Fair lending needs explainable models for responsible recommendation
arXiv:1809.04684v129 citations
Originality Synthesis-oriented
AI Analysis
This work targets the financial services industry by emphasizing the importance of explainable and fair models for credit recommendations, but it appears incremental as it reiterates known challenges without presenting new solutions.
The paper addresses the challenges of explainability and fairness in using machine learning for credit decisions in financial services, highlighting the need for responsible models to meet compliance and ethical standards.
The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.