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@string{ACM = {Association for Computing Machinery,}}

@inproceedings{vigano2022people,
abbr={ACM},
author = {Vigan{\`o}, Eleonora and Hertweck, Corinna and <NAME> and <NAME>},
title = {People Are Not Coins: Morally Distinct Types of Predictions Necessitate Different Fairness Constraints},
year = {2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.2139/ssrn.3857889},
doi = {10.1145/3442188.3445936},
abstract = {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.},
booktitle = {Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency  (forthcoming)},
keywords = {fairness metrics, algorithmic decision making, fair prediction, moral principles},
location = {Seoul, Korea},
series = {FAccT '22},
ssrn={3857889},
}

@inproceedings{hertweck2021gradual,
  abbr={AAAI},
  author={<NAME> and <NAME>},
  title={Gradual (In)Compatibility of Fairness Criteria}, 
  year={2022},
  publisher={Association for the Advancement of Artificial Intelligence},
  address = {Palo Alto, California, USA},
  abstract={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.},
  booktitle = {Proceedings of the 36th AAAI Conference on Artificial Intelligence (forthcoming)},
  location = {Virtual Event, Canada},
  series = {AAAI '22},
  arxiv={2109.04399},
  poster = {aaai_poster.pdf},
  award = {Oral Presentation},
  code={https://github.com/hcorinna/gradual-compatibility}
}

@inproceedings{hertweck2021systematic,
  abbr={IEEE},
  author = {<NAME> and <NAME>},
  title = {A Systematic Approach to Group Fairness in Automated Decision Making},
  year = {2021},
  publisher = {Institute of Electrical and Electronics Engineers},
  abstract = {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.},
  booktitle = {2021 8th Swiss Conference on Data Science (SDS)},
  numpages = {6},
  keywords = {algorithmic fairness, group fairness, statistical parity, independence, separation, sufficiency},
  location = {Lucerne, Switzerland},
  url = {https://doi.org/10.1109/SDS51136.2021.00008},
  doi = {10.1109/SDS51136.2021.00008},
  series = {SDS '21},
  award = {Best Paper in Ethical, Legal, and Social Issues (ELSI) of Data Science},
  arxiv = {2109.04230}
}

@inproceedings{hertweck2021moral,
abbr={ACM},
author = {<NAME> and Heitz, <NAME> <NAME>},
title = {On the Moral Justification of Statistical Parity},
year = {2021},
isbn = {9781450383097},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3442188.3445936},
doi = {10.1145/3442188.3445936},
abstract = {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.},
booktitle = {Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency},
pages = {747–757},
numpages = {11},
keywords = {bias, fairness, statistical parity, distributive justice, independence},
location = {Virtual Event, Canada},
series = {FAccT '21},
award = {Best Student Paper},
arxiv = {2011.02079},
poster = {facct_poster.pdf}
}