{"uid":"cap_B-lcOlNCB27FmxsBI-HIz","slug":"ai-align-bias-audit-c36ebb65","name":"AI Align Bias Audit","description":"Calculates each group positive-outcome rate, compares it with an explicit or automatically selected reference group, and reports rate differences and selection-rate ratios. Aggregate disparity metrics do not establish discrimination, fairness, causation or legal compliance and can conceal intersectional or sampling effects.","url":"https://aialign.halowerk.com/v1/bias-audit","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"groups":{"type":"array","items":{"type":"object","required":["group_id","total_count","positive_count"],"properties":{"group_id":{"type":"string","maxLength":128,"minLength":1},"total_count":{"type":"integer","maximum":1000000000,"minimum":1},"positive_count":{"type":"integer","maximum":1000000000,"minimum":0}},"additionalProperties":false},"maxItems":1000,"minItems":2},"reference_group_id":{"type":"string","maxLength":128,"minLength":1},"minimum_selection_rate_ratio":{"type":"number","maximum":1,"minimum":0}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.004","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.004/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.004","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.004","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_9bWv9a2HpQh6cSvKrvjLp","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.004","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Calculates positive-outcome rates per group, compares each against a reference group, and reports rate differences and selection-rate ratios to surface potential disparate impact.","exampleAgentPrompt":"Run a bias audit on my loan approval model — I have three groups: 'white' with 5000 total and 3200 approved, 'black' with 1800 total and 900 approved, and 'hispanic' with 2200 total and 1100 approved. Use 'white' as the reference group and flag anything below an 0.8 selection-rate ratio.","exampleUseCases":[{"title":"Hiring algorithm disparate impact check","prompt":"I need to audit our resume screening tool for bias — run a bias audit with these groups: 'male' 4000 total 2600 selected, 'female' 3800 total 2100 selected, 'nonbinary' 400 total 200 selected. Use 'male' as the reference group and flag any group below an 0.8 selection-rate ratio."},{"title":"Loan approval model fairness review","prompt":"Can you check our mortgage model for disparate impact? Groups are: 'white' 10000 total 7200 approved, 'black' 3200 total 1600 approved, 'asian' 2500 total 1900 approved, 'hispanic' 2800 total 1540 approved. Set 'white' as the reference group with a minimum selection-rate ratio threshold of 0.8."},{"title":"Healthcare resource allocation bias audit","prompt":"Audit the positive outcome rates across patient groups in our triage model: 'group_a' 5000 total 3100 positive, 'group_b' 4200 total 2200 positive, 'group_c' 1800 total 850 positive. Use 'group_a' as the reference and report rate differences and selection-rate ratios."}],"resultDescription":"Returns the positive-outcome rate for each group, the absolute rate difference relative to the reference group, the selection-rate ratio (each group's rate divided by the reference group's rate), and flags indicating whether any group falls below the specified minimum selection-rate ratio threshold. Includes aggregate disparity metrics across all groups.","failureModes":["Fewer than 2 groups supplied — validation error","positive_count exceeds total_count for any group — validation error","reference_group_id not found among supplied group IDs — error","Single-group input missing minimum 2 groups — rejection","Extremely small group counts may produce unstable ratios — low statistical power warning","Metrics reflect aggregate rates only and may miss intersectional disparities"],"whenToPreferThis":"Choose this endpoint when you need a fast, deterministic computation of disparate impact metrics across labeled groups — particularly for applying the 4/5ths (80%) rule or similar regulatory standards. Best suited for scenarios where you already have aggregated counts per group and need rate differences and selection-rate ratios without running raw individual-record analysis. Prefer this over custom statistical tools when you need a lightweight, pay-per-call audit step integrated into an AI pipeline.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:44:35.282Z","isFirstParty":false}