Equalizing Credit Opportunity in Algorithms

Kumar, I. Elizabeth; Hines, Keegan E.; Dickerson, John P.

doi:10.1145/3514094.3534154

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…We assume that the policymaker is blind to each individual's group identity; they must set a universal 𝛽, so 𝛽 𝐴 = 𝛽 𝐷 . This assumption corresponds to legal standards such as disparate treatment law in mortgage lending, which prohibits using race, sex, nationality, and other protected characteristics as a reason for setting different approval rules [39,59].…”

Section: Model 31 Single Time Step Modelmentioning

confidence: 99%

Structural Interventions and the Dynamics of Inequality

Zhang,

Hosoi

2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

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Recent conversations in the algorithmic fairness literature have raised several concerns with standard conceptions of fairness. First, constraining predictive algorithms to satisfy fairness benchmarks may sometimes lead to non-optimal outcomes for disadvantaged groups. Second, technical interventions are often ineffective by themselves, especially when divorced from an understanding of structural processes that generate social inequality. Inspired by both these critiques, we construct a common decision-making model, using mortgage loans as a running example. We show that under some conditions, any choice of decision threshold will inevitably perpetuate existing disparities in financial stability unless one deviates from the Pareto optimal policy. This confirms the intuition that technical interventions, such as fairness constraints, often do not sufficiently address persistent underlying inequities. Then, we model the effects of three different types of interventions: (1) policy changes in the algorithm's decision threshold, and external changes to parameters that govern the downstream effects of late payment for (2) the whole population or (3) disadvantaged subgroups. We show how different interventions are recommended depending on the difficulty of enacting structural change upon external parameters and depending on the policymaker's preferences for equity or efficiency. Counterintuitively, we demonstrate that preferences for efficiency over equity may sometimes lead to recommendations for interventions that target the under-resourced group alone. Finally, we simulate the effects of interventions on a dataset that combines HMDA and Fannie Mae loan data. This research highlights the ways that structural inequality can be perpetuated by seemingly unbiased decision mechanisms, and it shows that in many situations, technical solutions must be paired with external, context-aware interventions to enact social change. CCS CONCEPTS• Applied computing → Sociology; • Human-centered computing → Collaborative and social computing design and evaluation methods; Social engineering (social sciences).

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Section: Model 31 Single Time Step Modelmentioning

confidence: 99%

Structural Interventions and the Dynamics of Inequality

Zhang,

Hosoi

2024

The 2024 ACM Conference on Fairness, Accountability, and Transparency

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“…The areas where the current research and application work are more focused are AI-based social infrastructure and management and business applications. Tables 13 and 14 summarize common applications and case studies with a comparison of the approaches and challenges, respectively, including education [273][274][275][276][277] health care [52,[278][279][280] criminal justice and sentencing [88,[281][282][283], hiring and recruiting [284][285][286], lending and credit decisions [287][288][289][290][291][292], online advertising [8,[293][294][295], customer service and chatbots [296][297][298][299][300][301][302][303].…”

Section: Ai Fairness In Practicementioning

confidence: 99%

“…Certain AI-driven credit scoring models have exhibited potential bias towards specific demographic groups [288,289]. Implementing bias mitigation techniques enhances model fairness, leading to more impartial lending determinations [290][291][292]. Looking ahead, future efforts should focus on integrating diverse data sources like rental histories or utility payments into credit assessments while maintaining fairness.…”

Section: Online Advertisingmentioning

confidence: 99%

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AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

Chen,

Wu,

Wang

2023

Applied Sciences

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This article provides a comprehensive overview of the fairness issues in artificial intelligence (AI) systems, delving into its background, definition, and development process. The article explores the fairness problem in AI through practical applications and current advances and focuses on bias analysis and fairness training as key research directions. The paper explains in detail the concept, implementation, characteristics, and use cases of each method. The paper explores strategies to reduce bias and improve fairness in AI systems, reviews challenges and solutions to real-world AI fairness applications, and proposes future research directions. In addition, this study provides an in-depth comparative analysis of the various approaches, utilizing cutting-edge research information to elucidate their different characteristics, strengths, and weaknesses. The results of the comparison provide guidance for future research. The paper concludes with an overview of existing challenges in practical applications and suggests priorities and solutions for future research. The conclusions provide insights for promoting fairness in AI systems. The information reviewed in this paper is drawn from reputable sources, including leading academic journals, prominent conference proceedings, and well-established online repositories dedicated to AI fairness. However, it is important to recognize that research nuances, sample sizes, and contextual factors may create limitations that affect the generalizability of the findings.

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“…Machine Learning (ML) models are increasingly utilized in critical decision-making applications, such as workforce recruiting [1], [2], justice risk assessments [3], [4], and credit risk prediction [5], [6]. Even though ML algorithms are not intentionally designed to incorporate bias, studies have shown that ML models not only reproduce existing biases in the training data [7] but also amplify them [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

FairGridSearch: A Framework to Compare Fairness-Enhancing Models

Ma,

Ermakova,

Fabian

2023

2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

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Machine learning models are increasingly used in critical decision-making applications. However, these models are susceptible to replicating or even amplifying bias present in real-world data. While there are various bias mitigation methods and base estimators in the literature, selecting the optimal model for a specific application remains challenging. This paper focuses on binary classification and proposes FairGridSearch, a novel framework for comparing fairness-enhancing models. FairGridSearch enables experimentation with different model parameter combinations and recommends the best one. The study applies FairGridSearch to three popular datasets (Adult, COMPAS, and German Credit) and analyzes the impacts of metric selection, base estimator choice, and classification threshold on model fairness. The results highlight the significance of selecting appropriate accuracy and fairness metrics for model evaluation. Additionally, different base estimators and classification threshold values affect the effectiveness of bias mitigation methods and fairness stability respectively, but the effects are not consistent across all datasets. Based on these findings, future research on fairness in machine learning should consider a broader range of factors when building fair models, going beyond bias mitigation methods alone.

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Equalizing Credit Opportunity in Algorithms

Cited by 9 publications

References 18 publications

Structural Interventions and the Dynamics of Inequality

Structural Interventions and the Dynamics of Inequality

AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

FairGridSearch: A Framework to Compare Fairness-Enhancing Models

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