{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"deadweight_loss_reduction__tech_ethics_ai_governance","trajectory_id":"R","attempt_index":0,"archetype_slug":"deadweight_loss_reduction","domain_slug":"tech_ethics_ai_governance","decision":"CANDIDATE","problem_id":"uniform_ai_review_blocks_low_risk_changes","causal_lever_id":"risk_tiered_ai_review_with_sunset","proposal":{"problem":"A uniform, full-depth governance review for every AI-system change can make low-risk, reversible changes wait behind high-risk deployments. When review depth is unrelated to plausible harm, beneficial fixes and evaluations are delayed without necessarily improving protection. Whether this avoidable wedge is material is a HYPOTHESIS to test, not an assumption that AI oversight is inherently inefficient.","actors_substrate":["AI governance and risk-review body","model owners and developers","operators and internal users","people subject to AI-supported decisions","privacy, security, legal, safety, and civil-rights reviewers","organizational leadership and regulators where applicable"],"observable_state":"Low-risk and high-risk changes enter substantially the same approval path; queues and repeated handoffs delay low-risk changes, while reviewer capacity remains occupied. The existence, magnitude, and harm-neutrality of this pattern are HYPOTHESES requiring case-level measurement.","consequence":"Potentially beneficial bug fixes, accessibility improvements, monitoring changes, and bounded evaluations are deferred or abandoned; teams may also bundle changes or seek informal workarounds, while genuinely consequential reviews compete for limited attention.","affected_objective":"Timely realization of beneficial AI changes while preserving privacy, security, safety, fairness, contestability, and lawful governance.","structural_mapping":[{"archetype_element":"Value-blocking wedge","domain_realization":"A coarse approval rule imposes full review depth on changes whose plausible exposure and reversibility differ materially.","claim_kind":"HYPOTHESIS"},{"archetype_element":"Blocked mutually beneficial activity","domain_realization":"Some delayed changes would benefit operators or affected people without weakening protected outcomes.","claim_kind":"HYPOTHESIS"},{"archetype_element":"Protected purpose","domain_realization":"Governance review protects affected people from safety, discrimination, privacy, security, rights, and accountability failures.","claim_kind":"CORPUS"},{"archetype_element":"Less-distortive redesign","domain_realization":"A risk-tiered path could preserve mandatory checks and escalation triggers while shortening only procedural handling for eligible cases.","claim_kind":"INFERENCE"},{"archetype_element":"Incidence and rebound monitoring","domain_realization":"Measure gains, reviewer displacement, classification errors, cumulative changes, gaming, and harm indicators by affected group.","claim_kind":"INFERENCE"}],"component_map":[{"component":"Distortion Map","status":"direct","domain_realization":"Trace intake, handoffs, duplication, queue time, substantive review time, abandonment, and workarounds by change class."},{"component":"Protected Constraint Safeguard","status":"direct","domain_realization":"Keep mandatory privacy, security, discrimination, safety, rights, documentation, and escalation checks."},{"component":"Surplus Estimate","status":"adapted","domain_realization":"Estimate avoided delay, reviewer capacity recovered, beneficial changes completed, and affected-person benefit with uncertainty ranges."},{"component":"Affected-Party Incidence Map","status":"direct","domain_realization":"Record effects on subjects, users, developers, reviewers, leadership, and oversight authorities."},{"component":"Redesign Lever","status":"direct","domain_realization":"Use risk-tiered intake, parallel checks, data reuse, decision deadlines, and automatic escalation."},{"component":"Distributional Review","status":"direct","domain_realization":"Test whether faster processing shifts error or harm toward less powerful or historically burdened groups."},{"component":"Behavioral Response Model","status":"direct","domain_realization":"Anticipate under-classification, change splitting, bundling, route shopping, increased submissions, and reviewer adaptation."},{"component":"Implementation Boundary","status":"direct","domain_realization":"Limit the pilot to reversible, internally contained changes meeting enumerated exposure criteria."},{"component":"Monitoring and Rebound Check","status":"direct","domain_realization":"Track cycle time, backlog, escalations, incidents, complaints, overrides, cumulative exposure, and subgroup outcomes."},{"component":"Rollback or Adjustment Rule","status":"direct","domain_realization":"Suspend the fast path and restore ordinary review when harm, misclassification, or gaming thresholds are crossed."},{"component":"Cost–Benefit Assessment Frame","status":"adapted","domain_realization":"Compare recovered time and capacity with transition cost, missed-hazard risk, and non-monetizable rights impacts."},{"component":"Price-Wedge Diagnostic","status":"incompatible","domain_realization":"No access price is posited; the candidate concerns approval scope and delay."},{"component":"Friction Source Breakdown","status":"direct","domain_realization":"Separate substantive scrutiny from redundant requests, serial routing, unclear ownership, and idle queue time."},{"component":"Compensating Adjustment Plan","status":"adapted","domain_realization":"If burdens shift, add targeted review capacity, notification, appeal, audit sampling, or narrower eligibility rather than monetary compensation."},{"component":"Legitimacy and Authority Review","status":"direct","domain_realization":"Confirm who may alter workflow and which legal or policy checks cannot be waived."},{"component":"Sensitivity Analysis","status":"direct","domain_realization":"Vary benefit valuation, incident severity, classification-error rates, and attributed delay."},{"component":"Pilot or Sunset Path","status":"direct","domain_realization":"Run an expiring pilot that reverts unless evidence supports renewal."}],"mechanism_dispositions":[{"slug":"congestion_or_capacity_pricing_adjustment","disposition":"incompatible","contribution_type":"NONE","adaptation_or_rejection":"Reviewer congestion exists, but pricing access could privilege well-resourced teams and does not match the approval wedge.","counterfactual_removal":"No change; the causal chain does not use prices."},{"slug":"cost_benefit_assessment_protocol","disposition":"selected_supporting","contribution_type":"TEST_DESIGN","adaptation_or_rejection":"Use a rights-constrained, uncertainty-sensitive comparison rather than monetizing all harms.","counterfactual_removal":"The pilot could run, but its net-value and distributional case would be materially weaker."},{"slug":"distortion_reduction_review","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Separates procedural drag from substantive protection and tests whether recoverable loss exists.","counterfactual_removal":"There would be no defensible basis for calling the delay avoidable rather than protective."},{"slug":"impact_assessment_table","disposition":"selected_supporting","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Registers party-specific effects, protected interests, uncertainty, and triggers.","counterfactual_removal":"Aggregate speed gains could conceal concentrated harm."},{"slug":"matching_improvement_program","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"The hypothesized failure is a coarse approval path, not inability to pair willing parties.","counterfactual_removal":"No change."},{"slug":"permit_or_approval_streamlining","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Risk-tier the workflow while ring-fencing substantive checks.","counterfactual_removal":"The specific process lever connecting diagnosis to reduced delay disappears."},{"slug":"price_control_redesign","disposition":"incompatible","contribution_type":"NONE","adaptation_or_rejection":"No administered price, shortage-producing cap, or subsidy is identified.","counterfactual_removal":"No change."},{"slug":"quota_or_allocation_rule_review","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Reviewer capacity is scarce, but no quota, entitlement, or fixed allocation rule is the proposed cause.","counterfactual_removal":"No change."},{"slug":"regulatory_simplification_pilot","disposition":"selected_load_bearing","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Cage the faster path by eligibility, duration, monitoring, and automatic expiry.","counterfactual_removal":"The redesign would lack the bounded reversibility required under uncertain AI harms."},{"slug":"sunset_clause_review","disposition":"selected_supporting","contribution_type":"OPERATIONAL","adaptation_or_rejection":"Apply sunset to the pilot pathway, requiring affirmative renewal; do not default the protective regime itself to lapse.","counterfactual_removal":"The test remains possible, but experimental relaxation could persist through inertia."},{"slug":"tariff_fee_or_toll_redesign","disposition":"incompatible","contribution_type":"NONE","adaptation_or_rejection":"No authority-imposed monetary charge is causal.","counterfactual_removal":"No change."}],"causal_chain":["Uniform review scope exposes low-risk changes to procedural steps designed for higher-risk deployments.","Those steps consume scarce reviewer time and increase queueing, rework, uncertainty, and abandonment.","A bounded risk-tiered path removes only measured procedural drag while retaining mandatory checks and escalation triggers.","Eligible changes complete sooner and reviewer capacity shifts toward consequential cases.","Monitoring detects classification errors, behavioral gaming, rebound volume, and distributional or rights harms.","Threshold breaches halt the fast path; favorable evidence at sunset permits adjustment or renewal."],"baseline":"All changes follow the existing full review path, with prioritization handled informally or case by case.","nearest_rival":"Add reviewer staffing or workflow software while retaining uniform review depth. This addresses capacity or transaction cost without testing whether the coarse rule itself creates avoidable loss.","authority_safety":{"affected_parties":["people subject to affected AI systems","internal users and operators","developers and model owners","governance reviewers","privacy, security, legal, safety, and civil-rights functions","external oversight bodies where applicable"],"decision_authority":"The body formally authorized to set the AI review workflow, subject to non-waivable legal, regulatory, contractual, and board-level controls.","authorized_first_step":"Audit a retrospective sample, then run one time-limited fast-path pilot for reversible internal changes with no new sensitive data, no autonomous high-impact decision, low exposure, named owner, ordinary documentation, audit sampling, and automatic escalation.","excluded_actions":["waiving legally mandatory review","including employment, credit, health, policing, benefits, or other high-impact decisions","including irreversible deployment or materially expanded population exposure","removing appeal, incident reporting, audit access, or human override","treating faster approval as evidence of safety","expanding the pilot before its sunset review"],"halt_rollback":"Immediately suspend new fast-path approvals and return pending cases to ordinary review if a serious incident occurs, protected checks are bypassed, predefined subgroup-harm or complaint thresholds are crossed, classification error exceeds tolerance, or material gaming appears; preserve records and reassess eligibility."}},"negative_tests":{"strongest_counterevidence":"Uniform review may be necessary because seemingly minor AI changes can alter system behavior, combine cumulatively, or evade reliable ex ante risk classification. If substantive review—not procedural drag—accounts for most elapsed time or catches material hazards at an appreciable rate, streamlining is poorly supported.","analogy_break":"AI governance is not an ordinary market exchange: affected people may neither consent nor capture the benefits, harms may be uncertain and irreversible, and rights cannot be offset by aggregate surplus. Review delay is therefore not deadweight loss unless equivalent protection is demonstrated.","failure_condition":"The candidate fails if no separable procedural wedge exists, expedited cases cannot be bounded reliably, or retained safeguards cannot keep exposure within the authority's accepted risk limits.","problem_falsifier":"Case-level data show that eligible low-risk changes are not materially delayed or abandoned by uniform review, or that the allegedly redundant steps regularly identify consequential hazards unavailable through the proposed retained checks.","intervention_falsifier":"Compared with ordinary review, the pilot produces no meaningful cycle-time or backlog improvement, displaces delay elsewhere, increases missed hazards or subgroup harm beyond preset bounds, induces material route gaming, or cannot operate without weakening protected checks.","risks":["Teams understate risk to qualify.","Many small changes create cumulative exposure.","Speed metrics crowd out safety and rights outcomes.","Pilot cases are unrepresentatively easy.","Recovered capacity triggers rebound submissions.","Distributional harms are hidden by aggregate benefits.","Temporary simplification becomes permanent without evidence."]},"null_rationale":null,"classification":{"candidate_kind":"MECHANISM_COMPOSITION","prior_art_status":"UNSEARCHED","evidence_maturity":"HYPOTHESIS"},"revision_change_log":{"revision_kind":"ORIGINAL","prior_problem_id":null,"prior_causal_lever_id":null,"problem_changed":false,"causal_lever_changed":false,"conceptual_changes":[],"operational_changes":[],"repairs_addressed":[]},"confidence":0.82,"generator_notes":"Closed-book structural inference from the supplied packet. The proposal does not claim that current AI review queues exhibit the hypothesized distortion; diagnosis precedes redesign, and prior art remains unsearched."}