[Paper] The Confidence Trap: Gender Bias and Predictive Certainty in LLMs

Source: arXiv - 2601.07806v1

Overview

Large Language Models (LLMs) are increasingly deployed in high‑stakes settings—customer support bots, hiring tools, content moderation, and more. While these models output a probability “confidence” for each prediction, it’s unclear whether that confidence reliably reflects fairness, especially regarding gender bias. The paper The Confidence Trap: Gender Bias and Predictive Certainty in LLMs investigates exactly this mismatch, revealing that some state‑of‑the‑art models are poorly calibrated when gendered pronoun resolution is involved.

Key Contributions

• Fairness‑aware calibration analysis – First systematic study of how LLM confidence scores line up with human judgments of gender bias.
• Gender‑ECE metric – A novel Expected Calibration Error variant that isolates calibration disparities across gender groups.
• Benchmark across six leading LLMs – Empirical comparison showing that Gemma‑2 suffers the worst gender‑specific mis‑calibration.
• Guidelines for ethical deployment – Practical recommendations for developers who rely on confidence scores for decision‑making.

Methodology

1. Dataset construction – The authors curated a gender‑bias benchmark consisting of sentences that require pronoun resolution (e.g., “The doctor said she will arrive soon”). Each instance is annotated by human raters for the “fair” gender assignment.
2. Model inference – Six popular LLMs (including Gemma‑2, Llama‑2, GPT‑4, etc.) generate probability distributions over possible pronoun choices. The top‑scoring choice and its confidence score are recorded.
3. Calibration measurement – Traditional Expected Calibration Error (ECE) is computed separately for male‑referent and female‑referent groups. The new Gender‑ECE aggregates the difference between these two ECE values, quantifying gender‑specific calibration gaps.
4. Statistical analysis – Paired t‑tests and bootstrapped confidence intervals assess whether observed gaps are statistically significant.

The pipeline is deliberately simple: no fine‑tuning or prompt engineering is applied, so the results reflect out‑of‑the‑box model behavior.

Results & Findings

• Calibration mismatch: All models exhibit some degree of mis‑calibration, but the gender‑specific disparity varies widely.
• Confidence vs. fairness: High confidence does not guarantee unbiased predictions; in many cases, the model is most certain when it makes a biased choice.
• Gender‑ECE effectiveness: The new metric correlates strongly (ρ = 0.78) with human‑perceived fairness gaps, outperforming raw ECE in detecting bias.

Practical Implications

• Risk assessment: Developers using confidence scores to gate downstream actions (e.g., auto‑approving a request) should treat those scores as potentially biased indicators, especially in gender‑sensitive contexts.
• Model selection: When fairness is a priority, GPT‑4‑style models currently offer better calibrated confidence, while Gemma‑2 may require additional post‑processing or fine‑tuning.
• Calibration‑as‑a‑service: The Gender‑ECE metric can be integrated into CI pipelines to flag regression in gender fairness after model updates.
• Prompt engineering: Simple prompt tweaks (e.g., explicitly stating “use gender‑neutral language”) can reduce confidence gaps, offering a low‑cost mitigation path.
• Regulatory compliance: For industries subject to fairness audits (finance, hiring, healthcare), reporting Gender‑ECE alongside traditional performance metrics can satisfy emerging transparency requirements.

Limitations & Future Work

• Scope limited to gender – The study focuses solely on binary gender pronouns; extending the framework to non‑binary and intersectional identities is needed.
• Static benchmarks – The dataset reflects a specific set of sentence structures; real‑world user inputs may be noisier and more diverse.
• No fine‑tuning evaluated – The authors deliberately avoided model adaptation; future work could explore how calibration‑aware fine‑tuning impacts Gender‑ECE.
• Broader bias dimensions – Applying the same calibration lens to race, age, or socioeconomic bias remains an open research avenue.

Bottom line: Confidence scores from LLMs are not a silver bullet for fairness. By measuring calibration through the lens of gender bias, this paper equips developers with a concrete diagnostic tool (Gender‑ECE) and actionable insights for building more equitable AI systems.

Authors

• Ahmed Sabir
• Markus Kängsepp
• Rajesh Sharma

Paper Information

• arXiv ID: 2601.07806v1
• Categories: cs.CL, cs.LG
• Published: January 12, 2026
• PDF: Download PDF