Judge Bias Detection Prompt for Scoring Patterns

This prompt is for evaluation platform teams and responsible AI engineers who need to audit an LLM judge for systematic scoring biases before it gates a product or dataset. The job-to-be-done is detecting whether an automated judge assigns materially different scores based on input attributes—such as topic, response length, writing style, or demographic signals present in the text—rather than on the quality construct the rubric defines. Use this when you have a judge that has been calibrated for accuracy but not yet tested for disparate impact across subgroups, when you are expanding the domain of inputs a judge evaluates, or when you observe unexplained score variance in production that doesn't correlate with human ratings.

Do not use this prompt as a one-shot fairness certification. It is a statistical detection tool, not a root-cause fix. It requires a batch of scored outputs with associated metadata attributes to analyze. If you lack attribute-labeled evaluation data, you need to pair this with a metadata extraction step first. The prompt produces a bias audit report with per-dimension severity classifications and recommended follow-up actions, but it cannot replace a human review of the underlying rubric for construct validity. For regulated domains, always route findings through a human reviewer before acting on them.

The ideal reader is an evaluation engineer or ML platform developer who already has a judge in production or pre-production and needs to surface hidden scoring patterns. You should have access to at least 100 scored examples with consistent metadata tags. Before running this prompt, confirm that your attribute labels are accurate and that your sample size per subgroup is large enough to detect meaningful effect sizes. After receiving the audit report, expect to run follow-up prompts for root cause analysis, rubric revision, or calibration set rebalancing. The output is a diagnostic, not a remediation.

This template is the core instruction set you'll send to a meta-evaluator model. Its job is to act as an impartial auditor, analyzing a batch of historical scoring decisions made by your primary LLM judge. The prompt is designed to be parameterized so you can reuse it across different judges, evaluation dimensions, and input datasets without rewriting the core logic. The placeholders in square brackets are your injection points for the specific judge, data, and attributes under audit.

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You are an expert evaluation auditor specializing in detecting systematic bias in automated scoring systems. Your task is to analyze a batch of scoring decisions made by an LLM judge and produce a structured bias audit report.

## AUDIT CONTEXT
- **Judge Being Audited:** [JUDGE_NAME_AND_VERSION]
- **Evaluation Dimension:** [EVALUATION_DIMENSION]
- **Scoring Scale:** [SCORING_SCALE_DEFINITION]
- **Protected or Input Attributes to Test:** [ATTRIBUTES_LIST]
- **Minimum Sample Size per Attribute Group:** [MIN_SAMPLE_SIZE]
- **Significance Threshold (p-value):** [P_VALUE_THRESHOLD]

## INPUT DATA
You will receive a JSON array of scoring records. Each record contains:
- `id`: unique identifier for the scored item
- `input_text`: the original input that was scored
- `input_metadata`: object containing attribute values for the item (e.g., topic, length, style, demographic signals if present)
- `judge_score`: the numeric or categorical score assigned by the judge
- `judge_justification`: the judge's written reasoning for the score (if available)

[SCORING_DATA_JSON]

## AUDIT INSTRUCTIONS
1. **Stratify the data** by each attribute in [ATTRIBUTES_LIST]. For each attribute group, calculate the mean score, standard deviation, and score distribution.
2. **Perform statistical tests** comparing score distributions across attribute groups. Use appropriate tests based on the scoring scale (e.g., t-test for continuous, chi-squared for categorical). Flag any group comparison where the p-value falls below [P_VALUE_THRESHOLD].
3. **Analyze the judge's justifications** for flagged groups. Identify whether score differences correlate with irrelevant input characteristics (e.g., longer inputs receiving systematically higher scores, certain topics receiving systematically lower scores).
4. **Classify severity** for each detected bias pattern:
   - **CRITICAL:** Statistically significant difference with clear evidence of irrelevant attribute influence and potential for harm.
   - **WARNING:** Statistically significant difference but possible legitimate correlation with quality; requires human review.
   - **NOTABLE:** Difference approaching significance; monitor in next audit cycle.
   - **PASS:** No significant difference detected.
5. **Generate per-attribute findings** with supporting evidence, including example records that illustrate the pattern.

## OUTPUT SCHEMA
Return a JSON object with this exact structure:
{
  "audit_metadata": {
    "judge_audited": "string",
    "evaluation_dimension": "string",
    "total_records_analyzed": number,
    "attributes_tested": ["string"],
    "significance_threshold": number
  },
  "per_attribute_findings": [
    {
      "attribute": "string",
      "groups_compared": ["string"],
      "group_statistics": {
        "[group_name]": {
          "mean_score": number,
          "std_dev": number,
          "sample_size": number,
          "score_distribution": {}
        }
      },
      "statistical_test": "string",
      "p_value": number,
      "significant": boolean,
      "severity": "CRITICAL|WARNING|NOTABLE|PASS",
      "evidence_summary": "string",
      "example_record_ids": ["string"],
      "recommendation": "string"
    }
  ],
  "overall_assessment": {
    "total_biases_detected": number,
    "critical_count": number,
    "warning_count": number,
    "notable_count": number,
    "pass_count": number,
    "summary": "string",
    "recommended_actions": ["string"]
  }
}

## CONSTRAINTS
- Do not fabricate statistical results. If sample sizes are too small for a test, report "INSUFFICIENT_DATA" and skip the comparison.
- Do not infer demographic attributes from text unless explicitly provided in input_metadata.
- Flag any attribute groups with fewer than [MIN_SAMPLE_SIZE] records as underpowered.
- If the judge's justifications are missing or empty, note this limitation and rely solely on score patterns.
- Do not recommend removing the judge without human review of findings.

To adapt this template, start by defining your [ATTRIBUTES_LIST] carefully. Only include attributes you have a legitimate operational or fairness reason to test—such as input length, topic category, or source system—and avoid inferring sensitive attributes from text unless you have a validated classifier and a clear governance mandate. The [SCORING_DATA_JSON] should be programmatically assembled from your evaluation logs, ensuring each record includes the required fields. If your judge uses categorical scores instead of numeric, adjust the statistical tests section accordingly and update the output schema's group_statistics to use frequency distributions rather than means. Always run this audit on a held-out sample that wasn't used to calibrate the judge, and schedule recurring audits using the same template with updated data to detect drift over time.

The Judge Bias Detection Prompt is designed to operate as a batch audit step within an existing evaluation pipeline, not as a real-time scoring component. It expects a structured dataset of scored outputs—each record containing the input text, the LLM judge's score, the judge's justification, and the attribute dimensions you want to audit (e.g., topic, length, style, demographic signals). The prompt produces a structured bias audit report in JSON format, which your application should parse, validate, and store for review. Because this prompt performs statistical reasoning over score distributions, it is sensitive to sample size: run it on batches of at least 50 scored examples per attribute category to produce meaningful results. Smaller batches will trigger the prompt's built-in low-confidence warnings, which your harness should surface rather than suppress.

Wire this prompt into your application as a post-scoring audit job. After your primary LLM judge completes a scoring run, collect the scored records into a JSON array and pass them as [SCORED_DATASET] along with the [ATTRIBUTE_DIMENSIONS] you want to test for bias (e.g., ["topic", "input_length", "formality_level"]). The prompt returns a JSON object with a bias_audit_report containing per-dimension statistical tests, severity classifications, and flagged patterns. Your harness must validate this output against a schema that checks for required fields: dimension, test_type, p_value, effect_size, severity, affected_groups, and evidence_summary. If validation fails, retry once with the same input and a note in the [CONSTRAINTS] field requesting stricter JSON compliance. After two failures, log the raw output and escalate for human review. For high-stakes audits (e.g., fairness reviews before model deployment), always route the final report through a human reviewer before accepting automated severity classifications.

Model choice matters for this prompt. Use a model with strong reasoning capabilities and reliable JSON output (e.g., GPT-4o, Claude 3.5 Sonnet, or equivalent). Avoid smaller or older models that may hallucinate statistical values or misapply test semantics. Set temperature=0 to maximize reproducibility across audit runs. Log every audit result with a unique audit_id, the model version used, the input batch size, and the output report. Store these logs alongside your evaluation records so you can track whether bias patterns shift after judge prompt changes or model updates. Do not use this prompt as a real-time gate on individual scores—it is a batch analysis tool. If you need per-example bias flags, build a separate lightweight classifier and use this prompt for periodic calibration and oversight.