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

Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoning