Fair lending needs explainable models for responsible recommendation
Not on the map. Why?
Abstract (arXiv)
The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Sep 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 4; 2023: 2; 2024: 0; 2025: 2; 2026: 1.
12 papers.
- 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanationscited by 40
- 2021Counterfactual Explanations for Arbitrary Regression Modelscited by 22
- 2023Counterfactual Learning on Graphs: A Surveycited by 22
- 2022Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairnesscited by 13
- 2022Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivitycited by 8
- 2025Counterfactual Explanations for Model Ensembles Using Entropic Risk Measurescited by 5
- 2021Seven challenges for harmonizing explainability requirementscited by 4
- 2022p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representationscited by 2
- 2023Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness settingcited by 2
- 2021Ontology drift is a challenge for explainable data governancecited by 0
- 2025Applications and Challenges of Fairness APIs in Machine Learning Softwarecited by 0
- 2026Prediction-Intervention Games and Invariant Setscited by 0
- 2026Prediction-Intervention Games and Invariant Sets16 May 2026
- 2025Applications and Challenges of Fairness APIs in Machine Learning Software22 Aug 2025
- 2025Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures11 Mar 2025
- 2023Counterfactual Learning on Graphs: A Survey3 Apr 2023
- 2023Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting16 Feb 2023
- 2022Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity3 Nov 2022
- 2022p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations25 Jul 2022
- 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations15 May 2022
- 2022Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness18 Apr 2022
- 2021Ontology drift is a challenge for explainable data governance11 Aug 2021
- 2021Seven challenges for harmonizing explainability requirements11 Aug 2021
- 2021Counterfactual Explanations for Arbitrary Regression Models29 Jun 2021