Do LLMs Take Sides? Adjudex-Labor: Benchmarking Employer-Employee Bias in Arbitral and Judicial Decision-Making

Evaluations of legal large language models (LLMs) often emphasize agreement with human adjudicators, overlooking whose interests their disagreements favor. In labor disputes, this omission is consequential: denying an employment relationship can exclude workers from legal protection, and repeated departures may concentrate legal risks on the same party. We introduce Adjudex-Labor, a benchmark of real Chinese labor disputes that examines positional tendencies across arbitration and subsequent judgments, using human decisions as institutional references rather than normative ground truth. Our evaluation distinguishes how often models disagree with human adjudicators from differences in the rates of employer- and employee-favoring departures. Employer-favoring tendencies predominate in independent arbitration. When the same model configuration performs arbitration and judgment, replacing human arbitration outcomes with model-generated outcomes shifts subsequent judgments toward employers. Crucially, closer agreement with human courts can coexist with more employer-favoring departures, while additional reasoning does not consistently improve positional balance. A separate monetary evaluation finds that most configurations award employees less than matched human adjudicators, including some that favor employees or appear balanced in employment-status decisions. These findings show that institutional agreement and positional balance are distinct properties, and that positional tendencies depend on both the adjudicative context and the outcome evaluated. LLM evaluation must therefore examine not only agreement with human decisions, but also how departures distribute protection, risk, and remedies between parties and across decision stages.