The paper proposes a lifecycle governance framework, built on the authors' BEATS benchmark, to quantify and mitigate bias, ethics, fairness, and factuality failures in large language models.
Fairness Assessment for Artificial Intelligence in Financial Industry
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abstract
Artificial Intelligence (AI) is an important driving force for the development and transformation of the financial industry. However, with the fast-evolving AI technology and application, unintentional bias, insufficient model validation, immature contingency plan and other underestimated threats may expose the company to operational and reputational risks. In this paper, we focus on fairness evaluation, one of the key components of AI Governance, through a quantitative lens. Statistical methods are reviewed for imbalanced data treatment and bias mitigation. These methods and fairness evaluation metrics are then applied to a credit card default payment example.
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cs.CL 1years
2025 1verdicts
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Data and AI governance: Promoting equity, ethics, and fairness in large language models
The paper proposes a lifecycle governance framework, built on the authors' BEATS benchmark, to quantify and mitigate bias, ethics, fairness, and factuality failures in large language models.