  As language models continue to be integrated into applications of personal
and societal relevance, ensuring these models' trustworthiness is crucial,
particularly with respect to producing consistent outputs regardless of
sensitive attributes. Given that first names may serve as proxies for
(intersectional) socio-demographic representations, it is imperative to examine
the impact of first names on commonsense reasoning capabilities. In this paper,
we study whether a model's reasoning given a specific input differs based on
the first names provided. Our underlying assumption is that the reasoning about
Alice should not differ from the reasoning about James. We propose and
implement a controlled experimental framework to measure the causal effect of
first names on commonsense reasoning, enabling us to distinguish between model
predictions due to chance and caused by actual factors of interest. Our results
indicate that the frequency of first names has a direct effect on model
prediction, with less frequent names yielding divergent predictions compared to
more frequent names. To gain insights into the internal mechanisms of models
that are contributing to these behaviors, we also conduct an in-depth
explainable analysis. Overall, our findings suggest that to ensure model
robustness, it is essential to augment datasets with more diverse first names
during the configuration stage.
