mlsanityData to 7 Oct 2026

BeFair: Addressing Fairness in the Banking Sector

arXiv 2102.02137 ↗First posted 3 Feb 2021cs.LG cs.CYcited by 8

Not on the map. Why?

Abstract (arXiv)

Algorithmic bias mitigation has been one of the most difficult conundrums for the data science community and Machine Learning (ML) experts. Over several years, there have appeared enormous efforts in the field of fairness in ML. Despite the progress toward identifying biases and designing fair algorithms, translating them into the industry remains a major challenge. In this paper, we present the initial results of an industrial open innovation project in the banking sector: we propose a general roadmap for fairness in ML and the implementation of a toolkit called BeFair that helps to identify and mitigate bias. Results show that training a model without explicit constraints may lead to bias exacerbation in the predictions.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 25 of its 38 references that are in the map. First posted on arXiv 3 Feb 2021.

Builds on

25 of 38 references.

References · 25

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2017Counterfactual Fairnesscited by 802
  4. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  7. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  8. 2018A Reductions Approach to Fair Classificationcited by 585
  9. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  10. 2017On Fairness and Calibrationcited by 406
  11. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  12. 2015The Variational Fair Autoencodercited by 223
13 more
  1. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  2. 2020Fairness in Machine Learningcited by 194
  3. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  4. 2018Path-Specific Counterfactual Fairnesscited by 179
  5. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  6. 2018The Frontiers of Fairness in Machine Learningcited by 139
  7. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  8. 201850 Years of Test (Un)fairness: Lessons for Machine Learningcited by 101
  9. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  10. 2018Bias Mitigation Post-processing for Individual and Group Fairnesscited by 72
  11. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  12. 2019Fairness Assessment for Artificial Intelligence in Financial Industrycited by 7
  13. 2018Avoiding Disparity Amplification under Different Worldviewscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 3; 2025: 1; 2026: 1.

8 papers.

  1. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  2. 2021Counterfactual Explanations as Interventions in Latent Spacecited by 18
  3. 2024Time Can Invalidate Algorithmic Recoursecited by 8
  4. 2024FairQuant: Certifying and Quantifying Fairness of Deep Neural Networkscited by 4
  5. 2024Mapping the Potential of Explainable AI for Fairness Along the AI Lifecyclecited by 1
  6. 2026Analyzing Fairness of Neural Network Prediction via Counterfactual Dataset Generationcited by 1
  7. 2022Investigating Bias with a Synthetic Data Generator: Empirical Evidence and Philosophical Interpretationcited by 0
  8. 2025flowengineR: A Modular and Extensible Framework for Fair and Reproducible Workflow Design in Rcited by 0