Cancer-related discussions on social media provide important spaces for information exchange and peer support, but can also expose users to misinformation with implications for prevention, screening, and treatment decisions. Existing work often treats cancer misinformation as a binary phenomenon, providing limited insight into how misinformation is expressed, engaged with, and associated with potential harm. We introduce a multi-dimensional taxonomy for characterizing cancer misinformation in Reddit discussions of breast, lung, colon, and prostate cancer. Developed through expert annotation, the taxonomy captures seven dimensions spanning misinformation presence, cancer stage, information-seeking and sharing behavior, misinformation type, risk, stance, and topical focus. We evaluate 21 large language models (LLMs) across zero- and few-shot settings and develop a cross-model agreement strategy for scaling misinformation identification to over 133K posts. Our analysis shows that misinformation is heterogeneous in both form and function: unproven and alternative treatments emerge as a prominent topic, misinformation frequently occurs within exchanges that combine information seeking and sharing, and users engage with questionable claims through both endorsement and uncertainty. We further find that classification difficulty varies substantially across dimensions, with risk assessment posing particular challenges for both human annotators and LLMs. Our taxonomy and empirical findings move beyond binary detection toward a more nuanced characterization of how cancer misinformation is produced, discussed, and encountered in online health communities.

Large language models (LLMs) deployed as decision agents in regulated settings—loan approval, hiring, clinical triage—must pass pre-deployment fairness audits that report a single fairness number per model under one implicit configuration. We argue this framing misses the dominant source of variation: configuration itself. Sweeping five axes (persona, interview, deliberation, aggregation rule, order) across three frontier LLMs (Claude Sonnet 4.6, GPT-4o-mini, Grok 4.20 Reasoning) on a 1,000-applicant Adult Census Income sample and a 500-applicant ACSIncome (2024 ACS data) sample, we find that within a single model, persona alone swings positive-prediction rate by up to 91 percentage points and collapses decision rules on two of three models; within a Conservative/Balanced/Progressive panel, replacing the panel chair on identical deliberation transcripts moves the disparate-impact ratio from adverse-impact to near-parity; and reversing the speaking order flips up to 34% of applicants on the same panel. We identify a median-voter effect: the Balanced agent matches the majority-vote verdict on 95.8–100% of applicants across all three models—deliberative aggregation reduces, mechanically, to single-agent decision by the median. The audit unit is the configuration, not the model: a system can pass an audit under one configuration and be silently reconfigured into a non-compliant one through a prompt edit—a change that current regulations do not treat as requiring re-audit. The code to reproduce the experiments in this paper is available at https://anonymous.4open.science/r/agent-decision-bias-emnlp-F059/