Debiasing Credit Scoring using Evolutionary Algorithms
- URL: http://arxiv.org/abs/2110.12838v1
- Date: Mon, 25 Oct 2021 12:09:10 GMT
- Title: Debiasing Credit Scoring using Evolutionary Algorithms
- Authors: Nigel Kingsman
- Abstract summary: This paper investigates the application of machine learning when training a credit decision model over real, publicly available data.
We use the term "bias objective" to describe the requirement that a trained model displays discriminatory bias against a given groups of individuals.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: This paper investigates the application of machine learning when training a
credit decision model over real, publicly available data whilst accounting for
"bias objectives". We use the term "bias objective" to describe the requirement
that a trained model displays discriminatory bias against a given groups of
individuals that doesn't exceed a prescribed level, where such level can be
zero. This research presents an empirical study examining the tension between
competing model training objectives which in all cases include one or more bias
objectives.
This work is motivated by the observation that the parties associated with
creditworthiness models have requirements that can not certainly be fully met
simultaneously. The research herein seeks to highlight the impracticality of
satisfying all parties' objectives, demonstrating the need for "trade-offs" to
be made. The results and conclusions presented by this paper are of particular
importance for all stakeholders within the credit scoring industry that rely
upon artificial intelligence (AI) models as part of the decision-making process
when determining the creditworthiness of individuals. This paper provides an
exposition of the difficulty of training AI models that are able to
simultaneously satisfy multiple bias objectives whilst maintaining acceptable
levels of accuracy. Stakeholders should be aware of this difficulty and should
acknowledge that some degree of discriminatory bias, across a number of
protected characteristics and formulations of bias, cannot be avoided.
Related papers
- Investigating Implicit Bias in Large Language Models: A Large-Scale Study of Over 50 LLMs [0.0]
Large Language Models (LLMs) are being adopted across a wide range of tasks.
Recent research indicates that LLMs can harbor implicit biases even when they pass explicit bias evaluations.
This study highlights that newer or larger language models do not automatically exhibit reduced bias.
arXiv Detail & Related papers (2024-10-13T03:43:18Z) - DualFair: Fair Representation Learning at Both Group and Individual
Levels via Contrastive Self-supervision [73.80009454050858]
This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations.
Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
arXiv Detail & Related papers (2023-03-15T07:13:54Z) - In Search of Insights, Not Magic Bullets: Towards Demystification of the
Model Selection Dilemma in Heterogeneous Treatment Effect Estimation [92.51773744318119]
This paper empirically investigates the strengths and weaknesses of different model selection criteria.
We highlight that there is a complex interplay between selection strategies, candidate estimators and the data used for comparing them.
arXiv Detail & Related papers (2023-02-06T16:55:37Z) - Exploring the Trade-off between Plausibility, Change Intensity and
Adversarial Power in Counterfactual Explanations using Multi-objective
Optimization [73.89239820192894]
We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances.
We present a novel framework for the generation of counterfactual examples.
arXiv Detail & Related papers (2022-05-20T15:02:53Z) - SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles [50.90773979394264]
This paper studies a model that protects the privacy of individuals' sensitive information while also allowing it to learn non-discriminatory predictors.
A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model.
arXiv Detail & Related papers (2022-04-11T14:42:54Z) - Distraction is All You Need for Fairness [0.0]
We propose a strategy for training deep learning models called the Distraction module.
This method can be theoretically proven effective in controlling bias from affecting the classification results.
We demonstrate the potency of the proposed method by testing it on UCI Adult and Heritage Health datasets.
arXiv Detail & Related papers (2022-03-15T01:46:55Z) - Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity [10.144058870887061]
We argue that individuals can be harmed when one predictor is chosen ad hoc from a group of equally well performing models.
Our findings suggest that such unfairness can be readily found in real life and it may be difficult to mitigate by technical means alone.
arXiv Detail & Related papers (2022-03-14T14:33:39Z) - Measuring Fairness Under Unawareness of Sensitive Attributes: A
Quantification-Based Approach [131.20444904674494]
We tackle the problem of measuring group fairness under unawareness of sensitive attributes.
We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
arXiv Detail & Related papers (2021-09-17T13:45:46Z) - Beyond Individualized Recourse: Interpretable and Interactive Summaries
of Actionable Recourses [14.626432428431594]
We propose a novel model framework called Actionable Recourse agnostic (AReS) to construct global counterfactual explanations.
We formulate a novel objective which simultaneously optimize for correctness of the recourses and interpretability of the explanations.
Our framework can provide decision makers with a comprehensive overview of recourses corresponding to any black box model.
arXiv Detail & Related papers (2020-09-15T15:14:08Z) - Plausible Counterfactuals: Auditing Deep Learning Classifiers with
Realistic Adversarial Examples [84.8370546614042]
Black-box nature of Deep Learning models has posed unanswered questions about what they learn from data.
Generative Adversarial Network (GAN) and multi-objectives are used to furnish a plausible attack to the audited model.
Its utility is showcased within a human face classification task, unveiling the enormous potential of the proposed framework.
arXiv Detail & Related papers (2020-03-25T11:08:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.