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Essay 4.6

Data Quality And Bias in Explainable AI for Retail Banks: A propensity score matching

31
Pages
Chicago
Style
~ 32–47 mins
Reading Time
Psychology Climate Cybersecurity
Abstract

This essay investigates “Data Quality And Bias in Explainable AI for Retail Banks: A propensity score matching” using a propensity score matching. Through a socio-technical lens, the analysis integrates multi-source data to derive a stakeholder-aligned blueprint for researchers and practitioners.

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Data Quality And Bias in Explainable AI for Retail Banks: A propensity score matching

ABSTRACT
Data Quality And Bias in Explainable AI for Retail Banks: A propensity score matching is unpacked across themes: costs, scalability, security, and benefits. Limitations and future research paths are noted.
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