A Ranking Approach to Fair Classification

Schoeffer, Jakob; Kuehl, Niklas; Valera, Isabel

doi:10.1145/3460112.3471950

Cited by 8 publications

(9 citation statements)

References 20 publications

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“…Through extensive experiments on synthetic and real-world data, I have shown that the proposed method is fair in the sense that it (a) assigns the desirable outcome to the most qualified individuals, and (b) removes the effect of stereotypes in decision-making, thereby outperforming traditional classification algorithms. Finally, in [51], I have also shown theoretically that my method is consistent with the prominent individual fairness notion of FTA (see Section 2).…”

Section: Results and Contributions To Datesupporting

confidence: 69%

“…In fact, in many real-world settings, we only have access to data with imperfect labels, as the result of (potentially biased) human-made decisions. In [51], I propose a novel ranking-based decision system that does not learn to mimic biased decisions but (a) incorporates only useful information from historical decisions, and (b) accounts for unwanted correlation between sensitive (e.g., gender) and legitimate features. Specifically, I introduce a decision criterion based on weighted distances between individual data points and a so-called North Star, which represents the (potentially hypothetical) observation with the highest possible qualification towards a given outcome.…”

Section: Results and Contributions To Datementioning

confidence: 99%

“…Furthermore, error rate-based fairness notions-as well as the whole idea of optimizing for predictive performance in ADS-rely on the availability of ground-truth labels; for instance, whether or not an applicant actually paid back a loan. This is a strong assumption that is often not met in real-world decision-making scenarios [33,51]. Specifically, if ground-truth labels are not (or selectively) available [33], then maximizing for accuracy subject to fairness constraints seems counterintuitive and is, in fact, sub-optimal [26].…”

Section: Key Related Work and Research Gapsmentioning

confidence: 99%

“…Schoeffer et al [51] ACM COMPASS 2021 human fairness perceptions of ADS for four distinct post-hoc explanation styles. Their works suggest differences in effectiveness of individual explanation styles-however, they also note that there does not seem to be a single best approach to explaining automated decisions.…”

Section: Rq3mentioning

confidence: 99%

“…Next, regarding RQ3, I plan to empirically evaluate my technical artifact [51] with respect to people's perceptions and understanding. I hypothesize that people appreciate the "white-box" approachspecifically, the fact that legitimate features are monotonically related to the final outcome; for instance, that higher GRE scores will never decrease the chances of being admitted to grad school.…”

Section: Next Steps and Outlookmentioning

confidence: 99%

See 4 more Smart Citations

A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

Schoeffer

2022

CHI Conference on Human Factors in Computing Systems Extended Abstracts

Self Cite

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Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield unfair outcomes because their sanity is challenging to assess and calibrate in the first place-which is particularly worrisome for human decision-subjects. Based on this observation and building upon existing work, I aim to make the following three main contributions through my doctoral thesis: (a) understand how (potential) decision-subjects perceive algorithmic decisions (with varying degrees of transparency of the underlying ADS), as compared to similar decisions made by humans; (b) evaluate different tools for transparent decision-making with respect to their effectiveness in enabling people to appropriately assess the quality and fairness of ADS; and (c) develop human-understandable technical artifacts for fair automated decision-making. Over the course of the first half of my PhD program, I have already addressed substantial pieces of (a) and (c), whereas (b) will be the major focus of the second half. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); • Computing methodologies → Machine learning; • Information systems → Decision support systems.

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Section: Results and Contributions To Datesupporting

confidence: 69%

Section: Results and Contributions To Datementioning

confidence: 99%

Section: Key Related Work and Research Gapsmentioning

confidence: 99%

Section: Rq3mentioning

confidence: 99%

Section: Next Steps and Outlookmentioning

confidence: 99%

See 3 more Smart Citations

A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

Schoeffer

2022

CHI Conference on Human Factors in Computing Systems Extended Abstracts

Self Cite

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Algorithmic discrimination in the credit domain: what do we know about it?

2023

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The widespread usage of machine learning systems and econometric methods in the credit domain has transformed the decision-making process for evaluating loan applications. Automated analysis of credit applications diminishes the subjectivity of the decision-making process. On the other hand, since machine learning is based on past decisions recorded in the financial institutions’ datasets, the process very often consolidates existing bias and prejudice against groups defined by race, sex, sexual orientation, and other attributes. Therefore, the interest in identifying, preventing, and mitigating algorithmic discrimination has grown exponentially in many areas, such as Computer Science, Economics, Law, and Social Science. We conducted a comprehensive systematic literature review to understand (1) the research settings, including the discrimination theory foundation, the legal framework, and the applicable fairness metric; (2) the addressed issues and solutions; and (3) the open challenges for potential future research. We explored five sources: ACM Digital Library, Google Scholar, IEEE Digital Library, Springer Link, and Scopus. Following inclusion and exclusion criteria, we selected 78 papers written in English and published between 2017 and 2022. According to the meta-analysis of this literature survey, algorithmic discrimination has been addressed mainly by looking at the CS, Law, and Economics perspectives. There has been great interest in this topic in the financial area, especially the discrimination in providing access to the mortgage market and differential treatment (different fees, number of parcels, and interest rates). Most attention has been devoted to the potential discrimination due to bias in the dataset. Researchers are still only dealing with direct discrimination, addressed by algorithmic fairness, while indirect discrimination (structural discrimination) has not received the same attention.

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Metric learning for monotonic classification: turning the space up to the limits of monotonicity

Suárez,

González-Almagro,

García

et al. 2024

Appl Intell

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This paper presents, for the first time, a distance metric learning algorithm for monotonic classification. Monotonic datasets arise in many real-world applications, where there exist order relations in the input and output variables, and the outputs corresponding to ordered pairs of inputs are also expected to be ordered. Monotonic classification can be addressed through several distance-based classifiers that are able to respect the monotonicity constraints of the data. The performance of distance-based classifiers can be improved with the use of distance metric learning algorithms, which are able to find the distances that best represent the similarities among each pair of data samples. However, learning a distance for monotonic data has an additional drawback: the learned distance may negatively impact the monotonic constraints of the data. In our work, we propose a new model for learning distances that does not corrupt these constraints. This methodology will also be useful in identifying and discarding non-monotonic pairs of samples that may be present in the data due to noise. The experimental analysis conducted, supported by a Bayesian statistical testing, demonstrates that the distances obtained by the proposed method can enhance the performance of several distance-based classifiers in monotonic problems.

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A Ranking Approach to Fair Classification

Cited by 8 publications

References 20 publications

A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

Algorithmic discrimination in the credit domain: what do we know about it?

Metric learning for monotonic classification: turning the space up to the limits of monotonicity

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