Communication Bias in Large Language Models: A Regulatory Perspective

TL;DR

This paper examines how communication biases in large language models impact societal fairness and discusses regulatory frameworks like the EU's AI Act, emphasizing the need for better governance and competition to ensure trustworthy AI.

Contribution

It provides a regulatory perspective on communication bias in LLMs, highlighting the importance of design governance and competition beyond existing regulations.

Findings

Bias risks in LLM outputs and societal impact analyzed

Regulatory frameworks like EU's AI Act discussed

Calls for stronger governance and competition in AI

Abstract

Large language models (LLMs) are increasingly central to many applications, raising concerns about bias, fairness, and regulatory compliance. This paper reviews risks of biased outputs and their societal impact, focusing on frameworks like the EU's AI Act and the Digital Services Act. We argue that beyond constant regulation, stronger attention to competition and design governance is needed to ensure fair, trustworthy AI. This is a preprint of the Communications of the ACM article of the same title.