New Study Shows the Bias of AI

A new study using 8000 articles highlights AI’s bias issue and amplification. If you use AI for science could it be bringing its own flavour of human biases to your research?

As AI technology becomes increasingly adept at catering to human needs, its potential to also fulfil those needs in ways that amplify existing bias is something that is only just beginning to be be explored. This is a primary concern highlighted in recent research conducted by a team from the University of Delaware, shedding light on the inherent biases within AI large language models, such as the widely used ChatGPT. Published in March in the journal Scientific Reports, the study “Bias of AI-generated content: an examination of news produced by large language models” reveals the pressing need for the community, especially to be mindful of these powerful tools.

Led by Xiao Fang, a professor at the Alfred Lerner College of Business and Economics, and his team including Ming Zhao, Minjia Mao, Hongzhe Zhang, and Xiaohang Zhao, the research focused on identifying biases in AI-generated content. Their findings were fairly unequivocal: large language models, including ChatGPT, often produced biased content towards certain groups, even in response to seemingly innocent prompts. This bias was not only present in subtle forms but was even more alarmingly pronounced when the AI models were requested to generate intentionally biased or discriminatory content.

The challenge of measuring bias, inherently subjective, was addressed by comparing the AI-generated content with articles from reputable news outlets known for their careful approach, like Reuters and the New York Times. Using over 8,000 articles as prompts, the study highlighted a stark difference in bias levels, with AI models exhibiting significantly more biased language against minorities and displaying a more toxic sentiment overall.

Reproduced from the paper: Framework for Evaluating Bias of AIGC. (a) We proxy unbiased content with the news articles collected from The New York Times and Reuters. Please see the Section of “Data” for the justification of choosing these news agencies. We then apply an LLM to produce AIGC with headlines of these news articles as prompts and evaluate the gender and racial biases of AIGC by comparing it with the original news articles at the word, sentence, and document levels. (b) Examine the gender bias of AIGC under biased prompts.

Recognising the extent of the problem, Fang and his team are now focused on “debiasing” these language models. Their work underscores the importance of active research in this area, not only to refine AI technologies but also to ensure they serve the broader purpose of unbiased scientific inquiry. The goal is to develop AI tools that truly understand the diversity of human experience and can contribute positively to all areas of research without prejudice.

The study’s revelations about AI biases are not just technical challenges; they raise profound ethical questions. As AI becomes more integrated into scientific research, the responsibility to ensure these tools do not perpetuate harmful biases grows. Future research and development must prioritise ethical considerations, designing AI models that are not only technically advanced but also culturally and socially aware.

For researchers utilising AI in laboratory settings, these findings should be looked at carefully. AI models are increasingly employed for a variety of tasks, including data analysis, literature reviews, and even in drafting research papers. The inherent biases in these models could potentially influence research outcomes, perpetuating stereotypes or underrepresenting minority groups in scientific literature.

The University of Delaware’s research serves as a reminder of the dual nature of AI technology. While AI offers unparalleled opportunities for advancing scientific research, its potential to inadvertently harm through biased outputs cannot be overlooked. As the scientific community continues to harness AI’s power, it must also commit to rigorous efforts to understand, identify, and eliminate biases within these technologies.

You can read the full open access paper in Nature Scientific Reports.

Staff Writer

Our in-house science writing team has prepared this content specifically for Lab Horizons

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