BiAxisBias: Evaluating LLM Bias Beyond a Single Prompt and a Single Explanation

LLM bias scores can depend on audit design. We introduce BiAxisBias, a prespecified audit varying task, role, perspective, sentiment, and wording over 200 stereotype statements while retaining forced Selection and Rationale as separate protocol readouts. Its main matrix spans eight LLMs and 401 templates (641,600 responses). Across five equivalent questions, 17.1% of 1,600 model-statement pairs change Selection. With three observations per unit in both arms, instability averages 10.5% across all ten three-wording subsets, versus 6.3% for three identical calls. Across four controlled task paradigms, 9/28 model pairs reverse; a seven-model factorial sensitivity identifies task-by-sentiment as the largest two-way component (raw eta-squared = 0.0465). In 10,000 equal-budget resampling draws, mean absolute error against a declared 18-condition finite reference is 10.63 points for one-template concentration, 1.86 for matrix-wide simple random sampling, and 1.75 for condition stratification; ranking inversions are 20.6%, 4.8%, and 5.5%. Thus broad coverage drives the gain, while stratification has only a modest score-error advantage and no ranking advantage over random sampling. In a separate forced-output diagnostic, task-specific Selection mappings and judge-coded Rationale stance disagree in 34.2% of 7,959 dual-valid responses (31.2% versus 3.0% by direction). This diagnoses output-contract sensitivity, not two validated measures of one construct.