Discrimination & Equity

In brief

Forms of bias that count as discrimination against social groups or individuals should be avoided, both from legal and ethical perspectives. Discrimination can be direct or indirect, intentional or unintentional.

More in Detail

Not all forms of bias (also known as statistical discrimination) are problematic. Here we use the normative sense of discrimination, where all forms of bias that count as discrimination are considered problematic and should be avoided. This discrimination can be direct (where a protected feature is intentionally used in the decision making procedure). In such a case explainability tools such as feature importance methods can help to detect whether a model’s decisions are based on the feature, in addition to the fairness metrics in the entry on . Often, however, protected features are purposely not included among the input variables, and so no direct discrimination will take place. Instead, indirect discrimination (where there is direct discrimination on features that strongly correlate with a protected feature, in such a way that users with a socially salient value of the feature – e.g. women – are worse off, cf. [1]) is the most common type of discrimination in machine learning systems. For ways to detect these cases of indirect discrimination, see the fairness metrics.

This type of indirect discrimination is often unintentional. Philosophical accounts thus disagree about the degree of intentionality that is required for bias to count as discrimination: mental state accounts [2] require systematic animosity or preferences towards a certain group. Such animosity need not be present among the designers of the system, though it may be part of the reason for the societal biases that filter through into the data. Other accounts [1, 2] opt for weaker notions of intentionality, where it is sufficient to enable the feature/group membership to play a role in the decision making procedure. This clearly allows for the (frequent) scenario where an algorithm has disparate impacts on groups even when this was not the result of preferences/animosities of the developers. Yet even then not all types of bias are considered normatively problematic: a statistical bias that negatively impacts smokers is not clearly a case of discrimination. So when is a bias a case of discrimination?

An influential point of departure is the idea that biases should not be on features outside of people’s control [4, 5]. This might explain why the paradigmatic cases of discrimination is when there is disparate treatment based on gender, race, or ethnicity (cf. the entry Grounds of Discrimination), as we cannot choose these. However, there are more features beyond our control, as illustrated by the ‘other people’s choice principle’ [4]: statistical patterns resulting from other people’s choices are not in our control either, and thus may lead to discrimination. Consequently, charging higher premiums to a buyer of a red car because on average drivers of red cars cause more accidents may be seen as problematic. It violates the other people’s choice principle, as a buyer has no control over the driving of other car owners. On the other hand, charging higher premiums to smokers does not violate this principle, since smoking is a direct cause of higher health risks. In practice, however, it is difficult to draw a clear border, as e.g. socio-economic status impacts people’s choices. Instead, some authors [2] suggest to consider notions of Justice as guiding the distribution of benefits and burdens resulting from the use of AI. For example, luck egalitarianism would consider it discriminatory to uphold biases which reflect factors of luck. Still, while the exact confines of normatively problematic discrimination are difficult to define, it is clear that gender, race, etc. are protected features and that such discrimination needs to be detected and tackled.

Following the egalitarian principles of [7, 8], some authors address fairness from a multi-agent perspective [9, 10] in automated decision making. Taking a welfare perspective [11] propose a family of welfare based measures that can be integrated together with other fairness and performance constraints. Following the same tracks, [12] considered the temporal/sequential dimension and addressed fairness in the context of Markov decision processes and reinforcement learning. Such multi-objective and welfare approaches not only enforce human intervention and social criteria to prevent unfair outcomes for some users or stakeholders, but could be adapted to ensure equity and fair trade-off between privilege and unprivileged groups.

Bibliography

1(1,2)

Kasper Lippert-Rasmussen. Born free and equal?: A philosophical inquiry into the nature of discrimination. Oxford University Press, 2013.

2

Thomas M Scanlon. Moral dimensions. In Moral Dimensions. Harvard University Press, 2009.

3

Michele Loi and Markus Christen. Choosing how to discriminate: navigating ethical trade-offs in fair algorithmic design for the insurance sector. Philosophy & Technology, 34(4):967–992, 2021.

4(1,2)

Kasper Lippert-Rasmussen. Nothing personal: on statistical discrimination. Journal of Political Philosophy, 15(4):385–403, 2007.

5

Daniel E Palmer. Insurance, risk assessment and fairness: an ethical analysis. In Insurance ethics for a more ethical world. Emerald Group Publishing Limited, 2007.

6

Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt. 'it's reducing a human being to a percentage': perceptions of justice in algorithmic decisions. In CHI, 377. ACM, 2018.

7

John Rawls. A theory of justice. Harvard university press, 2009.

8

Hervé Moulin. Fair division and collective welfare. MIT press, 2004.

9

Steven de Jong, Karl Tuyls, and Katja Verbeeck. Fairness in multi-agent systems. Knowl. Eng. Rev., 23(2):153–180, 2008.

10

Jianye Hao and Ho-fung Leung. Fairness in Cooperative Multiagent Systems, pages 27–70. Springer Berlin Heidelberg, Berlin, Heidelberg, 2016.

11

Hoda Heidari, Claudio Ferrari, Krishna P. Gummadi, and Andreas Krause. Fairness behind a veil of ignorance: A welfare analysis for automated decision making. In NeurIPS, 1273–1283. 2018.

12

Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, and Aaron Roth. Fairness in reinforcement learning. In ICML, volume 70 of Proceedings of Machine Learning Research, 1617–1626. PMLR, 2017.

This entry was written by Stefan Buijsman and Miguel Couceiro.