Data and AI governance: Promoting equity, ethics, and fairness in large
  language models
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.
6895003b48b0ae5ca2710c75
Alok Abhishek
claimed_verified
2025-08-08T16:20:52.515Z
6478fc1512ae749b62ebbbd5
https://cdn-avatars.huggingface.co/v1/production/uploads/6478fc1512ae749b62ebbbd5/zw-wYy1vEWnG9-t6c9W_x.jpeg
Alok Abhishek
user
alokabhishek
6895003b48b0ae5ca2710c76
Lisa Erickson
6895003b48b0ae5ca2710c77
Tushar Bandopadhyay
claimed_verified
2025-08-27T07:11:57.091Z
657372396da136b50f5489a0
/avatars/57a693058e9a05a2c32f02bab1d8e819.svg
Tushar Bandopadhyay
user
tbandopa
Data and AI governance: Promoting equity, ethics, and fairness in large
  language models
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.
Approaches to govern, assess, and quantify bias in machine learning models, particularly large language models, are discussed, emphasizing data and AI governance frameworks for ethical deployment.
Large Language Models Bias Evaluation and Assessment Test Suite (BEATS) data and AI governance Bias Ethics Fairness Factuality GenAI systems