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Article
Management
Pushkala Prasad
Summary: This paper discusses the concept of racial capitalism and its significance for Management and Organization Studies. Racial capitalism is a process of deriving socio-economic value from non-white racial identity groups, which has influenced the development of capitalism for over 500 years. Drawing on the works of scholars such as W.E.B. Du Bois and Bourdieu, the paper argues that whiteness operates as symbolic capital and status property in the market, perpetuating economic inequalities based on race globally. The paper demonstrates how the additional value placed on whiteness creates a shadowland of divided labor markets, colorism, and transnational patterns of exploitation that systematically disadvantage people of color.
Article
Business
Rachel Atkins et al.
Summary: Our research found that Black-owned businesses received PPP loans that were approximately 50% lower than loans received by White-owned businesses with similar characteristics. However, this disparity decreased over time as non-bank lenders like fintechs were allowed to participate, and the differences were slightly smaller in areas with more bank branches. These results align with previous studies demonstrating lending discrimination by commercial banks against Black borrowers and suggest that increasing access and competition among lenders can help reduce disparities.
SMALL BUSINESS ECONOMICS
(2022)
Article
Management
Nathan Kallus et al.
Summary: With the increasing impact of algorithmic decisions on people's lives, it is important to examine their fairness and the potential disparate impacts they may have on different groups. This paper focuses on the challenge of assessing disparate impacts in practice when the membership of protected classes is often unobserved in the data. The study proposes the use of an auxiliary data set, such as the U.S. census, to predict the protected class using proxy variables like surname and geolocation. The research provides exact characterizations of the possible true disparities consistent with the data, and offers optimization-based algorithms and statistical tools for computing and visualizing these disparities, as well as assessing sampling uncertainty.
MANAGEMENT SCIENCE
(2022)
Article
Business, Finance
Andreas Fuster et al.
Summary: Innovations in statistical technology, particularly in credit-screening, have raised concerns about the distributional impacts across race categories. Using traditional and machine learning models on U.S. mortgage data, it was found that Black and Hispanic borrowers did not benefit proportionately from machine learning, leading to increased disparities in interest rates. This disparity was primarily attributed to the greater flexibility of machine learning models.
JOURNAL OF FINANCE
(2022)
Article
Business, Finance
Robert Bartlett et al.
Summary: U.S. fair-lending law prohibits lenders from making credit determinations that disparately affect minority borrowers based on characteristics unrelated to creditworthiness. Research shows that risk-equivalent Lat-inx/Black borrowers pay significantly higher interest rates in high-minority-share neighborhoods, resulting in over $450 million yearly loss for minority borrowers. FinTech lenders' rate disparities were similar to non-Fintech lenders for GSE mortgages, but lower for FHA mortgages issued in 2009-2015 and FHA refi mortgages issued in 2018-2019.
JOURNAL OF FINANCIAL ECONOMICS
(2022)
Article
Business
Samuel Anokye Nyarko
Summary: This study investigates the influence of gender discrimination on microfinance outreach to women and tests the moderating effect of an international founder. The results show that gender discrimination negatively impacts microfinance outreach to women, but that the negative effect is mitigated by having an international founder.
INTERNATIONAL BUSINESS REVIEW
(2022)
Article
Management
Nikita Kozodoi et al.
Summary: This paper explores the implementation of fair machine learning in credit scoring, revisiting statistical fairness criteria, cataloging algorithmic options, and balancing the trade-off between profit and fairness. Empirical results show that multiple fairness criteria can be satisfied simultaneously and fair in-processors can reduce algorithmic discrimination at a reasonable cost.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Management
Maxime C. Cohen et al.
Summary: This paper examines the problem of setting prices for different groups under fairness constraints in the context of price discrimination strategies. The study finds that satisfying multiple fairness constraints is impossible even under simple settings. Analyzing the pricing strategy of a profit-maximizing seller, the authors show that moderate price fairness can increase social welfare, but excessive price fairness may result in lower welfare compared to no fairness. Additionally, fairness in demand or consumer surplus always decreases social welfare, while fairness in no-purchase valuation always increases social welfare.
MANAGEMENT SCIENCE
(2022)
Article
Business, Finance
Liming Brotcke
Summary: Focus on fair lending has increased recently as bank and non-bank lenders adopt AI-based credit decision methods. However, the transparency and explainability of AI and ML techniques may raise unique concerns about compliance with fair lending laws compared to traditional regression models. Nonetheless, ML technology can reduce discretionary and subjective decisions, thus reducing the potential for discrimination. Therefore, as financial institutions explore ML applications in loan underwriting and pricing, fair lending assessments will continue to evolve and require additional evaluations.
JOURNAL OF RISK AND FINANCIAL MANAGEMENT
(2022)
Article
Computer Science, Artificial Intelligence
Arashdeep Singh et al.
Summary: Machine learning models used in high-stake applications may exhibit biases towards certain social groups. Previous work has attempted to mitigate this bias through various methods, but more work is needed to extend it to intersectional fairness and to find a balance between accuracy and fairness. This paper addresses these issues by proposing a bias mitigation technique called DualFair and introducing a new fairness metric, AWI, that can handle intersectional fairness. The proposed method is tested using a U.S. mortgage lending dataset and demonstrates relatively high fairness and accuracy metrics.
MACHINE LEARNING AND KNOWLEDGE EXTRACTION
(2022)
Article
Ethnic Studies
Jose Loya
Summary: Studies have shown significant racial disparities between white and black homeowners in the mortgage industry. This paper examines racial differences within the Latino population, finding that black Latinos have the highest rates of loan rejections and high cost originations, while white and Asian Latinos perform better. These findings suggest a tri-racial hierarchy in the mortgage market.
RACE AND SOCIAL PROBLEMS
(2022)
Article
Development Studies
Tyler Haupert
Summary: Despite limited understanding of racial patterns in fintech mortgage lending, this study examines disparities in lending outcomes between White and non-White applicants, as well as neighborhoods of different racial compositions in the United States. The results suggest that fintech lenders have lower approval rates for non-White applicants, particularly Latinos. Additionally, both fintech and traditional lenders distribute subprime credit to non-White neighborhoods at significantly higher rates than to White neighborhoods.
HOUSING POLICY DEBATE
(2022)
Article
Computer Science, Artificial Intelligence
Michelle Seng Ah Lee et al.
Summary: Researchers have proposed various notions of fairness in response to the concern that algorithmic decision-making may reinforce discriminatory biases, but in reality, the ethical and practical trade-offs are more complex and not a one-size-fits-all absolute condition. A new approach considers fairness as a relational notion compared to alternative decision-making processes, discussing the ethical foundations of each fairness definition using US mortgage lending as an example.
MINDS AND MACHINES
(2021)
Article
Computer Science, Artificial Intelligence
Vincenzo Moscato et al.
Summary: Credit risk assessment is crucial for financial institutions, and the emergence of social lending platforms has disrupted traditional services in this area. While these platforms facilitate interaction between borrowers and lenders, the lack of lenders' experience and uncertainty in borrower's credit history can increase risks, necessitating accurate credit risk scoring.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Mathematics, Interdisciplinary Applications
Shira Mitchell et al.
Summary: Recent research has aimed to quantify fairness, particularly in the context of decisions based on statistical and machine learning model predictions. The inconsistency in motivations, terminology, and notation in this new field poses a challenge for cataloging and comparing definitions. This article attempts to bring order by providing a consistent catalog of fairness definitions and exploring the choices, assumptions, and fairness considerations in prediction-based decision-making.
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 8, 2021
(2021)
Article
Multidisciplinary Sciences
Felix G. Rebitschek et al.
Summary: This study provides a representative analysis of error estimations and willingness to accept errors in a Western country (Germany) with regards to algorithmic decision-making systems (ADM). The study found that most respondents underestimated the actual errors made by algorithms and were willing to accept even fewer errors than estimated. People's living conditions affected domain-specific acceptance of errors, suggesting that acceptance of ADM appears to be conditional to strict accuracy requirements.
SCIENTIFIC REPORTS
(2021)
Article
Business, Finance
Pablo de Andres et al.
Summary: The study shows that female entrepreneurs are less likely to ask for a loan, and if they do, the likelihood of obtaining one in the founding year is lower compared to male peers in the same industry. However, women-led companies are less likely to default after receiving a loan in the founding year.
JOURNAL OF CORPORATE FINANCE
(2021)
Article
Computer Science, Theory & Methods
Ninareh Mehrabi et al.
Summary: With the widespread use of AI systems in everyday life, fairness in design has become crucial. Researchers have developed methods to address biases in different subdomains and established a taxonomy for fairness definitions. Existing work shows biases in AI applications, and researchers are working on solutions to mitigate these problems.
ACM COMPUTING SURVEYS
(2021)
Proceedings Paper
Computer Science, Software Engineering
Max Hort et al.
Summary: Machine Learning plays a crucial role in modern decision-making software, but bias is a concern. Achieving fairness involves treating sensitive attributes equally, while also considering discrimination against anti-protected attributes. By using grid search, it is possible to reduce bias while improving the ability to discriminate against anti-protected attributes.
2021 36TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING ASE 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Shahar Segal et al.
Summary: The study introduces a framework to certify the fairness degree of a model based on an interactive and privacy-preserving test, regardless of its training process and architecture. It is found that cryptographic techniques can automate fairness testing and certified inference.
AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY
(2021)
Proceedings Paper
Computer Science, Software Engineering
Joymallya Chakraborty et al.
Summary: This study addresses bias in software decisions and proposes a new solution. By eliminating biased labels and rebalancing internal distributions, it reduces bias while increasing performance and maintaining fairness.
PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21)
(2021)
Proceedings Paper
Computer Science, Information Systems
Avijit Ghosh et al.
Summary: This study investigates the impact of uncertainty and errors in demographic inference on the fairness of fair ranking algorithms. Through simulations and three case studies using real datasets, it demonstrates how demographic inferences drawn from real systems can result in unfair rankings. The results suggest that developers should refrain from using inferred demographic data as input to fair ranking algorithms unless the inferences are highly accurate.
SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL
(2021)
Review
Business, Finance
Anil Kumar et al.
Summary: This study focuses on highlighting the AI-ML-based credit scoring methods adopted by banking or non-banking institutions currently in practice, and their gaps. Through systematic literature review methods, existing research articles have been empirically reviewed in an attempt to identify and compare the most suitable AI-ML-based model adopted by various financial institutions worldwide. The main purpose is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history.
Article
Economics
Teresa Bono et al.
Summary: The study demonstrates that machine learning models are more accurate in credit scoring than traditional models, and they perform consistently on fairness criteria. It also shows that switching to machine learning does not exacerbate or eliminate differences in model performance for different subgroups.
OXFORD REVIEW OF ECONOMIC POLICY
(2021)
Article
Business, Finance
Brent W. Ambrose et al.
Summary: The study reveals that minorities pay higher fees compared to similarly qualified whites when obtaining a loan through white brokers. Additionally, the premium paid by minorities is dependent on the race of the broker. Recent policy changes may not only reduce price disparities but also limit credit access for minorities.
REVIEW OF FINANCIAL STUDIES
(2021)
Article
Business, Finance
Neil Bhutta et al.
Summary: The study found statistically significant gaps by race and ethnicity in mortgage interest rates, but these differences are offset by variations in discount points. Minorities and whites face identical rate schedules, but are located in different positions. Ultimately, there are no differences in total fees by race or ethnicity in mortgage lending.
REVIEW OF FINANCIAL STUDIES
(2021)
Article
Management
Antonio Blanco-Oliver et al.
Summary: Gender plays a role in lending transactions and specifically impacts the loan portfolio credit risk of microfinance institutions (MFIs). A higher proportion of female loan officers increases the loan portfolio at risk, but this is mitigated by the gender affinity between female loan officers and female borrowers.
JOURNAL OF SMALL BUSINESS MANAGEMENT
(2021)
Article
Social Sciences, Mathematical Methods
Richard Berk et al.
Summary: This article clarifies the trade-offs between accuracy and fairness in criminal justice risk assessments, highlighting at least six kinds of fairness which may be incompatible with each other and with accuracy. The differences in base rates across legally protected groups present a major complication in practice, requiring consideration of challenging trade-offs.
SOCIOLOGICAL METHODS & RESEARCH
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Lize Coenen et al.
2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020)
(2020)
Proceedings Paper
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Lily Hu et al.
FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY
(2020)
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Miranda Bogen et al.
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(2020)
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INTERNATIONAL JOURNAL OF FINANCIAL STUDIES
(2020)
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Hua Sun et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2019)
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Ziad Obermeyer et al.
Proceedings Paper
Computer Science, Theory & Methods
Hadi Elzayn et al.
FAT*'19: PROCEEDINGS OF THE 2019 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY
(2019)
Proceedings Paper
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Nripsuta Ani Saxena et al.
AIES '19: PROCEEDINGS OF THE 2019 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY
(2019)
Proceedings Paper
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Ines Valentim et al.
2019 IEEE 30TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE)
(2019)
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Andreas Fuster et al.
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(2019)
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IBM JOURNAL OF RESEARCH AND DEVELOPMENT
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GENDER TECHNOLOGY & DEVELOPMENT
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(2017)
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E Bonilla-silva
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