Failure of contextual invariance in gender inference with large language models

Large language models exhibit significant instability in gender inference tasks when faced with minor changes in contextual discourse. Researchers have discovered that introducing minimal, theoretically uninformative context can induce substantial shifts in model outputs, challenging the assumption of contextual invariance. This finding has significant implications for the reliability and fairness of AI systems, particularly in applications where gender inference is critical. The study utilized a controlled pronoun selection task to demonstrate the vulnerability of large language models to contextual manipulation¹. The results suggest that the outputs of these models are highly sensitive to subtle changes in input context, which can lead to inconsistent and potentially biased outcomes. This instability matters to practitioners and informed readers because it can have far-reaching consequences for the development and deployment of AI systems, affecting not only technological performance but also policy, security, and workforce dynamics.