# Enhancements for Developing a Comprehensive AI Fairness Assessment Standard

arXiv: https://arxiv.org/abs/2504.07516 · HTML: https://mlsanity.com/paper/2504.07516/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [A Survey on Bias in Visual Datasets](https://mlsanity.com/paper/2107.07919/) (2021)
- [Fairness in Large Language Models: A Taxonomic Survey](https://mlsanity.com/paper/2404.01349/) (2024)
- [Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems](https://mlsanity.com/paper/2201.06952/) (2022)
- [A Survey on Bias and Fairness in Natural Language Processing](https://mlsanity.com/paper/2204.09591/) (2022)

## Built on by (most cited first, 3 of 3)

- [A five-layer framework for AI governance: integrating regulation, standards, and certification](https://mlsanity.com/paper/2509.11332/) (2025)
- [Incorporating AI incident reporting into telecommunications law and policy: Insights from India](https://mlsanity.com/paper/2509.09508/) (2025)
- [Nishpaksh: TEC Standard-Compliant Framework for Fairness Auditing and Certification of AI Models](https://mlsanity.com/paper/2601.16926/) (2026)

## Abstract (arXiv)

As AI systems increasingly influence critical sectors like telecommunications, finance, healthcare, and public services, ensuring fairness in decision-making is essential to prevent biased or unjust outcomes that disproportionately affect vulnerable entities or result in adverse impacts. This need is particularly pressing as the industry approaches the 6G era, where AI will drive complex functions like autonomous network management and hyper-personalized services. The TEC Standard for Fairness Assessment and Rating of AI Systems provides guidelines for evaluating fairness in AI, focusing primarily on tabular data and supervised learning models. However, as AI applications diversify, this standard requires enhancement to strengthen its impact and broaden its applicability. This paper proposes an expansion of the TEC Standard to include fairness assessments for images, unstructured text, and generative AI, including large language models, ensuring a more comprehensive approach that keeps pace with evolving AI technologies. By incorporating these dimensions, the enhanced framework will promote responsible and trustworthy AI deployment across various sectors.