Reject inference methods in credit scoring

Ehrhardt, Adrien; Biernacki, Christophe; Vandewalle, Vincent; Heinrich, Philippe; Beben, Sébastien

doi:10.1080/02664763.2021.1929090

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…3 Bias management layer-understanding bias: challenges, policy advices, and best practices. In parenthesis, references to the subsections where they are discussed know whether an applicant with denied credit would have repaid the credit if granted, a sample selection bias problem tackled by reject inference in credit scoring (Ehrhardt et al, 2021). An idea close to reject inference has been considered in (Ji et al, 2020) for group fairness.…”

Section: The Ground Truth Is Biasedmentioning

confidence: 99%

Policy advice and best practices on bias and fairness in AI

Alvarez,

Colmenarejo,

Elobaid

et al. 2024

Ethics Inf Technol

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The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace, making it difficult for novel researchers and practitioners to have a bird’s-eye view picture of the field. In particular, many policy initiatives, standards, and best practices in fair-AI have been proposed for setting principles, procedures, and knowledge bases to guide and operationalize the management of bias and fairness. The first objective of this paper is to concisely survey the state-of-the-art of fair-AI methods and resources, and the main policies on bias in AI, with the aim of providing such a bird’s-eye guidance for both researchers and practitioners. The second objective of the paper is to contribute to the policy advice and best practices state-of-the-art by leveraging from the results of the NoBIAS research project. We present and discuss a few relevant topics organized around the NoBIAS architecture, which is made up of a Legal Layer, focusing on the European Union context, and a Bias Management Layer, focusing on understanding, mitigating, and accounting for bias.

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Section: The Ground Truth Is Biasedmentioning

confidence: 99%

Policy advice and best practices on bias and fairness in AI

Alvarez,

Colmenarejo,

Elobaid

et al. 2024

Ethics Inf Technol

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“…However, we do not know whether or not a defendant who was not released would have recidivated in case she/he would have been released. Similarly, we do not know whether an applicant with denied credit would have repayed the credit if granted, a sample selection bias problem tackled by reject inference in credit scoring (Ehrhardt et al 2021). An idea close to reject inference has been considered in (Ji, Smyth, and Steyvers 2020) for group fairness.…”

Section: Some Issues With Fair-aimentioning

confidence: 99%

Can We Trust Fair-AI?

Ruggieri

Álvarez

Pugnana

et al. 2023

AAAI

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There is a fast-growing literature in addressing the fairness of AI models (fair-AI), with a continuous stream of new conceptual frameworks, methods, and tools. How much can we trust them? How much do they actually impact society? We take a critical focus on fair-AI and survey issues, simplifications, and mistakes that researchers and practitioners often underestimate, which in turn can undermine the trust on fair-AI and limit its contribution to society. In particular, we discuss the hyper-focus on fairness metrics and on optimizing their average performances. We instantiate this observation by discussing the Yule's effect of fair-AI tools: being fair on average does not imply being fair in contexts that matter. We conclude that the use of fair-AI methods should be complemented with the design, development, and verification practices that are commonly summarized under the umbrella of trustworthy AI.

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“…This causes the missing-not-at-random bias in data [9] [10] [11] for machine learning models. Some reject inference approaches are accordingly proposed [8] [14] [15].…”

Section: Reject Inferencementioning

confidence: 99%

RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring

Liu

Luo

et al. 2022

IEEE Trans. Knowl. Data Eng.

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In financial credit scoring, loan applications may be approved or rejected. We can only observe default/non-default labels for approved samples but have no observations for rejected samples, which leads to missing-not-at-random selection bias. Machine learning models trained on such biased data are inevitably unreliable. In this work, we find that the default/non-default classification task and the rejection/approval classification task are highly correlated, according to both real-world data study and theoretical analysis. Consequently, the learning of default/non-default can benefit from rejection/approval. Accordingly, we for the first time propose to model the biased credit scoring data with Multi-Task Learning (MTL). Specifically, we propose a novel Reject-aware Multi-Task Network (RMT-Net), which learns the task weights that control the information sharing from the rejection/approval task to the default/non-default task by a gating network based on rejection probabilities. RMT-Net leverages the relation between the two tasks that the larger the rejection probability, the more the default/non-default task needs to learn from the rejection/approval task. Furthermore, we extend RMT-Net to RMT-Net++ for modeling scenarios with multiple rejection/approval strategies. Extensive experiments are conducted on several datasets, and strongly verifies the effectiveness of RMT-Net on both approved and rejected samples. In addition, RMT-Net++ further improves RMT-Net's performances.

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Reject inference methods in credit scoring

Cited by 12 publications

References 23 publications

Policy advice and best practices on bias and fairness in AI

Policy advice and best practices on bias and fairness in AI

Can We Trust Fair-AI?

RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring

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