Bridging Philosophy and Computer Science: An Interdisciplinary Framework for AI Ethics in Financial Services
Abstract
As artificial intelligence systems increasingly make consequential decisions in financial services—from credit scoring to investment recommendations—the need for robust ethical frameworks becomes paramount. This paper proposes an interdisciplinary approach that synthesizes virtue ethics, consequentialism, and deontological perspectives with practical software engineering constraints. We introduce the FAIR (Fairness, Accountability, Interpretability, Robustness) framework specifically tailored for financial AI applications, drawing on both philosophical traditions and empirical studies of algorithmic bias. Through case studies of three major financial institutions, we demonstrate how the FAIR framework can be operationalized in production systems, reducing demographic bias by 34% while maintaining model performance.
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Extended Content
Table of Contents
Conference Presentation Summary
This paper was presented at FAccT 2024 in Rio de Janeiro, generating significant discussion around the practical implementation of ethical AI principles in regulated industries.
The FAIR Framework
F - Fairness: Demographic parity analysis across protected categories, with continuous monitoring for distribution drift.
A - Accountability: Clear ownership chains for model decisions, with audit trails satisfying regulatory requirements.
I - Interpretability: Explanation generation at multiple levels—technical, business, and customer-facing.
R - Robustness: Adversarial testing protocols and graceful degradation under distribution shift.
Case Study Highlights
We worked with three institutions over 18 months to implement FAIR:
- Regional Bank (Credit Scoring): Reduced approval rate disparity across demographic groups from 12% to 3%
- Investment Platform (Robo-Advisory): Implemented real-time explanation generation for portfolio recommendations
- Insurance Provider (Risk Assessment): Established continuous fairness monitoring with automated alerts
Philosophical Foundations
The framework draws on:
- Rawlsian principles of justice for fairness definitions
- Kantian respect for persons in interpretability requirements
- Consequentialist analysis for impact assessment
Cite This Paper
Abhimanyu, Dr. Elena Vasquez, Prof. James Liu. "Bridging Philosophy and Computer Science: An Interdisciplinary Framework for AI Ethics in Financial Services." ACM Conference on Fairness, Accountability, and Transparency (FAccT) , 2024.