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Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs

Abstract:
Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at the center of the solution. This study proposes Human Centered approaches to AI design as one solution to mitigate biases and ensure fairness of an AI model in the Financial Services industry. The study provides an overview of HCAI and what it means for an AI system to be fair from the Human Centered angle. Existing AI biases in the financial industry will be examined through case studies reviewing two of the most prominent examples: lending and credit scoring. I will then discuss potential sources of bias at each of the six stages of the AI life cycle. The strategies to mitigate these biases will be examined in the context of the financial sector. Drawing from the Human Centered Design process, I will investigate the role of design in AI and how the Human Centered principles can be applied to the AI life cycle
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s11023-020-09529-4

Authors

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Role:
Author
ORCID:
0000-0001-7725-2503
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Institution:
University of Oxford
Division:
SSD
Department:
Oxford Internet Institute
Role:
Author
ORCID:
0000-0002-5444-2280


Publisher:
Springer
Journal:
Minds and Machines More from this journal
Volume:
31
Issue:
1
Pages:
165-191
Publication date:
2020-06-09
DOI:
EISSN:
1572-8641
ISSN:
0924-6495


Language:
English
Keywords:
Pubs id:
1112757
Local pid:
pubs:1112757
Source identifiers:
W3013532257
Deposit date:
2026-02-12
ARK identifier:
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