Data and AI governance: Promoting equity, ethics, and fairness in large language models

TL;DR

This paper discusses comprehensive governance strategies for large language models to systematically evaluate and mitigate bias, ensuring ethical and fair AI deployment throughout the entire development and operational lifecycle.

Contribution

It introduces a practical governance framework building on BEATS for assessing bias and fairness in LLMs, applicable to real-world deployment and continuous monitoring.

Findings

Identification of prevalent bias and fairness gaps in LLMs

Development of a governance framework for bias mitigation

Enabling rigorous benchmarking and real-time evaluation

Abstract

In this paper, we cover approaches to systematically govern, assess and quantify bias across the complete life cycle of machine learning models, from initial development and validation to ongoing production monitoring and guardrail implementation. Building upon our foundational work on the Bias Evaluation and Assessment Test Suite (BEATS) for Large Language Models, the authors share prevalent bias and fairness related gaps in Large Language Models (LLMs) and discuss data and AI governance framework to address Bias, Ethics, Fairness, and Factuality within LLMs. The data and AI governance approach discussed in this paper is suitable for practical, real-world applications, enabling rigorous benchmarking of LLMs prior to production deployment, facilitating continuous real-time evaluation, and proactively governing LLM generated responses. By implementing the data and AI governance across the life cycle of AI development, organizations can significantly enhance the safety and responsibility of their GenAI systems, effectively mitigating risks of discrimination and protecting against potential reputational or brand-related harm. Ultimately, through this article, we aim to contribute to advancement of the creation and deployment of socially responsible and ethically aligned generative artificial intelligence powered applications.