Confident, Calibrated, or Complicit: Probing the Trade-offs between Safety Alignment and Ideological Bias in Language Models in Detecting Hate Speech
This research addresses the problem of bias and reliability in LLMs for content moderation, highlighting critical limitations for developers and users, though it is incremental in building on existing safety alignment studies.
The study investigated the trade-offs between safety alignment and ideological bias in large language models (LLMs) for hate speech detection, finding that heavily-aligned models outperformed uncensored ones with 78.7% versus 64.1% strict accuracy but exhibited ideological anchoring and fairness disparities.
We investigate the efficacy of Large Language Models (LLMs) in detecting implicit and explicit hate speech, examining whether models with minimal safety alignment (uncensored) might provide more objective classification capabilities compared to their heavily-aligned (censored) counterparts. While uncensored models theoretically offer a less constrained perspective free from moral guardrails that could bias classification decisions, our results reveal a surprising trade-off: censored models significantly outperform their uncensored counterparts in both accuracy and robustness, achieving 78.7% versus 64.1% strict accuracy. However, this enhanced performance comes with its own limitation -- the safety alignment acts as a strong ideological anchor, making censored models resistant to persona-based influence, while uncensored models prove highly malleable to ideological framing. Furthermore, we identify critical failures across all models in understanding nuanced language such as irony. We also find alarming fairness disparities in performance across different targeted groups and systemic overconfidence that renders self-reported certainty unreliable. These findings challenge the notion of LLMs as objective arbiters and highlight the need for more sophisticated auditing frameworks that account for fairness, calibration, and ideological consistency.