Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias

TL;DR

This paper discusses the biases, toxicity, and unreliability in large language models like ChatGPT, emphasizing data quality issues and advocating for interdisciplinary efforts and governance to mitigate societal harms.

Contribution

It highlights the primary data-driven origins of biases in LLMs and calls for collaborative, interdisciplinary approaches and governance frameworks to address these challenges.

Findings

Biases stem mainly from training data quality and diversity

Need for interdisciplinary efforts to mitigate biases

Call for governance and accountability frameworks

Abstract

This paper investigates the challenges associated with bias, toxicity, unreliability, and lack of robustness in large language models (LLMs) such as ChatGPT. It emphasizes that these issues primarily stem from the quality and diversity of data on which LLMs are trained, rather than the model architectures themselves. As LLMs are increasingly integrated into various real-world applications, their potential to negatively impact society by amplifying existing biases and generating harmful content becomes a pressing concern. The paper calls for interdisciplinary efforts to address these challenges. Additionally, it highlights the need for collaboration between researchers, practitioners, and stakeholders to establish governance frameworks, oversight, and accountability mechanisms to mitigate the harmful consequences of biased LLMs. By proactively addressing these challenges, the AI community can harness the enormous potential of LLMs for the betterment of society without perpetuating harmful biases or exacerbating existing inequalities.