Consumer-lending discrimination in the FinTech Era

Bartlett, Robert P.; Morse, Adair; Stanton, Richard; Wallace, Nancy

doi:10.1016/j.jfineco.2021.05.047

Cited by 232 publications

(73 citation statements)

References 28 publications

(21 reference statements)

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“…Taking the flow of originations over these five years as a rough approximation to the stock of outstanding loans, a US$100 difference in annual interest payments would correspond to a total cost to Black borrowers of US$180 million per year. Using a slightly different calculation, Bartlett et al (2021) estimate that an increase in the interest rate paid by Latinx and African-American borrowers of 4.6 bps-corresponding to Bartlett et al We see larger effects of the more sophisticated technology on the dispersion of interest rates both in the overall population and within each group (columns ( 5) and ( 6)). These results are reminiscent of our Lemma 1, in which the new technology generates predictions that are a mean-preserving spread of the older technology.…”

Section: Accept (%)mentioning

confidence: 93%

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Predictably Unequal? The Effects of Machine Learning on Credit Markets

Fuster¹,

Goldsmith-Pinkham²,

Ramadorai³

et al. 2021

The Journal of Finance

213

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Innovations in statistical technology in functions including credit-screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.IN RECENT YEARS, NEW PREDICTIVE statistical methods and machine learning techniques have been rapidly adopted by businesses seeking profitability gains in a broad range of industries. 1 The pace at which these technologies have been adopted has prompted concerns that the risks associated with their

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Section: Accept (%)mentioning

confidence: 93%

“…11 Bartlett et al (2021) empirically study whether "FinTech" mortgage lenders in the United States appear to discriminate more across racial groups. Buchak et al (2018) and Fuster et al (2019) study other aspects of FinTech lending in the U.S. mortgage market.…”

mentioning

confidence: 99%

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Fuster¹,

Goldsmith-Pinkham²,

Ramadorai³

et al. 2021

The Journal of Finance

213

View full text Add to dashboard Cite

show abstract

“…Also, data sources may be incomplete or biased towards showing what actors want to show (UNESCO 2021). Moreover, AI systems may learn to misbehave from the information exchange with humans (Bartlett et al 2022;Mann and O'Neil 2016;Noble 2018). Advancements in approaches to ensure security, adaptability, transparency, and explainability are at the core of the future bidirectional exchanges between humans and AI (Ezer et al 2019).…”

Section: Explainable Ai: What Does It Do and Why?mentioning

confidence: 99%

“…The process of AI adaptation to humans' behaviour and needs is not coming without risks. AI systems might develop unexpected and unwanted misbehaviour (Bartlett et al 2022;Mann and O'Neil 2016;Noble 2018), with an open question: to whom does the responsibility of this belong?…”

Section: Extending the Perspective On Haii: Value Responsibility And ...mentioning

confidence: 99%

Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle

et al. 2022

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The European Union (EU) Commission’s whitepaper on Artificial Intelligence (AI) proposes shaping the emerging AI market so that it better reflects common European values. It is a master plan that builds upon the EU AI High-Level Expert Group guidelines. This article reviews the masterplan, from a culture cycle perspective, to reflect on its potential clashes with current societal, technical, and methodological constraints. We identify two main obstacles in the implementation of this plan: (i) the lack of a coherent EU vision to drive future decision-making processes at state and local levels and (ii) the lack of methods to support a sustainable diffusion of AI in our society. The lack of a coherent vision stems from not considering societal differences across the EU member states. We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU. Moreover, the Commission aims at changing the AI development culture proposing a human-centred and safety-first perspective that is not supported by methodological advancements, thus taking the risks of unforeseen social and societal impacts of AI. We discuss potential societal, technical, and methodological gaps that should be filled to avoid the risks of developing AI systems at the expense of society. Our analysis results in the recommendation that the EU regulators and policymakers consider how to complement the EC programme with rules and compensatory mechanisms to avoid market fragmentation due to local and global ambitions. Moreover, regulators should go beyond the human-centred approach establishing a research agenda seeking answers to the technical and methodological open questions regarding the development and assessment of human-AI co-action aiming for a sustainable AI diffusion in the society.

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“…However, the development of AI has also stirred a passionate debate about potential discrimination biases (O'Neil (2017), Bartlett et al (2022)). Indeed, when automatically assessing the creditworthiness of loan applicants, credit scoring models can place groups of individuals sharing a protected attribute, such as gender, age, citizenship or racial origin, at a systematic disadvantage in terms of access (acceptance rate) or cost (interest rate).…”

Section: Introductionmentioning

confidence: 99%

The Fairness of Credit Scoring Models

Hurlin¹,

Pérignon²,

Saurin³

2022

Preprint

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In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they also often discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the population. In this paper, we show how (1) to test whether there exists a statistically significant difference between protected and unprotected groups, which is called lack of fairness and (2) to identify the variables that cause the lack of fairness. We then use these variables to optimize the fairness-performance trade-off. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, and improved for the benefit of protected groups.

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Consumer-lending discrimination in the FinTech Era

Cited by 232 publications

References 28 publications

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle

The Fairness of Credit Scoring Models

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