Socially acceptable and fair AI

2022

ACM

Is calibration a fairness requirement? An argument from the point of view of moral philosophy and decision theory.

Loi, Michele, and Heitz, Christoph

In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (forthcoming) (2022)

Abs

In this paper, we provide a moral analysis of two criteria of statistical fairness debated in the machine learning literature: 1) calibration between groups and 2) equality of false positive and false negative rates between groups. In our paper, we focus on moral arguments in support of either measure. The conflict between group calibration vs. false positive and false negative rate equality is one of the core issues in the debate about group fairness definitions among practitioners. For any thorough moral analysis, the meaning of the term fairness has to be made explicit and defined properly. For our paper, we equate fairness with (non-)discrimination, which is a legitimate understanding in the discussion about group fairness. More specifically, we equate it with prima facie wrongful discrimination in the sense this is used in Prof. Lippert-Rasmussen’s treatment of this definition. In this paper, we argue that a violation of group calibration may be unfair in some cases, but not unfair in others. This is in line with claims already advanced in the literature, that algorithmic fairness should be defined in a way that is sensitive to context. The most important practical implication is that arguments based on examples in which fairness requires between-group calibration, or equality in the false-positive/false-negative rates, do no generalize. For it may be that group calibration is a fairness requirement in one case, but not in another.
ACM

Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency

Baumann, Joachim, Hannák, Anikó, and Heitz, Christoph

In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (forthcoming) (2022)
ACM

People Are Not Coins: Morally Distinct Types of Predictions Necessitate Different Fairness Constraints

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

In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (forthcoming) (2022)

Abs SSRN

A recent paper (Hedden 2021) has argued that most of the group fairness constraints discussed in the machine learning literature are not necessary conditions for the fairness of predictions, and hence that there are no genuine fairness metrics. This is proven by discussing a special case of a fair prediction. In our paper, we show that Hedden’s argument does not hold for the most common kind of predictions used in data science, which are about people and based on data from similar people; we call these “human-group-based practices”. We argue that there is a morally salient distinction between human-group-based practices and those that are based on data of only one person, which we call “human-individual-based practices”. Thus, what may be a necessary condition for the fairness of human-group-based practices may not be a necessary condition for the fairness of human-individual-based practices, on which Hedden’s argument is based. Accordingly, the group fairness metrics discussed in the machine learning literature may still be relevant for most applications of prediction-based decision making.
IEEE

Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation

Baumann, Joachim, and Heitz, Christoph

In 2022 9th Swiss Conference on Data Science (SDS) (forthcoming) (2022)
AAAI

Gradual (In)Compatibility of Fairness Criteria

Hertweck, Corinna, and Räz, Tim

In Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022)

Abs arXiv Poster

Impossibility results show that important fairness measures (independence, separation, sufficiency) cannot be satisfied at the same time under reasonable assumptions. This paper explores whether we can satisfy and/or improve these fairness measures simultaneously to a certain degree. We introduce information-theoretic formulations of the fairness measures and define degrees of fairness based on these formulations. The information-theoretic formulations suggest unexplored theoretical relations between the three fairness measures. In the experimental part, we use the information-theoretic expressions as regularizers to obtain fairness-regularized predictors for three standard datasets. Our experiments show that a) fairness regularization directly increases fairness measures, in line with existing work, and b) some fairness regularizations indirectly increase other fairness measures, as suggested by our theoretical findings. This establishes that it is possible to increase the degree to which some fairness measures are satisfied at the same time – some fairness measures are gradually compatible.

2021

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.
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.
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.
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.