{"schema_version":1,"assessment_id":"eoa_inverse_innovation_exp03_opportunity320_20260801","source_experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"invariant_mode_decomposition_design__tech_ethics_ai_governance","archetype_slug":"invariant_mode_decomposition_design","domain_slug":"tech_ethics_ai_governance","title":"Coupled-Mode Early Warning for AI Governance Risk Drift","opportunity_summary":"Retrospectively estimate coupled governance dynamics for one AI deployment and test whether stable joint-risk modes provide earlier, safer escalation prompts than separate thresholds or a preregistered multivariate rival. The proposal is an early-warning hypothesis, not a causal or individual-level decision mechanism.","adopter_authorizer":"The prospective adopter is the deployment owner's risk, compliance, audit, or ethics function. Interpretation and any later control require authorization from the accountable deployment owner and an independent risk or ethics review body, with data-owner approval and required worker or affected-party representation.","scores":{"meaningful_impact":{"score":4,"rationale":"If coordinated subthreshold deterioration is present, earlier investigation could reduce delayed escalation, organizational lock-in, and harm to people subject to AI-assisted decisions. The magnitude is potentially substantial, but the packet supplies no evidence of problem prevalence or realized impact."},"stakeholder_pull":{"score":3,"rationale":"Deployment owners and governance teams have a proposal-specific incentive to detect failures missed by existing dashboards, while affected parties could benefit from earlier containment. No interviews, adoption commitments, observed demand, or evidence that current reviewers regard this failure mode as a priority are provided."},"incremental_advantage":{"score":3,"rationale":"The candidate makes a clear incremental claim: joint modes should add held-out warning performance and decision-relevant lead time beyond separate thresholds and a preregistered multivariate score. Whether the interpretation or action mapping adds value over the nearest rival is entirely untested."},"distinctiveness_plausibility":{"score":3,"rationale":"Invariant-mode interpretation, stability gates, harm-weighted sensitivity, and explicit rollback form a coherent combination distinct from the stated baseline. Prior art is unsearched, so world distinctiveness cannot be established and substantial overlap with existing multivariate monitoring is possible."},"technical_implementability":{"score":3,"rationale":"A retrospective shadow analysis is technically bounded and avoids production intervention, but implementability depends on enough comparable time points, consistent indicator definitions, linked outcomes, a locally stationary regime, identifiable modes, and acceptable conditioning. The packet does not establish that these prerequisites exist."},"adoption_authority_feasibility":{"score":4,"rationale":"The candidate identifies accountable and independent authorizers, data-owner approval, representation requirements, excluded uses, and rollback authority. Feasibility is reduced by the coordination required among deployment owners, governance bodies, workers or affected parties, and data custodians."},"evidence_readiness":{"score":3,"rationale":"The proposal supplies a baseline, nearest rival, held-out comparisons, subgroup residual checks, falsifiers, and halt criteria suitable for a preregistered retrospective study. It supplies no dataset, sample-size basis, historical event count, outcome-label assessment, or evidence that a usable local transition operator can be estimated."},"safety_net_benefit":{"score":4,"rationale":"The first step is retrospective and shadow-only; the existing governance process remains in force, production and individual decisions are excluded, and explicit retirement and deletion conditions limit downside. Residual risk remains that descriptive modes could launder biased indicators or acquire unjustified causal authority."},"scalability":{"score":2,"rationale":"The method is expressly bounded to a declared population, model version, and operating window, while indicator definitions, incentives, populations, and policies can alter the fitted operator. Recalibration, drift checks, subgroup validation, and renewed authorization would likely be required for each materially different deployment."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"50K_TO_250K","scope":"Preregistered retrospective shadow study for one deployment, including data extraction and linkage, indicator and outcome review, baseline and nearest-rival implementation, resampling and stability tests, subgroup residual analysis, governance review, and a decision memo.","confidence":"LOW","assumptions":["Historical governance records and outcome labels already exist and are legally accessible.","The study covers one deployment and does not create new production infrastructure.","Internal technical, governance, privacy, and affected-party coordination labor is counted as resource-equivalent cost.","No prospective intervention or live excitation is performed."]},"initial_deployment_startup":{"band_2026_usd":"250K_TO_1M","scope":"Production-grade preparation for one authorized deployment after successful evidence, including durable data pipelines, access controls, versioning, alert integration, review procedures, documentation, independent validation, security and privacy review, and staff training.","confidence":"LOW","assumptions":["Existing governance and monitoring systems can be extended rather than replaced.","The alert remains advisory and system-level.","Additional validation is required before operational use.","Major remediation of missing or inconsistent historical data is not included."]},"operational_launch":{"band_2026_usd":"250K_TO_1M","scope":"Bounded launch for one deployment with parallel operation against the existing process, authorization and representation activities, reviewer staffing, audit logging, incident-response integration, subgroup monitoring, predefined rollback, and launch evaluation.","confidence":"LOW","assumptions":["Retrospective evidence clears all preregistered gates.","Launch does not automate suspension or individual decisions.","A limited number of governance indicators and reviewer groups are involved.","No organization-wide rollout is included."]},"annual_recurring":{"band_2026_usd":"250K_TO_1M","scope":"Annual operation for one or a small number of related deployments, including data maintenance, periodic refitting, drift and conditioning checks, subgroup audits, independent review, alert investigation, documentation, retraining, and compliance coordination.","confidence":"LOW","assumptions":["Indicator definitions and model versions change occasionally rather than continuously.","Human review remains mandatory for every alert.","Independent validation and affected-party participation recur periodically.","Major platform replacement or litigation response is excluded."]}},"research_burden":"HIGH","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"UNCERTAIN","reason":"The packet specifies an intelligible missed-joint-deterioration problem, observable state, consequence, and falsifier, but supplies no empirical case showing that such failures occur or evade ordinary single-metric thresholds."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"The accountable deployment owner and independent risk or ethics review body are expressly identified, along with data-owner approval and representation requirements."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The proposal can be tested for superior held-out warning performance and decision-relevant lead time against both separate thresholds and a preregistered multivariate risk-score or supervised-prediction rival."},"bounded_next_evidence_step":{"status":"YES","reason":"A one-deployment retrospective shadow study is defined, makes no production or individual-level decisions, and includes explicit comparisons, subgroup checks, falsifiers, and retirement conditions."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The authorized first step is non-production, preserves existing governance, excludes adverse individual use and unsupported causal claims, and has specified halt, rollback, and score-deletion conditions. These safeguards support research without implying later deployment authority."},"implementation_cost_scope_and_range":{"status":"UNCERTAIN","reason":"The one-deployment scope is bounded enough for broad resource bands, but data condition, record volume, integration complexity, review workload, compliance obligations, and representation costs are not supplied."}},"blocking_evidence":["Evidence that at least some incidents or control failures are preceded by coupled subthreshold deterioration rather than an ordinary single-metric breach.","Enough comparable review periods and outcome events to support held-out evaluation without leakage or unstable estimation.","Evidence that the fitted modes, spectral separation, and interpretations remain sufficiently stable across resamples and relevant operating windows.","Evidence that mode-based alerts add warning performance or decision-relevant lead time over both separate thresholds and the preregistered nearest rival.","Acceptable harm-relevant residuals and subgroup error under preregistered tolerances.","Independent evidence before treating any proposed mode-damping control as causal or operationally effective."],"next_evidence_step":"With one willing data-holding deployment partner, preregister and run a retrospective shadow study on historical review periods. Compare separate thresholds, the nearest-rival multivariate model, and the modal method on held-out warning performance and decision-relevant lead time; stratify residuals by affected group and test mode stability across resamples. Falsify advancement if ordinary thresholds or the rival perform equally well or better, no added lead time appears, modes rotate materially, the spectral-gap margin fails, or subgroup error exceeds tolerance.","research_questions":["Do historical incidents or control failures actually exhibit coordinated subthreshold deterioration before any single-metric breach?","Are there enough comparable time points, outcomes, and stable indicator definitions to estimate and validate a local transition operator?","Does the modal method outperform separate thresholds and a preregistered multivariate rival on held-out warning quality and usable lead time?","Are identified modes stable under resampling, alternative reasonable specifications, non-normality checks, and operating-window changes?","Do aggregate gains persist when residuals and errors are examined for affected groups?","Can reviewers interpret alerts without converting descriptive modes into causal claims, individual traits, or automatic adverse actions?","What independently justified design could test whether a reversible system-level control actually changes the risky mode and harm-relevant outcomes?","How sensitive are costs and review workload to data cleanup, alert frequency, governance coordination, and model-version changes?","What prior systems or methods already use coupled dynamic modes for organizational or AI-governance monitoring?"] ,"recommendation":"PARTNERED_RESEARCH","uncertainty_constraints":["Closed-book assessment provides no external evidence of prevalence, stakeholder demand, prior art, market size, realized impact, or actual implementation cost.","The candidate is a hypothesis and supplies no empirical dataset or validated operator.","Governance indicators may be strategically reported, delayed, socially constructed, or redefined after policy and incentive changes.","Observational modes do not establish causal control effects.","Cost bands depend strongly on unknown data quality, integration architecture, compliance requirements, and reviewer workload.","Generalization beyond one population, model version, and operating window is unsupported."],"closed_book_prior_art_boundary":"Prior-art status is UNSEARCHED. This assessment makes no claim that invariant-mode governance monitoring, its component methods, or their combination is novel, rare, prevalent, commercially differentiated, or absent from existing practice."}