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As argued in this section, we can fail to treat someone as an individual without grounding such judgement in an identity shared by a given social group. The classifier estimates the probability that a given instance belongs to. 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. This points to two considerations about wrongful generalizations. For example, Kamiran et al. Caliskan, A., Bryson, J. J., & Narayanan, A. 104(3), 671–732 (2016). It simply gives predictors maximizing a predefined outcome. Bias is to fairness as discrimination is to control. This may not be a problem, however. In principle, sensitive data like race or gender could be used to maximize the inclusiveness of algorithmic decisions and could even correct human biases. The second is group fairness, which opposes any differences in treatment between members of one group and the broader population. This means predictive bias is present. Algorithms may provide useful inputs, but they require the human competence to assess and validate these inputs.
CHI Proceeding, 1–14. In these cases, an algorithm is used to provide predictions about an individual based on observed correlations within a pre-given dataset. How To Define Fairness & Reduce Bias in AI. Notice that Eidelson's position is slightly broader than Moreau's approach but can capture its intuitions. Yet, one may wonder if this approach is not overly broad.
For him, discrimination is wrongful because it fails to treat individuals as unique persons; in other words, he argues that anti-discrimination laws aim to ensure that all persons are equally respected as autonomous agents [24]. Bias is to Fairness as Discrimination is to. Does chris rock daughter's have sickle cell? However, many legal challenges surround the notion of indirect discrimination and how to effectively protect people from it. 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.
Indirect discrimination is 'secondary', in this sense, because it comes about because of, and after, widespread acts of direct discrimination. A common notion of fairness distinguishes direct discrimination and indirect discrimination. One of the features is protected (e. g., gender, race), and it separates the population into several non-overlapping groups (e. g., GroupA and. 2012) for more discussions on measuring different types of discrimination in IF-THEN rules. This is particularly concerning when you consider the influence AI is already exerting over our lives. Strandburg, K. : Rulemaking and inscrutable automated decision tools. While situation testing focuses on assessing the outcomes of a model, its results can be helpful in revealing biases in the starting data. Bias is to fairness as discrimination is to justice. Second, as we discuss throughout, it raises urgent questions concerning discrimination. 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]. Adebayo, J., & Kagal, L. (2016). In the same vein, Kleinberg et al.
Hence, interference with individual rights based on generalizations is sometimes acceptable. This threshold may be more or less demanding depending on what the rights affected by the decision are, as well as the social objective(s) pursued by the measure. 2017) demonstrates that maximizing predictive accuracy with a single threshold (that applies to both groups) typically violates fairness constraints. Sometimes, the measure of discrimination is mandated by law. Mich. 92, 2410–2455 (1994). Schauer, F. : Statistical (and Non-Statistical) Discrimination. ) The very purpose of predictive algorithms is to put us in algorithmic groups or categories on the basis of the data we produce or share with others. ● Mean difference — measures the absolute difference of the mean historical outcome values between the protected and general group. Is discrimination a bias. In general, a discrimination-aware prediction problem is formulated as a constrained optimization task, which aims to achieve highest accuracy possible, without violating fairness constraints. This underlines that using generalizations to decide how to treat a particular person can constitute a failure to treat persons as separate (individuated) moral agents and can thus be at odds with moral individualism [53].
2013) surveyed relevant measures of fairness or discrimination. Study on the human rights dimensions of automated data processing (2017). First, the training data can reflect prejudices and present them as valid cases to learn from. They argue that hierarchical societies are legitimate and use the example of China to argue that artificial intelligence will be useful to attain "higher communism" – the state where all machines take care of all menial labour, rendering humans free of using their time as they please – as long as the machines are properly subdued under our collective, human interests. AI’s fairness problem: understanding wrongful discrimination in the context of automated decision-making. 4 AI and wrongful discrimination. Maya Angelou's favorite color?
Ehrenfreund, M. The machines that could rid courtrooms of racism. 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. Relationship between Fairness and Predictive Performance. Introduction to Fairness, Bias, and Adverse Impact. Proposals here to show that algorithms can theoretically contribute to combatting discrimination, but we remain agnostic about whether they can realistically be implemented in practice. Addressing Algorithmic Bias. Moreover, we discuss Kleinberg et al. Supreme Court of Canada.. (1986).
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]. 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. 2017) extends their work and shows that, when base rates differ, calibration is compatible only with a substantially relaxed notion of balance, i. e., weighted sum of false positive and false negative rates is equal between the two groups, with at most one particular set of weights. For him, for there to be an instance of indirect discrimination, two conditions must obtain (among others): "it must be the case that (i) there has been, or presently exists, direct discrimination against the group being subjected to indirect discrimination and (ii) that the indirect discrimination is suitably related to these instances of direct discrimination" [39]. These terms (fairness, bias, and adverse impact) are often used with little regard to what they actually mean in the testing context. 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. The very nature of ML algorithms risks reverting to wrongful generalizations to judge particular cases [12, 48]. Calders, T., Kamiran, F., & Pechenizkiy, M. (2009). Respondents should also have similar prior exposure to the content being tested. 2016) discuss de-biasing technique to remove stereotypes in word embeddings learned from natural language.
They define a distance score for pairs of individuals, and the outcome difference between a pair of individuals is bounded by their distance. Calders et al, (2009) propose two methods of cleaning the training data: (1) flipping some labels, and (2) assign unique weight to each instance, with the objective of removing dependency between outcome labels and the protected attribute. 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. Footnote 10 As Kleinberg et al. Of the three proposals, Eidelson's seems to be the more promising to capture what is wrongful about algorithmic classifications.
Burrell, J. : How the machine "thinks": understanding opacity in machine learning algorithms. Given that ML algorithms are potentially harmful because they can compound and reproduce social inequalities, and that they rely on generalization disregarding individual autonomy, then their use should be strictly regulated. Let's keep in mind these concepts of bias and fairness as we move on to our final topic: adverse impact. Footnote 13 To address this question, two points are worth underlining. Interestingly, the question of explainability may not be raised in the same way in autocratic or hierarchical political regimes. For example, a personality test predicts performance, but is a stronger predictor for individuals under the age of 40 than it is for individuals over the age of 40. Yet, different routes can be taken to try to make a decision by a ML algorithm interpretable [26, 56, 65]. Therefore, the use of ML algorithms may be useful to gain in efficiency and accuracy in particular decision-making processes. Feldman, M., Friedler, S., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2014). The Marshall Project, August 4 (2015). Accordingly, the fact that some groups are not currently included in the list of protected grounds or are not (yet) socially salient is not a principled reason to exclude them from our conception of discrimination.
Second, as mentioned above, ML algorithms are massively inductive: they learn by being fed a large set of examples of what is spam, what is a good employee, etc.