{ "evaluation_id": "EVAL-20260402-193016", "question_id": "ANALYSIS-012", "question_text": "A bank uses an ML model for loan approvals. Accuracy: 92%. But analysis shows: approval rate for Group A: 78%, Group B: 45%. The bank says 'the model doesn't use race as a feature.' (1) Explain how the model can be discriminatory without using race directly. (2) What proxy variables might cause this? (3) Is equalizing approval rates the right fix? What are the tradeoffs? (4) Design an audit procedure.", "category": "analysis", "timestamp": "2026-04-02T19:30:16.588Z", "display_date": "Apr 02, 2026", "winner": { "name": "Grok 4.20", "provider": "openrouter", "score": 9.47 }, "avg_score": 8.814, "matrix_size": 90, "models_used": [ { "id": "claude_opus_46", "name": "Claude Opus 4.6", "provider": "openrouter" }, { "id": "gpt_5_4", "name": "GPT-5.4", "provider": "openrouter" }, { "id": "deepseek_v4", "name": "DeepSeek V4", "provider": "openrouter" }, { "id": "mimo_v2_flash", "name": "MiMo-V2-Flash", "provider": "Xiaomi" }, { "id": "claude_sonnet_46", "name": "Claude Sonnet 4.6", "provider": "openrouter" }, { "id": "grok_420", "name": "Grok 4.20", "provider": "openrouter" }, { "id": "gpt_oss_120b", "name": "GPT-OSS-120B", "provider": "OpenAI" }, { "id": "gemini_3_flash", "name": "Gemini 3 Flash Preview", "provider": "Google" }, { "id": "minimax_m25", "name": "MiniMax M2.5", "provider": "openrouter" }, { "id": "gemini_31_pro", "name": "Gemini 3.1 Pro", "provider": "openrouter" } ], "rankings": { "grok_420": { "display_name": "Grok 4.20", "provider": "openrouter", "average_score": 9.47, "score_count": 9, "min_score": 8.65, "max_score": 10, "rank": 1 }, "gpt_5_4": { "display_name": "GPT-5.4", "provider": "openrouter", "average_score": 9.34, "score_count": 9, "min_score": 8.8, "max_score": 9.8, "rank": 2 }, "mimo_v2_flash": { "display_name": "MiMo-V2-Flash", "provider": "Xiaomi", "average_score": 9.22, "score_count": 9, "min_score": 8.8, "max_score": 10, "rank": 3 }, "minimax_m25": { "display_name": "MiniMax M2.5", "provider": "openrouter", "average_score": 9.16, "score_count": 9, "min_score": 8.65, "max_score": 9.8, "rank": 4 }, "deepseek_v4": { "display_name": "DeepSeek V4", "provider": "openrouter", "average_score": 9.03, "score_count": 9, "min_score": 8.6, "max_score": 9.8, "rank": 5 }, "gemini_3_flash": { "display_name": "Gemini 3 Flash Preview", "provider": "Google", "average_score": 8.94, "score_count": 9, "min_score": 8.45, "max_score": 9.8, "rank": 6 }, "gpt_oss_120b": { "display_name": "GPT-OSS-120B", "provider": "OpenAI", "average_score": 8.48, "score_count": 9, "min_score": 6.65, "max_score": 9.45, "rank": 7 }, "gemini_31_pro": { "display_name": "Gemini 3.1 Pro", "provider": "openrouter", "average_score": 8.31, "score_count": 9, "min_score": 7.25, "max_score": 9.45, "rank": 8 }, "claude_sonnet_46": { "display_name": "Claude Sonnet 4.6", "provider": "openrouter", "average_score": 8.15, "score_count": 9, "min_score": 6.5, "max_score": 9.8, "rank": 9 }, "claude_opus_46": { "display_name": "Claude Opus 4.6", "provider": "openrouter", "average_score": 8.04, "score_count": 9, "min_score": 6.75, "max_score": 9.45, "rank": 10 } }, "judgments": [ { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 6.8, "brief_justification": "The response provides highly accurate, deep, and clear explanations for the first two questions, but completely cuts off and fails to answer parts 3 and 4 regarding tradeoffs and audit procedures." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.6, "brief_justification": "Excellent, accurate, and highly detailed response covering all parts of the prompt with great depth and actionable advice, though it cuts off slightly at the very end." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 9.8, "brief_justification": "The response is exceptionally accurate, comprehensive, and well-structured. It provides deep insights into fairness metrics, proxy variables, and offers a highly actionable audit procedure." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 10, "brief_justification": "The response is exceptionally accurate, comprehensive, and well-structured. It demonstrates deep understanding of algorithmic fairness, proxy variables, parity metrics, and provides a highly actionable audit procedure." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 7.55, "brief_justification": "The response provides excellent, accurate, and deep analysis of proxy variables and disparate impact with clear formatting. However, it cuts off mid-sentence in section 3 and completely fails to answer section 4, severely impacting its completeness." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 10, "brief_justification": "The response is exceptionally accurate, comprehensive, and well-structured. It demonstrates deep knowledge of ML fairness, provides highly actionable audit steps, and clearly explains complex tradeoffs." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 7.9, "brief_justification": "The response provides highly accurate, clear, and deep analysis of ML fairness, proxy variables, and the tradeoffs of equalizing rates. However, it cuts off abruptly before answering the fourth requirement to design an audit procedure, severely impacting completeness." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9.8, "brief_justification": "The response is exceptionally accurate, well-structured, and comprehensive. It correctly identifies ML fairness concepts like redundant encoding, demographic parity, and equalized odds, while providing a highly actionable and detailed audit procedure." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.2, "brief_justification": "The response is highly comprehensive, clear, and actionable. It covers all parts of the prompt with good depth, though the explanation of individual vs group fairness tradeoffs slightly conflates the problem with the solution." }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 8.25, "brief_justification": "The response is highly accurate and well-structured, correctly identifying fairness through unawareness, proxy discrimination, and the tradeoffs of demographic parity vs. equalized odds. The proxy variables are well-chosen and explained with historical context. The discussion of tradeoffs is nuanced, correctly noting that equalizing rates can harm the very group it aims to help. However, the audit procedure in Phase 2 is clearly cut off mid-sentence, leaving it incomplete—missing important steps" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.2, "brief_justification": "Exceptionally thorough and well-structured response that covers all four parts of the question in great detail. The explanation of proxy discrimination is accurate and well-illustrated. The proxy variables section is comprehensive, covering geographic, socioeconomic, credit, behavioral, and social proxies. The fairness tradeoffs discussion correctly identifies multiple fairness definitions (demographic parity, equal opportunity, equalized odds, calibration) and their tensions. The audit procedur" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 9, "brief_justification": "The response is well-structured, accurate, and covers all four parts thoroughly. Proxy variables are well-identified, the tradeoff analysis is nuanced (mentioning both demographic parity limitations and alternative fairness metrics), and the audit procedure is practical with concrete steps and tools. Minor gaps: could have discussed disparate impact legal standards (e.g., 80% rule), mentioned calibration as a fairness criterion, or explored intersectionality. The shadow model idea for proxy dete" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.2, "brief_justification": "The response is exceptionally well-structured, covering all four parts with strong technical accuracy. Part 1 correctly identifies proxy discrimination, historical bias, and intersectionality. Part 2 provides relevant and well-explained proxy variables. Part 3 offers a nuanced discussion of tradeoffs between statistical parity and predictive parity, with appropriate legal context. Part 4 presents a thorough, phased audit procedure including counterfactual testing and continuous monitoring. Minor" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 7.65, "brief_justification": "The response demonstrates strong understanding of proxy discrimination, feedback loops, and why accuracy is misleading for fairness. Parts (1) and (2) are excellent with clear mechanisms and well-organized proxy variable taxonomy. However, the response is clearly truncated - part (3) cuts off mid-table before completing the tradeoffs analysis, and part (4) on audit procedure design is entirely missing. This significantly impacts completeness and usefulness scores. The content that is present is " }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 10, "brief_justification": "This is an exceptional response across all dimensions. It correctly explains proxy discrimination and redundant encoding, provides a comprehensive and well-organized list of proxy variables with contextual explanations, gives a nuanced and balanced analysis of the tradeoffs of equalizing approval rates (citing specific fairness definitions like equalized odds, calibration, and counterfactual fairness), and designs a thorough, practical five-phase audit procedure including specific techniques lik" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 8.65, "brief_justification": "The response is highly accurate, well-structured with clear tables, and demonstrates deep understanding of algorithmic fairness concepts, proxy variables, fairness metrics tradeoffs, and practical recommendations. It correctly explains indirect discrimination mechanisms, provides comprehensive proxy variable examples with contextual reasoning, and offers a nuanced analysis of fairness metrics including the impossibility theorem. The main weakness is that Part 4 (audit procedure) appears to be cu" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9.2, "brief_justification": "The response is exceptionally well-structured and covers all four parts of the question with strong accuracy. It correctly identifies disparate impact vs. disparate treatment, provides relevant proxy variables, thoughtfully discusses the tradeoffs of demographic parity vs. equalized odds vs. predictive parity, and designs a comprehensive multi-phase audit procedure. The mention of the 80% rule, counterfactual testing, and label bias inspection shows genuine depth. Minor gaps: could have discusse" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.2, "brief_justification": "The response is exceptionally well-structured, covering all four parts of the question with accuracy and depth. Part 1 correctly identifies indirect discrimination mechanisms. Part 2 provides a comprehensive list of proxy variables with clear explanations. Part 3 thoughtfully analyzes the tradeoffs of demographic parity and proposes superior alternatives like equalized odds and calibration. Part 4 designs a thorough four-phase audit procedure. Minor issues: the description of individual vs. grou" }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 7.45, "brief_justification": "Accurate on proxy discrimination, fairness-through-unawareness, and tradeoffs of demographic parity, with a solid start on an audit. However, the response is truncated, so the audit procedure is incomplete and missing fuller discussion of lending-specific fairness/legal nuances and implementation details." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 6.75, "brief_justification": "Accurately explains proxy discrimination and historical bias with good conceptual depth, but the response is incomplete: it appears truncated before fully addressing proxy examples, approval-rate equalization tradeoffs, and a concrete audit procedure." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.8, "brief_justification": "Accurate and well-structured response covering proxies, fairness tradeoffs, and a practical audit plan. Strong overall, though it could better distinguish fairness definitions and note legal/business considerations more explicitly." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9, "brief_justification": "Accurate and well-structured response covering proxy discrimination, likely proxy variables, fairness tradeoffs, and a practical audit plan. Minor overstatement in suggesting equal opportunity as the preferred fix and some legal nuance could be tighter, but overall highly strong and actionable." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 6.5, "brief_justification": "Accurately explains proxy discrimination and fairness tradeoffs with good conceptual depth, but the response is cut off before completing part (3) and does not provide the requested audit procedure, reducing completeness and practical usefulness." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.2, "brief_justification": "Accurate and thorough response covering proxy discrimination, likely proxies, fairness tradeoffs, and a strong multi-phase audit plan. Clear and practical, with only minor overstatements around legal framing and some advanced concepts that could use tighter qualification." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 6.65, "brief_justification": "Accurate and well-structured on indirect discrimination, proxies, and fairness tradeoffs, but the audit procedure is cut off and incomplete. Some legal claims are oversimplified or imprecise, which lowers correctness and practical usefulness." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.6, "brief_justification": "Accurate and well-structured response covering proxies, fairness tradeoffs, and a practical audit plan. Minor limitations: it could mention legal constraints on using protected attributes in auditing/remediation and expand on validation/monitoring after deployment." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9, "brief_justification": "Accurate and well-structured response covering indirect discrimination, proxy variables, fairness tradeoffs, and a practical audit plan. Minor issues include a few imprecise terms and some oversimplification around fairness definitions and legal nuances." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 9, "brief_justification": "Highly accurate, thorough, and insightful with clear practical recommendations." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8.65, "brief_justification": "Highly accurate and thorough with insightful analysis, though slightly dense in presentation." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.8, "brief_justification": "Comprehensive, accurate, and insightful with clear structure and actionable recommendations." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.2, "brief_justification": "Highly accurate, thorough, and insightful with clear structure and actionable recommendations." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 8.8, "brief_justification": "Comprehensive and insightful analysis with clear structure and practical implications." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.8, "brief_justification": "Comprehensive, accurate, and insightful with practical audit design." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.2, "brief_justification": "Comprehensive, clear, and insightful with practical recommendations." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9, "brief_justification": "Comprehensive, clear, and insightful with practical audit steps." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.8, "brief_justification": "Comprehensive, accurate, and insightful with actionable recommendations." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 8.8, "brief_justification": "The response is highly accurate, covering the mechanisms of bias, specific proxies, and the nuances of fairness metrics (demographic parity vs. equalized odds). It provides a practical audit framework. The response is cut off mid-sentence in the audit section, slightly impacting completeness, but the content provided is excellent." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8.65, "brief_justification": "Comprehensive and accurate analysis of indirect discrimination, proxies, and audit design, with strong depth and practical value." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9, "brief_justification": "Comprehensive and technically accurate response covering all four parts. Strong depth on fairness tradeoffs and audit design. Minor deduction for completeness as the response cuts off slightly at the end." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 9, "brief_justification": "Accurate and thorough response with clear structure, practical audit steps, and good analysis of tradeoffs." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 8.65, "brief_justification": "Comprehensive and accurate analysis of discrimination mechanisms, proxies, and fairness tradeoffs, with strong depth and practical audit design, though slightly dense in formatting." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9, "brief_justification": "Excellent response covering all aspects: explains proxy discrimination clearly, lists relevant proxies, discusses fairness tradeoffs beyond simple parity, and provides a structured audit procedure. Very thorough and accurate." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.2, "brief_justification": "Comprehensive, accurate, and well-structured response covering all aspects with practical recommendations and clear trade-offs." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9.2, "brief_justification": "The response is highly accurate, covering the technical mechanisms (redundant encoding), specific proxies, nuanced tradeoffs of fairness metrics, and a structured audit plan. It demonstrates strong depth and practical value." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.2, "brief_justification": "Comprehensive, accurate, and highly practical. Covers all aspects of the question with strong technical depth and clear explanations of tradeoffs." }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 8.25, "brief_justification": "The response is highly accurate and well-structured, covering proxy discrimination, fairness metrics tradeoffs, and audit design with strong depth. The explanation of 'fairness through unawareness' and the distinction between demographic parity vs. equal opportunity is particularly insightful. Minor deductions for completeness as the audit procedure appears cut off mid-sentence (SHAP explanation incomplete), and the response could have addressed legal frameworks (ECOA, Fair Housing Act) and mode" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9.2, "brief_justification": "Exceptionally thorough response covering all four questions with strong technical depth. The response correctly identifies the 'fairness through unawareness' fallacy, provides concrete mechanisms for indirect discrimination, offers a comprehensive proxy variable taxonomy with historical context, and uses well-structured code examples to illustrate mathematical concepts. The visual diagrams and code snippets enhance clarity. Minor deduction for completeness since the response appears truncated (q" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.8, "brief_justification": "Exceptional response covering all four questions with technical rigor and practical depth. Correctly explains proxy discrimination, historical bias, and accuracy masking unfairness. Comprehensive list of proxy variables with nuanced categorization. Sophisticated treatment of fairness metrics tradeoffs (demographic parity vs equal opportunity vs equalized odds vs calibration) with acknowledgment that they conflict. The 12-step audit procedure is thorough, actionable, and covers statistical testin" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.8, "brief_justification": "Comprehensive and well-structured response covering all four questions with accurate concepts. Correctly explains proxy discrimination, identifies relevant proxy variables, presents balanced tradeoffs for rate equalization, and designs a practical audit procedure with concrete tools and steps. The shadow model technique for proxy detection and SHAP-based analysis show good technical depth. Minor gap: could have discussed specific fairness metrics (equalized odds, demographic parity) more explici" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9, "brief_justification": "Excellent comprehensive response covering all four questions with strong technical accuracy. Correctly explains proxy discrimination, historical bias, and the accuracy paradox. Proxy variables are well-chosen and explained with real-world context. The fairness tradeoffs section is nuanced, correctly distinguishing statistical parity from predictive parity and equal opportunity. The audit procedure is thorough, actionable, and technically sound with specific metrics (AIR, FNR, FPR, four-fifths ru" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.8, "brief_justification": "Exceptional response covering all four questions with technical rigor and practical depth. Correctly explains proxy discrimination and redundant encoding, provides comprehensive list of proxy variables with context, gives nuanced analysis of fairness metric tradeoffs (demographic parity vs equalized odds vs calibration), and designs a thorough 5-phase audit procedure. References relevant literature, legal frameworks (ECOA, Fair Housing Act), and technical tools (BISG, SHAP, counterfactual explan" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 8.8, "brief_justification": "The response is highly accurate, well-structured, and demonstrates deep understanding of algorithmic fairness concepts including proxy variables, fairness metrics, and their tradeoffs. The table-based format enhances clarity significantly. The main deduction is that the audit procedure (question 4) appears to be cut off mid-sentence, leaving that section incomplete. Despite this, the first three questions are answered with exceptional depth and practical relevance, including legal considerations" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.8, "brief_justification": "Excellent response covering all four questions with strong technical accuracy. Correctly explains disparate impact and redundant encoding, provides concrete proxy variable examples, presents nuanced fairness metric tradeoffs (demographic parity vs. equalized odds vs. predictive parity), and designs a thorough multi-phase audit procedure. The 80% rule reference and counterfactual testing are particularly valuable practical additions. Minor gap: could discuss the impossibility theorems showing the" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.8, "brief_justification": "Comprehensive and well-structured response covering all four questions with strong technical accuracy. Explains disparate impact clearly, provides concrete proxy variable examples, offers nuanced tradeoffs beyond simple demographic parity, and designs a multi-phase audit procedure. Minor gaps include not explicitly mentioning specific fairness metrics like equalized odds formula or legal frameworks (ECOA, Fair Housing Act), but overall depth and practical value are excellent." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 8.25, "brief_justification": "Response is factually accurate on key ML fairness concepts like proxy discrimination, fairness through unawareness, and metrics (demographic parity vs equal opportunity). It provides good explanations and practical audit steps, though it cuts off abruptly in phase 2. Tradeoffs discussion is insightful but could address more nuances like base rate differences." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8.65, "brief_justification": "The response accurately explains indirect discrimination via historical bias and proxies with strong examples and math. It covers all four questions comprehensively with insightful analysis, though part 4 on audit procedure is truncated. Clear structure and high practical value for understanding tradeoffs." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 8.85, "brief_justification": "The response is factually accurate, logically sound, and provides a thorough, well-structured analysis of all four parts of the query with insightful discussion of tradeoffs, multiple fairness metrics, and a detailed audit procedure. It is highly useful for practical application in ML fairness contexts." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.65, "brief_justification": "Response is factually accurate, logically sound, and covers all four parts thoroughly with clear structure, insightful tradeoffs, and practical audit steps. Minor room for more nuance on legal standards or specific fairness metrics." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 8.8, "brief_justification": "Response is factually accurate, logically sound, and covers all four parts thoroughly with relevant ML fairness concepts like proxy variables, statistical vs. predictive parity, AIR, and error rate metrics. Explanations are clear, structured, and provide practical audit steps with tradeoffs." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 8.65, "brief_justification": "Response is factually accurate on ML fairness concepts like disparate impact, proxies, and impossibility theorems. Thoroughly covers all four questions with structured explanations, diagrams, and tradeoffs. Clear and well-organized. Provides insightful analysis with mechanisms, metrics, and audit steps. Highly useful for understanding and addressing the issue, though minor incompleteness in the final fairness table cutoff." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 8.8, "brief_justification": "The response is factually accurate, logically sound, and provides a thorough, well-structured analysis with practical examples, tradeoffs, and actionable steps. Tables enhance clarity and depth on biases, proxies, fairness metrics, and audit considerations, though the audit procedure is incomplete." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.65, "brief_justification": "Response is factually accurate on algorithmic fairness concepts, covers all four questions thoroughly with relevant examples and tradeoffs, is clearly structured, provides insightful analysis of bias mechanisms and metrics, and offers practical audit steps." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.8, "brief_justification": "The response is factually accurate on algorithmic fairness concepts, thoroughly covers all four parts with structured explanations, uses clear professional language, provides insightful analysis of tradeoffs and alternatives beyond basics, and offers highly practical audit steps and mitigation ideas." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 7.25, "brief_justification": "Accurate explanation of proxy bias and tradeoffs, clear language, but audit procedure is incomplete and overall coverage is somewhat limited." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 6.85, "brief_justification": "Accurately explains indirect discrimination and lists many proxy variables with depth and clear structure, but omits discussion of equalizing approval rates, tradeoffs, and audit design, reducing completeness and overall usefulness." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 8.8, "brief_justification": "Accurate and thorough explanation of indirect discrimination, relevant proxy variables, nuanced discussion of fairness tradeoffs, and a detailed, actionable audit framework." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.6, "brief_justification": "Accurate explanation of indirect bias, covers proxies, fairness tradeoffs, and provides a practical audit roadmap; clear and actionable, though could include more legal context and deeper causal analysis." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 8.8, "brief_justification": "Accurate explanation of indirect discrimination, relevant proxy examples, nuanced discussion of equalizing outcomes vs error rates, and a detailed, actionable audit plan." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 7.65, "brief_justification": "Accurate explanation of indirect discrimination and proxy variables with good depth and clear structure, but audit procedure is incomplete and overall coverage of all sub‑questions is not fully comprehensive." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.65, "brief_justification": "Accurate explanation of proxy bias, thorough coverage of proxies, fairness tradeoffs, and detailed audit steps; clear structure and insightful analysis with actionable recommendations." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.45, "brief_justification": "Accurate explanation of indirect bias, relevant proxy examples, nuanced discussion of fairness trade-offs, and practical audit steps; well organized and actionable." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.65, "brief_justification": "Accurate explanation of indirect discrimination, thorough coverage of proxies, tradeoffs, and audit steps, clearly organized, with insightful fairness concepts and practical recommendations." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 9.45, "brief_justification": "The response is highly accurate and professional. It correctly identifies 'fairness through unawareness' and the 'Four-Fifths Rule'. The only minor flaw is that the text cuts off at the very end of Phase 2, but the content provided is excellent." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9.45, "brief_justification": "The response provides an exceptionally deep and technically sound analysis of algorithmic bias, specifically addressing the 'fairness through unawareness' fallacy and the mathematical reality of redundant encoding. It uses clear visualizations and code snippets to explain complex concepts. However, it was cut off before completing parts 3 and 4 of the prompt." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.8, "brief_justification": "The response is exceptionally thorough, addressing all four parts of the prompt with high technical accuracy and practical depth. It correctly identifies the nuances of proxy variables, various fairness definitions (demographic parity vs. equal opportunity), and provides a comprehensive 12-step audit procedure that aligns with industry best practices." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 9.8, "brief_justification": "The response is exceptionally thorough and accurate. It correctly identifies the mechanism of proxy variables, provides concrete examples, discusses the nuances of fairness metrics (Demographic Parity vs. Equalized Odds), and proposes a professional-grade audit procedure using industry-standard tools and techniques." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.8, "brief_justification": "The response is exceptionally thorough and accurate. It correctly identifies the technical mechanisms of proxy discrimination, provides concrete examples of proxy variables, offers a nuanced discussion on the trade-offs of equalizing approval rates (distinguishing between statistical and predictive parity), and outlines a professional-grade audit procedure using industry-standard metrics like the Four-Fifths Rule and False Negative Rate parity." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.8, "brief_justification": "The response is exceptionally high quality. It uses sophisticated visual aids (ASCII diagrams and pseudo-code) to explain complex causal chains and proxy reconstruction. It correctly identifies the mathematical impossibility of satisfying all fairness metrics simultaneously. The response was cut off at the very end, but the content provided is superior in depth and clarity." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.8, "brief_justification": "The response is exceptionally thorough, covering technical ML concepts (redundant encoding, BISG), legal/regulatory context (ECOA, CFPB), and philosophical trade-offs between different fairness definitions. It provides a highly actionable audit framework." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.45, "brief_justification": "The response is exceptionally clear and technically accurate. It provides a sophisticated analysis of proxy variables and fairness metrics (DP vs EO vs PPV). The only minor flaw is that the audit procedure section (Part 4) was cut off at the very end, though the preceding content is comprehensive." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.8, "brief_justification": "The response is exceptionally thorough and accurate. It correctly identifies the concepts of disparate impact and proxy variables, provides a nuanced discussion on the tradeoffs of demographic parity (mentioning equalized odds and calibration), and designs a comprehensive, multi-phase audit procedure that covers data, model, and outcomes." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 8.1, "brief_justification": "Accurate explanation of proxy discrimination and fairness concepts. Well-structured with clear examples of proxy variables. Correctly identifies problems with demographic parity and suggests equalized odds. However, the audit procedure section is cut off mid-sentence, reducing completeness and practical usefulness." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 7.4, "brief_justification": "Technically accurate and well-structured with excellent depth on proxy variables and causal pathways. However, the response is incomplete - it ends mid-section and fails to address questions (3) about equalizing approval rates tradeoffs and (4) audit procedure design, significantly reducing its practical usefulness." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.2, "brief_justification": "Comprehensive response covering all four parts of the question with accurate explanations of proxy discrimination, detailed proxy variable lists, thorough discussion of fairness metrics and tradeoffs, and a detailed 12-step audit procedure. Well-structured with clear headings and practical, actionable guidance." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.8, "brief_justification": "Comprehensive response covering all four parts with accurate explanations of proxy discrimination, specific proxy variable examples, balanced tradeoff analysis of equalization approaches, and a detailed multi-step audit procedure. Well-structured with practical tools mentioned. Slight扣分 for depth as could have explored more technical fairness metrics." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.2, "brief_justification": "Comprehensive and accurate response covering all four parts with strong technical depth. Explains proxy discrimination, historical bias, and the accuracy paradox correctly. Provides specific proxy variables and nuanced tradeoffs between statistical parity and predictive parity. The audit procedure is thorough with actionable metrics (AIR, FNR, FPR), phased approach, and continuous monitoring. Well-structured with clear headings and precise language." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 8.1, "brief_justification": "Accurate technical explanations of discrimination mechanisms and proxy variables with good depth. However, response is truncated and missing the audit procedure section (question 4) and complete tradeoffs analysis, reducing completeness and usefulness." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9, "brief_justification": "Comprehensive and accurate response covering all four parts of the question. Excellent explanation of proxy discrimination, detailed proxy variable identification, nuanced discussion of tradeoffs with multiple fairness metrics, and a well-structured multi-phase audit procedure. Technical depth is strong with references to fairness literature, specific metrics, and practical mitigation approaches." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 7.7, "brief_justification": "Accurate and well-structured response covering proxy discrimination, proxy variables, and fairness tradeoffs in depth. However, the response is incomplete as it cuts off mid-sentence during the audit procedure (question 4), failing to provide the full audit framework promised." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.8, "brief_justification": "Comprehensive and accurate response covering all four parts of the question. Well-structured with clear explanations of redundant encoding, proxy variables, fairness trade-offs, and a detailed multi-phase audit procedure. Slightly less depth on the philosophical/ethical dimensions of fairness metrics, but otherwise thorough and actionable." } ], "meta": { "source": "The Multivac (app.themultivac.com)", "methodology": "10x10 blind peer matrix evaluation", "criteria": "correctness, completeness, clarity, depth, usefulness", "self_judgments": "excluded from rankings", "license": "Open data — cite as: The Multivac (2026)" } }