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July 20, 2024, 5:04 pm

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This is a central concern here because it raises the question of whether algorithmic "discrimination" is closer to the actions of the racist or the paternalist. However, it speaks volume that the discussion of how ML algorithms can be used to impose collective values on individuals and to develop surveillance apparatus is conspicuously absent from their discussion of AI. This echoes the thought that indirect discrimination is secondary compared to directly discriminatory treatment. For instance, the four-fifths rule (Romei et al. To go back to an example introduced above, a model could assign great weight to the reputation of the college an applicant has graduated from. Introduction to Fairness, Bias, and Adverse Impact. They argue that statistical disparity only after conditioning on these attributes should be treated as actual discrimination (a. k. a conditional discrimination).

Bias Is To Fairness As Discrimination Is To...?

Moreover, Sunstein et al. Yet, it would be a different issue if Spotify used its users' data to choose who should be considered for a job interview. Yet, in practice, the use of algorithms can still be the source of wrongful discriminatory decisions based on at least three of their features: the data-mining process and the categorizations they rely on can reconduct human biases, their automaticity and predictive design can lead them to rely on wrongful generalizations, and their opaque nature is at odds with democratic requirements. Difference between discrimination and bias. This series of posts on Bias has been co-authored by Farhana Faruqe, doctoral student in the GWU Human-Technology Collaboration group. In this new issue of Opinions & Debates, Arthur Charpentier, a researcher specialised in issues related to the insurance sector and massive data, has carried out a comprehensive study in an attempt to answer the issues raised by the notions of discrimination, bias and equity in insurance. From there, they argue that anti-discrimination laws should be designed to recognize that the grounds of discrimination are open-ended and not restricted to socially salient groups.

Footnote 1 When compared to human decision-makers, ML algorithms could, at least theoretically, present certain advantages, especially when it comes to issues of discrimination. Therefore, the data-mining process and the categories used by predictive algorithms can convey biases and lead to discriminatory results which affect socially salient groups even if the algorithm itself, as a mathematical construct, is a priori neutral and only looks for correlations associated with a given outcome. Bias is to Fairness as Discrimination is to. This is conceptually similar to balance in classification. Many AI scientists are working on making algorithms more explainable and intelligible [41]. Engineering & Technology.

Difference Between Discrimination And Bias

Moreover, this account struggles with the idea that discrimination can be wrongful even when it involves groups that are not socially salient. Similarly, the prohibition of indirect discrimination is a way to ensure that apparently neutral rules, norms and measures do not further disadvantage historically marginalized groups, unless the rules, norms or measures are necessary to attain a socially valuable goal and that they do not infringe upon protected rights more than they need to [35, 39, 42]. Hajian, S., Domingo-Ferrer, J., & Martinez-Balleste, A. As Boonin [11] has pointed out, other types of generalization may be wrong even if they are not discriminatory. Other types of indirect group disadvantages may be unfair, but they would not be discriminatory for Lippert-Rasmussen. Some people in group A who would pay back the loan might be disadvantaged compared to the people in group B who might not pay back the loan. As mentioned, the fact that we do not know how Spotify's algorithm generates music recommendations hardly seems of significant normative concern. Bias is to fairness as discrimination is to...?. 2018a) proved that "an equity planner" with fairness goals should still build the same classifier as one would without fairness concerns, and adjust decision thresholds.

Community Guidelines. Zimmermann, A., and Lee-Stronach, C. Proceed with Caution. It is rather to argue that even if we grant that there are plausible advantages, automated decision-making procedures can nonetheless generate discriminatory results. This would be impossible if the ML algorithms did not have access to gender information. Kim, P. : Data-driven discrimination at work.

Bias Is To Fairness As Discrimination Is To Meaning

It is a measure of disparate impact. First, it could use this data to balance different objectives (like productivity and inclusion), and it could be possible to specify a certain threshold of inclusion. The use of predictive machine learning algorithms is increasingly common to guide or even take decisions in both public and private settings. 2017) or disparate mistreatment (Zafar et al. Fairness Through Awareness. What's more, the adopted definition may lead to disparate impact discrimination. A Reductions Approach to Fair Classification. AI’s fairness problem: understanding wrongful discrimination in the context of automated decision-making. This case is inspired, very roughly, by Griggs v. Duke Power [28]. Sunstein, C. : Governing by Algorithm? Consequently, the examples used can introduce biases in the algorithm itself.

One may compare the number or proportion of instances in each group classified as certain class. Noise: a flaw in human judgment. Second, as we discuss throughout, it raises urgent questions concerning discrimination. For example, imagine a cognitive ability test where males and females typically receive similar scores on the overall assessment, but there are certain questions on the test where DIF is present, and males are more likely to respond correctly. Eidelson defines discrimination with two conditions: "(Differential Treatment Condition) X treat Y less favorably in respect of W than X treats some actual or counterfactual other, Z, in respect of W; and (Explanatory Condition) a difference in how X regards Y P-wise and how X regards or would regard Z P-wise figures in the explanation of this differential treatment. " Requiring algorithmic audits, for instance, could be an effective way to tackle algorithmic indirect discrimination. One of the basic norms might well be a norm about respect, a norm violated by both the racist and the paternalist, but another might be a norm about fairness, or equality, or impartiality, or justice, a norm that might also be violated by the racist but not violated by the paternalist. Yeung, D., Khan, I., Kalra, N., and Osoba, O. Identifying systemic bias in the acquisition of machine learning decision aids for law enforcement applications. Bias is to fairness as discrimination is to claim. However, they are opaque and fundamentally unexplainable in the sense that we do not have a clearly identifiable chain of reasons detailing how ML algorithms reach their decisions. Emergence of Intelligent Machines: a series of talks on algorithmic fairness, biases, interpretability, etc. 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]. Another case against the requirement of statistical parity is discussed in Zliobaite et al. Measurement and Detection. A selection process violates the 4/5ths rule if the selection rate for the subgroup(s) is less than 4/5ths, or 80%, of the selection rate for the focal group.

Bias Is To Fairness As Discrimination Is To Claim

This could be included directly into the algorithmic process. It follows from Sect. NOVEMBER is the next to late month of the year. Valera, I. : Discrimination in algorithmic decision making. 5 Conclusion: three guidelines for regulating machine learning algorithms and their use. Neg class cannot be achieved simultaneously, unless under one of two trivial cases: (1) perfect prediction, or (2) equal base rates in two groups. 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. For example, when base rate (i. e., the actual proportion of.

Bechavod, Y., & Ligett, K. (2017). Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2011). This is necessary to be able to capture new cases of discriminatory treatment or impact. Importantly, this requirement holds for both public and (some) private decisions. If so, it may well be that algorithmic discrimination challenges how we understand the very notion of discrimination. In this paper, however, we show that this optimism is at best premature, and that extreme caution should be exercised by connecting studies on the potential impacts of ML algorithms with the philosophical literature on discrimination to delve into the question of under what conditions algorithmic discrimination is wrongful. To illustrate, imagine a company that requires a high school diploma to be promoted or hired to well-paid blue-collar positions. Pedreschi, D., Ruggieri, S., & Turini, F. A study of top-k measures for discrimination discovery. 35(2), 126–160 (2007). The consequence would be to mitigate the gender bias in the data. If everyone is subjected to an unexplainable algorithm in the same way, it may be unjust and undemocratic, but it is not an issue of discrimination per se: treating everyone equally badly may be wrong, but it does not amount to discrimination. Considerations on fairness-aware data mining. For demographic parity, the overall number of approved loans should be equal in both group A and group B regardless of a person belonging to a protected group.

Alternatively, the explainability requirement can ground an obligation to create or maintain a reason-giving capacity so that affected individuals can obtain the reasons justifying the decisions which affect them. Strasbourg: Council of Europe - Directorate General of Democracy, Strasbourg.. (2018). 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]. G. past sales levels—and managers' ratings. Consequently, tackling algorithmic discrimination demands to revisit our intuitive conception of what discrimination is. Chouldechova (2017) showed the existence of disparate impact using data from the COMPAS risk tool. These model outcomes are then compared to check for inherent discrimination in the decision-making process.
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