Publications

2021

1. People are Not Coins: A Reply to Hedden.

   Loi, Michele, Viganò, Eleonora, Hertweck, Corinna, and Heitz, Christoph

   SSRN (2021)

   Abs SSRN

   This paper is a reply to "On Statistical Criteria of Algorithmic Fairness," by Brian Hedden. We question the significance of arguing that many group fairness criteria discussed in the machine learning literature are not necessary conditions for the fairness of predictions or decisions based on them. We show that it may be true, in general, that F is not a necessary condition for the fairness of all predictions (or decisions based on them). And yet, compatibly with this, most predictions or decisions involving people could be unfair if they violate the statistical fairness constraint F.

2. IEEE

   A Systematic Approach to Group Fairness in Automated Decision Making

   Hertweck, Corinna, and Heitz, Christoph

   In 2021 8th Swiss Conference on Data Science (SDS) (2021)

   ELSI award

   Abs arXiv

   While the field of algorithmic fairness has brought forth many ways to measure and improve the fairness of machine learning models, these findings are still not widely used in practice. We suspect that one reason for this is that the field of algorithmic fairness came up with a lot of definitions of fairness, which are difficult to navigate. The goal of this paper is to provide data scientists with an accessible introduction to group fairness metrics and to give some insight into the philosophical reasoning for caring about these metrics. We will do this by considering in which sense socio-demographic groups are compared for making a statement on fairness.

3. Discrimination for the Sake of Fairness: Fairness by Design and Its Legal Framework

   Hoch, Holly, Hertweck, Corinna, Loi, Michele, and Tamò, Aurelia

   SSRN (2021)

   Abs SSRN

   As algorithms are increasingly enlisted to make critical determinations about human actors, the more frequently we see these algorithms appear in sensational headlines crying foul on discrimination. There is broad consensus among computer scientists working on this issue that such discrimination can only be avoided by intentionally collecting and consciously using sensitive information about demographic features like sex, gender, race, religion etc. Companies implementing such algorithms might, however, be wary of allowing algorithms access to such data as they fear legal repercussions, as the promoted standard has been to omit protected attributes, otherwise dubbed “fairness through unawareness”. This paper asks whether such wariness is justified in light of EU data protection and anti-discrimination laws. In order to answer this question, we introduce a specific case and analyze how EU law might apply when an algorithm accesses sensitive information to make fairer predictions. We review whether such measures constitute discrimination, and for who, arriving at different conclusions based on how we define the harm of discrimination and the groups we compare. Finding that several legal claims could arise regarding the use of sensitive information, we ultimately conclude that the proffered fairness measures would be considered a positive (or affirmative) action under EU law. As such, the appropriate use of sensitive information in order to increase the fairness of an algorithm is a positive action, and not per se prohibited by EU law.

4. ACM

   Group Fairness: Independence Revisited

   Räz, Tim

   In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (2021)

   Abs arXiv

   This paper critically examines arguments against independence, a measure of group fairness also known as statistical parity and as demographic parity. In recent discussions of fairness in computer science, some have maintained that independence is not a suitable measure of group fairness. This position is at least partially based on two influential papers (Dwork et al., 2012, Hardt et al., 2016) that provide arguments against independence. We revisit these arguments, and we find that the case against independence is rather weak. We also give arguments in favor of independence, showing that it plays a distinctive role in considerations of fairness. Finally, we discuss how to balance different fairness considerations.

5. ACM

   On the Moral Justification of Statistical Parity

   Hertweck, Corinna, Heitz, Christoph, and Loi, Michele

   In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (2021)

   Best Student Paper

   Abs arXiv Poster

   A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric from a moral perspective. When fairness metrics are proposed, they are typically argued for by highlighting their mathematical properties. Rarely are the moral assumptions beneath the metric explained. Our aim in this paper is to consider the moral aspects associated with the statistical fairness criterion of independence (statistical parity). To this end, we consider previous work, which discusses the two worldviews "What You See Is What You Get" (WYSIWYG) and "We’re All Equal" (WAE) and by doing so provides some guidance for clarifying the possible assumptions in the design of algorithms. We present an extension of this work, which centers on morality. The most natural moral extension is that independence needs to be fulfilled if and only if differences in predictive features (e.g. high school grades and standardized test scores are predictive of performance at university) between socio-demographic groups are caused by unjust social disparities or measurement errors. Through two counterexamples, we demonstrate that this extension is not universally true. This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features.