Conceptualization and Instrumentation of Maturity of Responsible AI (MRAI): An Empirical Analysis of the US Banking Industry Credit Lending Practices
Abstract
Widespread use of Artificial Intelligence (AI) and Machine Learning (ML) have been employed ubiquitously in the competitive banking industry raising red flags with regulators and social groups due to potential risk of data and algorithms rendering unfair credit lending decisions. The absence of a valid and reliable measure Responsible AI (RAI) has stunted organizational research explaining the motivation and manipulation of RAI (i.e., the organizational balancing act to optimize efficiency and equity). To address this void, we develop a novel measurement instrument to assess the maturity of RAI programs. A review of the nascent literature reveals that there is a wide distribution of RAI capabilities. Our instrument to measure RAI tested favorably for construct validity and reliability. Advancement of this new instrument will enable banks to highlight investments and capabilities in their RAI programs for customer acquisition marketing as well as provide a communication tool for banks and regulators to align on maturity assessments and action plans to enhance fairness in credit lending.
Subject Area
Computer science|Artificial intelligence
Recommended Citation
Ratzan, John, "Conceptualization and Instrumentation of Maturity of Responsible AI (MRAI): An Empirical Analysis of the US Banking Industry Credit Lending Practices" (2022). ETD Collection for Pace University. AAI29391511.
https://digitalcommons.pace.edu/dissertations/AAI29391511
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