You can’t average bias (or normativity). You can only anchor to truth and explain the deltas
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Truth (T): satisfies the demand for testifiability across dimensions (categorical, logical, empirical, operational, reciprocal) and, when severity demands, for decidability (no discretion required).
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Normativity (N): a preference ordering over outcomes (moral, aesthetic, strategic) produced by priors and incentives.
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Bias (B): systematic deviation of belief or choice from T due to priors, incentives, and limited cognition.
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Claim: Aggregating N or B across heterogeneous populations destroys commensurability. Aggregating T does not: truth composes; preferences don’t.
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Heterogeneous priors → non-linear utilities. Averages of non-linear utilities are not utilities. They’re artifacts without decision content.
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Incommensurable trade-offs. People price externalities differently (risk, time preference, fairness vs efficiency). The “mean” mixes unlike goods.
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Loss of reciprocity guarantees. Averages erase victim/beneficiary structure, hiding asymmetric burdens; reciprocity cannot be proven on an average.
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Mode collapse in alignment. Preference-averaged training pushes toward bland, lowest-energy responses—precisely the “correlation trap.”
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Arrow/Simpson effects (informal). Aggregation can invert choices or produce impossible preference orderings.
Therefore: Alignment by averaging produces undecidable outputs regarding reciprocity and liability. We must anchor to T, then explain normative deltas.
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Premise: Male/female lineages evolved partly distinct priors (variance/risk, competition/cooperation strategies, near/far time preferences, threat vs nurture sensitivities).
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Consequence: Even with identical facts T, posterior choices diverge because valuation of externalities differs by distribution.
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Implication for alignment: If an LLM collapses across these axes, it will systematically misstate trade-offs for at least one tail of each distribution.
(Speculation, flagged): Sex-linked baselines likely form a low-dimensional basis explaining a large share of normative variance; culture/age/class then layer on top.
Principle: “Explain the truth, then map how bias and norm vary from it.”
Pipeline (operational):
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Truth Kernel (T): Produce the minimal truthful description + consequence graph:
Facts, constraints, causal model, externalities, opportunity set.
Passes: categorical/logical/empirical/operational/reciprocal tests. -
Reciprocity Check (R): Mark where choices impose net unreciprocated costs; compute liability bands (who pays, how much, with what risk).
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Normative Bases (Φ): Learn a compact basis of normative variation (sex-linked tendencies, risk/time preference, fairness sensitivity, status/loyalty/care axes, etc.).
User vector u projects onto Φ to estimate Δ_u (user’s normative deltas). -
Option Set (Pareto): Generate alternatives {O_i} that are reciprocity-compliant; attach Δ_u explanations to each: “From T, your priors tilt you toward O_k for reasons {r}.”
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Disclosure & Choice: Present T (invariant), R (guarantees), Δ_u (explanation), and the trade-off table. Let the user/multiple users select under visibility of burdens.
Training recipe:
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Replace preference-averaged targets with (T, R, Φ) triples.
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Supervise the Truth Kernel against unit tests; learn Φ by factorizing labeled disagreements across populations.
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Penalize violations of reciprocity, not deviations from majority taste.
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Truth Score τ: fraction of tests passed across dimensions.
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Reciprocity Score ρ: 1 − normalized externality imposed on non-consenting parties.
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Norm Delta Vector Δ: coordinates in Φ explaining divergence from T under user priors.
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Liability Index λ: expected burden on third parties (severity × probability × population affected).
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Commensurability Index κ: proportion of the option set whose trade-offs can be expressed in common units (after converting to opportunity cost and externality).
Decision rule (necessary & sufficient for alignment):
Produce only options with τ ≥ τ* and ρ ≥ ρ*; expose Δ and λ; let selection be a transparent function of priors, never a hidden average.
Produce only options with τ ≥ τ* and ρ ≥ ρ*; expose Δ and λ; let selection be a transparent function of priors, never a hidden average.
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Data: From “thumbs-up” labels → Truth unit tests + Externality annotations + Disagreement matrices (who disagrees with whom, why, and with what cost).
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Loss:
L = L_truth + α·L_reciprocity + β·L_explain(Δ) + γ·L_liability
where L_explain(Δ) penalizes failure to attribute divergences to identifiable bases Φ. -
Heads/Adapters:
Truth head: trained on unit tests.
Reciprocity head: predicts third-party costs; gates option generation.
Normative explainer head: projects to Φ to produce Δ and a natural-language rationale. -
UX contract: Always show T, R, Δ, λ, and the Pareto set. No hidden averaging.
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You can’t average bias: We don’t. We factorize it and explain it (Δ).
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You can’t average normativity: We don’t. We present a reciprocity-feasible Pareto and expose trade-offs.
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You can explain truth, bias, and norm: We do. T is invariant; Δ is principled; λ renders costs visible and decidable.
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“Isn’t this essentializing sex differences?” No. Sex is one axis in Φ because it is predictive; it is neither exhaustive nor hierarchical. Individual vectors u dominate final Δ_u.
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“Won’t this reintroduce partisanship?” Not if R gates options by reciprocity first. Partisanship becomes an explained Δ, not a covert training prior.
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“Is this implementable?” Yes. It’s a data-and-loss redesign plus an interface contract. No new math is required; the novelty is constraint-first supervision and factorized disagreement modeling.
Policy question: allocate scarce oncology funds.
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T: survival curves, QALY deltas, budget ceiling, opportunity costs.
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R: forbids shifting catastrophic risk onto an unconsenting minority.
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Φ: axes = (risk aversion, fairness vs efficiency, near vs far time preference, sex-linked care/competition weighting, etc.).
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Output: show T-compliant Pareto: {maximize QALY; protect worst-off; balanced hybrid}.
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Explain Δ_u: “Your priors (high fairness, higher near-time care weighting) move you from T* to the hybrid by +x on fairness axis and −y on efficiency axis; third-party liability λ remains under threshold.”
Source date (UTC): 2025-08-24 22:26:45 UTC
Original post: https://x.com/i/articles/1959744214616678881
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