Bias in Humans and AI - What To Do About It?

The rise in popularity of general-purpose large language models (LLMs) raises questions around bias and fairness in the decision they make. Do these models reflect the biases and stereotypes present in the data they have been pre-trained on? If so, how should we deal with it? In this talk, we first discuss issues of bias in human data using as an example gender bias in Wikipedia where we looked at how well represented genders are across different categories of articles. We then move on to look at issues of bias in Artificial Intelligence (AI) using as an example political bias in LLMs. We show how it is possible to measure the political standing of different LLMs and to control their standing by telling them to impersonate certain profiles. This also shows what are some of the existing stereotypes (e.g., a museum curator is left-wing and a retired army officer is right-wing) embedded into the LLMs during pre-training. Finally, we discuss how to explore and manage bias existing in LLMs, how these models perform when used for sensitive tasks, and how users tend to trust AI agents for low-risk and high-risks tasks.