A United States Fair Lending Perspective on Machine Learning

Hall, Patrick; Cox, Benjamin; Dickerson, Steven; Kannan, Arjun Ravi; Kulkarni, Raghu; Schmidt, Nicholas

doi:10.3389/frai.2021.695301

Cited by 12 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our work also links to ongoing regulatory initiatives, such as the recent European Commission proposal for Regulation on Artificial Intelligence, by providing a mechanism to provide meaningful interpretation to customers, regulators and investors. Similarly, US lending requirements relating to discrimination, regulatory compliance and fair lending provide a further motivation for the use of XAI in consumer financial services [Hall et al, 2021]. The possibility for divergent interpretations emerging from the common XAI approaches applied here highlights that there is scope for further research to identify suitable interpretation objectives and unravel discriminatory model outcomes.…”

Section: Introductionmentioning

confidence: 90%

Distilling a Disruptive Disintermediary’s Data: Interpretable Machine-Learning Explanations for LendingClub Customers

Conlon

Kearney

2023

FinTech Research and Applications

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 90%

Distilling a Disruptive Disintermediary’s Data: Interpretable Machine-Learning Explanations for LendingClub Customers

Conlon

Kearney

2023

FinTech Research and Applications

View full text Add to dashboard Cite

“…Remark 2 Given the regulatory constraints, the approaches of Feldman et al (2015) and Gordaliza et al (2019) would not be permitted in financial institutions that extend credit because a) the protected attribute cannot be used in training or prediction, and b) introducing randomness into the input dataset is prohibited; for details see (Hall et al 2021). To take into account the regulatory constraints and practical applications, in our companion paper (Miroshnikov et al 2021b) we propose a post-processing approach that relies on the fairness interpretability framework presented in the current article.…”

Section: Classifier Bias Mitigation Via Repaired Datasetsmentioning

confidence: 99%

Wasserstein-based fairness interpretability framework for machine learning models

Miroshnikov¹,

Kotsiopoulos²,

Franks³

et al. 2022

Mach Learn

Self Cite

View full text Add to dashboard Cite

The objective of this article is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a distribution. In our work, we measure the model bias across sub-population distributions in the model output using the Wasserstein metric. To properly quantify the contributions of predictors, we take into account favorability of both the model and predictors with respect to the non-protected class. The quantification is accomplished by the use of transport theory, which gives rise to the decomposition of the model bias and bias explanations to positive and negative contributions. To gain more insight into the role of favorability and allow for additivity of bias explanations, we adapt techniques from cooperative game theory.

show abstract

“…It can evolve, for example, through the limited contexts in which a system is used, in which case there is no opportunity to generalize it to other contexts. For a United States Fair Lending Perspective on Machine Learning (see Hall et al, 2021 ).…”

Section: Fairness and Biasmentioning

confidence: 99%

Financial Risk Management and Explainable, Trustworthy, Responsible AI

Fritz-Morgenthal¹,

Hein²,

Papenbrock³

2022

Front. Artif. Intell.

View full text Add to dashboard Cite

This perspective paper is based on several sessions by the members of the Round Table AI at FIRM1, with input from a number of external and international speakers. Its particular focus lies on the management of the model risk of productive models in banks and other financial institutions. The models in view range from simple rules-based approaches to Artificial Intelligence (AI) or Machine learning (ML) models with a high level of sophistication. The typical applications of those models are related to predictions and decision making around the value chain of credit risk (including accounting side under IFRS9 or related national GAAP approaches), insurance risk or other financial risk types. We expect more models of higher complexity in the space of anti-money laundering, fraud detection and transaction monitoring as well as a rise of AI/ML models as alternatives to current methods in solving some of the more intricate stochastic differential equations needed for the pricing and/or valuation of derivatives. The same type of model is also successful in areas unrelated to risk management, such as sales optimization, customer lifetime value considerations, robo-advisory, and other fields of applications. The paper refers to recent related publications from central banks, financial supervisors and regulators as well as other relevant sources and working groups. It aims to give practical advice for establishing a risk-based governance and testing framework for the mentioned model types and discusses the use of recent technologies, approaches, and platforms to support the establishment of responsible, trustworthy, explainable, auditable, and manageable AI/ML in production. In view of the recent EU publication on AI, also referred to as the EU Artificial Intelligence Act (AIA), we also see a certain added value for this paper as an instigator of further thinking outside of the financial services sector, in particular where “High Risk” models according to the mentioned EU consultation are concerned.

show abstract

A United States Fair Lending Perspective on Machine Learning

Cited by 12 publications

References 18 publications

Distilling a Disruptive Disintermediary’s Data: Interpretable Machine-Learning Explanations for LendingClub Customers

Distilling a Disruptive Disintermediary’s Data: Interpretable Machine-Learning Explanations for LendingClub Customers

Wasserstein-based fairness interpretability framework for machine learning models

Financial Risk Management and Explainable, Trustworthy, Responsible AI

Contact Info

Product

Resources

About