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.

Citation

Federico Torrielli, “Stars, Stripes, and Silicon: Unravelling the ChatGPT’s All-American, Monochrome, Cis-centric Bias,” in Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Revised Selected Papers, Part I, Rosa Meo and Fabrizio Silvestri, Eds., ser. Communications in Computer and Information Science, vol. 2133, Springer, 2023, pp. 283–292. DOI: 10.1007/978-3-031-74630-7_19

@inproceedings{DBLP:conf/pkdd/Torrielli23,
	title        = {Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias},
	author       = {Federico Torrielli},
	year         = 2023,
	booktitle    = {Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of {ECML} {PKDD} 2023, Turin, Italy, September 18-22, 2023, Revised Selected Papers, Part {I}},
	publisher    = {Springer},
	series       = {Communications in Computer and Information Science},
	volume       = 2133,
	pages        = {283--292},
	doi          = {10.1007/978-3-031-74630-7_19},
	url          = {https://doi.org/10.1007/978-3-031-74630-7_19},
	editor       = {Rosa Meo and Fabrizio Silvestri},
	timestamp    = {Mon, 10 Mar 2025 15:09:49 +0100},
	biburl       = {https://dblp.org/rec/conf/pkdd/Torrielli23.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Key Topics

  • AI Fairness and Bias
  • Cultural representation in LLMs
  • Gender and racial bias in AI
  • ChatGPT analysis