Challenging systematic prejudices: an investigation into bias against women and girls in large language models

Artificial intelligence is being adopted across industries at an unprecedented pace. Alongside its posited benefits, AI also presents serious risks to society, making the implementation of normative frameworks to reduce these risks a global imperative. The UNESCO Recommendation on the Ethics of AI asserts that “AI actors should make all reasonable efforts to minimize and avoid reinforcing or perpetuating discriminatory or biased applications and outcomes throughout the life cycle of the AI system to ensure fairness of such systems”. To date however, AI-based systems often perpetuate (and even scale and amplify) human, structural and social biases. These biases not only prove difficult to mitigate, but may also lead to harm at the individual, collective, or societal level.

This study explores biases in three significant large language models (LLMs): OpenAI’s GPT-2 and ChatGPT, along with Meta’s Llama 2, highlighting their role in both advanced decision-making systems and as user-facing conversational agents. Across multiple studies, the brief reveals how biases emerge in the text generated by LLMs, through gendered word associations, positive or negative regard for gendered subjects, or diversity in text generated by gender and culture.