a particular decision; in most cases, medical data should not be used to distribute social goods such as employment opportunities. Introduction to Fairness, Bias, and Adverse ImpactNot a PI Client? What matters is the causal role that group membership plays in explaining disadvantageous differential treatment. They identify at least three reasons in support this theoretical conclusion. Hence, interference with individual rights based on generalizations is sometimes acceptable.
A violation of balance means that, among people who have the same outcome/label, those in one group are treated less favorably (assigned different probabilities) than those in the other. 22] Notice that this only captures direct discrimination. Society for Industrial and Organizational Psychology (2003). Fourthly, the use of ML algorithms may lead to discriminatory results because of the proxies chosen by the programmers. Gerards, J., Borgesius, F. Z. : Protected grounds and the system of non-discrimination law in the context of algorithmic decision-making and artificial intelligence. The models governing how our society functions in the future will need to be designed by groups which adequately reflect modern culture — or our society will suffer the consequences. In: Lippert-Rasmussen, Kasper (ed. )