  The rapid advancement of Large Language Models (LLMs) has sparked intense
debate regarding the prevalence of bias in these models and its mitigation.
Yet, as exemplified by both results on debiasing methods in the literature and
reports of alignment-related defects from the wider community, bias remains a
poorly understood topic despite its practical relevance. To enhance the
understanding of the internal causes of bias, we analyse LLM bias through the
lens of causal fairness analysis, which enables us to both comprehend the
origins of bias and reason about its downstream consequences and mitigation. To
operationalize this framework, we propose a prompt-based method for the
extraction of confounding and mediating attributes which contribute to the LLM
decision process. By applying Activity Dependency Networks (ADNs), we then
analyse how these attributes influence an LLM's decision process. We apply our
method to LLM ratings of argument quality in political debates. We find that
the observed disparate treatment can at least in part be attributed to
confounding and mitigating attributes and model misalignment, and discuss the
consequences of our findings for human-AI alignment and bias mitigation. Our
code and data are at https://github.com/david-jenny/LLM-Political-Study.
