{"schema_version":1,"assessment_id":"eoa_inverse_innovation_exp03_opportunity320_20260801","source_experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"deadweight_loss_reduction__tech_ethics_ai_governance","archetype_slug":"deadweight_loss_reduction","domain_slug":"tech_ethics_ai_governance","title":"Risk-Tiered AI Change Review with Sunset and Escalation","opportunity_summary":"Test whether a bounded, expiring fast path for reversible, low-exposure internal AI changes can reduce separable procedural delay and redirect reviewer capacity without weakening mandatory privacy, security, safety, fairness, rights, or legal protections. The existence and materiality of the procedural wedge remain unverified.","adopter_authorizer":"The organizational body formally authorized to set the AI review workflow, subject to non-waivable legal, regulatory, contractual, and board-level controls.","scores":{"meaningful_impact":{"score":3,"rationale":"Faster bug fixes, accessibility improvements, monitoring changes, and bounded evaluations could benefit operators and affected people while freeing review capacity, but the packet supplies no evidence about delay prevalence, abandoned changes, recovered capacity, or protected-outcome effects."},"stakeholder_pull":{"score":2,"rationale":"The packet identifies developers, operators, reviewers, leadership, and affected people as stakeholders, but provides no expressed demand, complaints, queue measurements, adoption commitments, or evidence that any authorized body considers the problem material."},"incremental_advantage":{"score":3,"rationale":"Changing review depth could remove a procedural wedge that additional staffing or workflow software would leave intact, but no comparison establishes that uniform scope rather than capacity, necessary substantive review, or another bottleneck causes the delay."},"distinctiveness_plausibility":{"score":2,"rationale":"The risk-tiered path combines eligibility limits, retained checks, monitoring, escalation, rollback, and sunset, but prior art is explicitly unsearched, so distinctiveness relative to existing AI-governance review models is unsupported."},"technical_implementability":{"score":4,"rationale":"The intervention is a bounded workflow change with defined eligibility, named ownership, documentation, audit sampling, escalation, and suspension rules; implementation is plausible, although reliable classification and cumulative-change monitoring remain unresolved."},"adoption_authority_feasibility":{"score":4,"rationale":"The proposal identifies the formally authorized governance body and preserves non-waivable controls. Feasibility remains conditional on coordination among privacy, security, legal, safety, and civil-rights functions and any external oversight requirements."},"evidence_readiness":{"score":4,"rationale":"The proposal defines observable workflow states, eligible-case criteria, comparison against ordinary review, and falsifiers involving time, backlog, hazards, subgroup harm, gaming, and displaced delay. Actual case-record access and measurement quality are not established."},"safety_net_benefit":{"score":4,"rationale":"High-impact uses, irreversible deployments, sensitive-data expansion, and waiver of mandatory review are excluded, while audit access, human override, escalation, sunset, suspension, and record preservation constrain downside. Whether these controls reliably catch cumulative or misclassified risk is untested."},"scalability":{"score":3,"rationale":"A rule-based review path could be reused across changes and organizational units, but eligibility thresholds, authority structures, data systems, accepted risk limits, and legal controls may vary substantially, and rebound submissions could consume recovered capacity."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"50K_TO_250K","scope":"Retrospective case-level workflow audit comparing prospectively eligible low-risk changes with ordinary-review cases, including elapsed-time decomposition, rework, abandonment, hazards detected by each step, and protected-outcome review.","confidence":"LOW","assumptions":["A usable sample of review records exists and can be accessed internally.","The audit requires governance, data-analysis, legal, privacy, safety, and civil-rights labor.","No new live deployment is included.","Case reconstruction and data cleaning are material but do not require building a new enterprise system."]},"initial_deployment_startup":{"band_2026_usd":"50K_TO_250K","scope":"Design and configure the time-limited fast path, including eligibility rules, documentation templates, routing, escalation triggers, audit sampling, metric definitions, training, approvals, and rollback procedures.","confidence":"LOW","assumptions":["Existing workflow and record systems can be configured rather than replaced.","The pilot remains limited to reversible internal changes with low exposure.","Non-waivable reviews remain available through existing functions.","No major procurement or regulator-mandated system certification is required."]},"operational_launch":{"band_2026_usd":"250K_TO_1M","scope":"Operate and evaluate one time-limited pilot, including dual-path comparison support, case classification, reviewer participation, monitoring for hazards and subgroup effects, gaming detection, incident handling, independent evaluation, and sunset review.","confidence":"LOW","assumptions":["The pilot covers one organization or bounded business unit rather than an enterprise-wide rollout.","Protected checks and ordinary-review fallback remain staffed.","Evaluation includes sufficient cases to assess cycle time and detect evident operational or safety deterioration.","No serious incident triggers a costly extended investigation."]},"annual_recurring":{"band_2026_usd":"250K_TO_1M","scope":"If renewed, maintain classification, monitoring, audit sampling, reviewer coverage, training, records, threshold calibration, incident response, and periodic outcome and eligibility reviews.","confidence":"LOW","assumptions":["The fast path remains bounded rather than expanding to high-impact decisions.","Existing governance staff and platforms provide part of the operating capacity.","Recurring evaluation covers cumulative exposure, route gaming, complaints, hazards, and subgroup outcomes.","Case volume does not increase enough to require substantial new staffing or infrastructure."]}},"research_burden":"HIGH","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"UNCERTAIN","reason":"The packet clearly describes a recognizable queueing and review-depth hypothesis, but explicitly supplies no case-level evidence that eligible changes are materially delayed, abandoned, or subjected to redundant steps."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"The adopter is the body formally authorized to set the AI review workflow, with privacy, security, legal, safety, civil-rights, board, contractual, regulatory, and legal constraints preserved."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The proposal claims that bounded risk-tiering will outperform ordinary uniform review on cycle time or backlog without exceeding preset hazard, subgroup-harm, gaming, or protected-check bounds; this is distinct from merely adding staff or workflow software."},"bounded_next_evidence_step":{"status":"YES","reason":"A retrospective sample audit can compare prospectively eligible cases with ordinary-review cases and can falsify the diagnosis before any live fast path is authorized."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The packet excludes high-impact, irreversible, sensitive-data-expanding, and legally mandatory-review cases and specifies authorized control, automatic escalation, suspension, rollback, record preservation, and sunset review."},"implementation_cost_scope_and_range":{"status":"UNCERTAIN","reason":"The pilot scope is bounded sufficiently for broad resource bands, but actual case volume, data condition, workflow configurability, cross-functional review burden, and compliance requirements are absent, making the ranges assumption-dependent."}},"blocking_evidence":["No case-level evidence establishes that a separable procedural wedge materially delays or causes abandonment of eligible low-risk changes.","No evidence shows how often steps proposed for streamlining detect consequential hazards unavailable through retained checks.","Reliable ex ante classification of low-risk changes, including cumulative effects across many small changes, has not been demonstrated.","No comparative evidence establishes that the fast path improves cycle time or backlog without displaced delay, missed hazards, subgroup harm, complaints, or material route gaming.","Prior art is unsearched, so distinctiveness and the incremental contribution relative to existing governance models cannot be assessed."],"next_evidence_step":"Conduct a retrospective, access-controlled audit of a predefined sample of completed and abandoned AI-change reviews. Apply the proposed eligibility rules without changing any past decision, then compare prospectively eligible cases with ordinary-review cases on queue time, substantive review time, handoffs, rework, abandonment, and hazards uniquely detected by steps that the fast path would streamline. Falsify progression if eligible cases show no material separable delay or if streamlined steps regularly reveal consequential hazards not captured by retained checks.","research_questions":["What share of reviewed changes meets the proposed eligibility criteria, and how reliably do independent reviewers agree on classification?","For eligible cases, how much elapsed time arises from queueing, repeated handoffs, and rework versus necessary substantive review?","Which existing review steps detect consequential hazards, and would the proposed retained checks have detected the same hazards?","Are beneficial changes materially deferred or abandoned because of the review path, and which affected objectives are delayed?","Can cumulative exposure, subgroup harm, complaints, route gaming, and rebound submissions be measured with decision-relevant thresholds?","Does the mechanism composition differ meaningfully from documented risk-tiered AI-governance review models?","What data access, staffing, compliance, and workflow configuration would a bounded pilot actually require?"] ,"recommendation":"VALIDATE_PROBLEM_FIRST","uncertainty_constraints":["Problem prevalence, queue composition, delay magnitude, abandonment, and recoverable benefit are unmeasured.","Equivalent protection under the proposed retained checks is unproven.","Affected people may not consent to or capture the benefits, and rights harms cannot be offset by aggregate speed gains.","Reliable classification of seemingly minor and cumulatively interacting changes is unresolved.","Stakeholder demand and authorizer willingness are not evidenced.","Cost bands depend on unknown record quality, case volume, workflow systems, compliance obligations, and evaluation requirements.","Market size, realized impact, and world novelty are outside the sealed evidence."] ,"closed_book_prior_art_boundary":"Prior art is explicitly unsearched. This assessment makes no claim that risk-tiered, expiring AI review paths or this safeguard composition are novel, uncommon, or absent from existing governance practice."}