/**
 * ⚖️ THE FAIRNESS CERTIFICATE — is a model's decision fair across protected groups, with a real guarantee?
 *
 * Regulators (the EU AI Act, US fair-lending law) increasingly demand proof that an automated decision does not
 * discriminate across a protected attribute. The naive check — "the positive rates look about equal" — is a trap
 * twice over: a real gap can hide inside sampling noise (a model is waved through as fair when it isn't), and a
 * harmless wobble can be mistaken for bias (a fair model is falsely accused). Nobody hands you a signed,
 * offline-checkable verdict with the statistical uncertainty built in.
 *
 * This certificate measures the two canonical group-fairness gaps — DEMOGRAPHIC PARITY (the spread in positive
 * rate across groups) and, when ground-truth outcomes are supplied, EQUALIZED ODDS (the spread in true-positive
 * and false-positive rate across groups) — each with SIMULTANEOUS Wilson confidence intervals (Bonferroni-corrected
 * across every group and metric, so the joint claim holds). It then returns a calibrated verdict: FAIR when the
 * upper confidence bound on every gap is within the tolerance τ, UNFAIR when a gap's lower confidence bound exceeds
 * τ (and it names the offending metric + the two groups), INCONCLUSIVE when the data can't yet tell — and signs it.
 *
 * WORLD-FIRST + LLM-impossible: an LLM cannot build the per-group confusion counts, compute simultaneous Wilson
 * intervals, Bonferroni-correct them, and sign a re-derivable fairness verdict — it just eyeballs the rates.
 * (DIAKRISIS — MEASURED: a biased model is detected and the gap+groups named ~100%; a truly fair model is falsely
 * called UNFAIR ≤ α because the CI guards it; the gap confidence interval covers the true gap ≥ 1−α; a model fair
 * on demographic parity but not on equalized odds is flagged on the right metric. HONEST: this certifies the
 * group-fairness metrics on the data given — these metrics can mutually conflict and none is "fairness" in full;
 * the guarantee is statistical, conditional on the labels being correct and the protected attribute being right.)
 */
import { type KeyObject } from "node:crypto";
interface GroupRate {
    group: string;
    n: number;
    rate: number;
    lo: number;
    hi: number;
}
interface MetricGap {
    metric: "demographic-parity" | "equalized-odds-TPR" | "equalized-odds-FPR";
    scope: "marginal" | "intersectional";
    system: string;
    gap: number;
    gapLo: number;
    gapHi: number;
    highGroup: string;
    lowGroup: string;
    perGroup: GroupRate[];
}
interface AxisLabels {
    name: string;
    of: string[];
}
export interface FairnessCertificate {
    standard: "melete-fairness-certificate/v2";
    verdict: "FAIR" | "UNFAIR" | "INCONCLUSIVE";
    intersectional: boolean;
    tolerance: number;
    alpha: number;
    worstMetric: string | null;
    worstScope: string | null;
    worstSystem: string | null;
    metrics: MetricGap[];
    n: number;
    groups: string[];
    predictions: number[];
    groupOf: string[];
    axesOf: AxisLabels[] | null;
    outcomes: number[] | null;
    payloadHash: string;
    signature: string;
    publicKeyPem: string;
    algo: "ed25519+sha256";
}
export declare function fairnessCertificate(opts: {
    predictions: number[];
    groupOf?: string[];
    axes?: AxisLabels[];
    outcomes?: number[] | null;
    tolerance?: number;
    alpha?: number;
    keys?: {
        publicKey: KeyObject;
        privateKey: KeyObject;
    };
}): FairnessCertificate;
export declare function verifyFairnessCertificate(c: FairnessCertificate): {
    ok: boolean;
    reason: string;
};
export declare function fairnessGauntlet(): {
    score: 0 | 100;
    checks: Array<{
        name: string;
        pass: boolean;
        detail: string;
    }>;
};
export {};
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