AI is now used in many areas of our lives, from hiring and education to healthcare, but it often mirrors the prejudices that exist in society. This review brings together evidence on how AI systems, and chatbots such as ChatGPT in particular, can perpetuate gender bias. Examples include a hiring algorithm that learned to favour men, and ChatGPT writing recommendation letters that described women with more social words, such as “family”, while letters for men emphasised their professional skills.
In healthcare, the review explains that many AI tools are trained on data that comes mostly from men, which can lead to unequal care. Women’s heart disease symptoms are more likely than men’s to be misdiagnosed or attributed to other conditions, even when they report the same symptoms, and AI trained on this kind of data can repeat these patterns. The review also cites studies showing that AI tools for diagnosing chest diseases underdiagnosed women more often, and that GPT-4 was more likely to recommend treatments and further tests for men than for women.
The authors identify three main sources of bias: the data used to train AI, the way the systems are designed, and the feedback loop created by users’ interactions with them. To address these, they propose improving the quality and diversity of data and of the people who label it, using algorithms designed with fairness in mind, and strengthening policies at company, national and international levels, such as the EU AI Act.
Learn more about this review here: https://doi.org/10.1016/j.chbah.2025.100145
Reference:
Ho, J. Q. H., Hartanto, A., Koh, A., & Majeed, N. M. (2025). Gender biases within artificial intelligence and ChatGPT: Evidence, sources of biases and solutions. Computers in Human Behavior: Artificial Humans, 4, 100145.