{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp04_retrieval_first_paired20_20260802","cell_id":"negative_space_design__tech_ethics_ai_governance","round_index":0,"assessments":[{"hypothesis_id":"H1","search_queries":["AI model release review independent assessment before deliberation sponsor recommendation anchoring","structured analytic techniques independent judgment before group discussion anchoring review committee","nominal group technique silent independent ideas before group discussion primary study anchoring","official AI model release review preparedness framework independent reviewers recommendation deployment"],"sources":[{"source_id":"H1-S1","title":"OpenAI’s Frontier Governance Framework","publisher":"OpenAI","url":"https://openai.com/index/openai-frontier-governance-framework/","source_class":"COMMERCIAL_FIRST_PARTY","claims_supported":["Frontier-model governance already includes formal risk assessment, mitigation, model reporting, external expert input, and release-related governance processes."]},{"source_id":"H1-S2","title":"Govern — NIST AI RMF Playbook","publisher":"National Institute of Standards and Technology","url":"https://airc.nist.gov/airmf-resources/playbook/govern/","source_class":"OFFICIAL_GUIDANCE","claims_supported":["NIST recommends independent testing functions, effective challenge, and organizational practices intended to counter confirmation bias and groupthink in AI design and deployment decisions."]},{"source_id":"H1-S3","title":"Building Timely Consensus Among Diverse Stakeholders: An Adapted Nominal Group Technique","publisher":"Annals of Family Medicine","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11588383/","source_class":"PRIMARY_RESEARCH","claims_supported":["Nominal group technique already sequences silent individual idea generation, sharing, discussion, and ranking.","The reported adaptation used individual pre-elicitation and facilitator withholding to reduce bias and broaden expression."]},{"source_id":"H1-S4","title":"Silence is golden: The effect of verbalization on group performance","publisher":"Journal of Experimental Psychology: General / PubMed","url":"https://pubmed.ncbi.nlm.nih.gov/29888944/","source_class":"PRIMARY_RESEARCH","claims_supported":["A randomized study found quiet nominal groups outperformed verbalizing pairs on the studied problem-solving tasks, supporting the general causal premise behind an initial silent phase."]}],"closest_analogue":"Nominal group technique applied inside an AI release-governance review: reviewers silently record independent risk ideas before structured sharing and deliberation.","overlap":"The analogue contains the central sequence: an independently documented silent phase, withholding of facilitator or sponsor ideas, later reintroduction of views, and subsequent group deliberation. NIST separately supplies the AI-governance setting through independent testing and effective-challenge practices.","remaining_difference":"The remaining elements are the model-release label, the specific exclusion of sponsor recommendations, and the proposed 20% hazard-finding and 15% review-time thresholds. These are implementation and evaluation parameters rather than a distinct intervention structure.","classification":"OBVIOUS_COLLISION","disposition":"REJECT","rationale":"The causal mechanism and procedural sequence closely reproduce the established nominal-group pattern, while existing AI guidance already calls for independent challenge and bias-counteracting review. The AI-release application does not create enough structural distance for advancement in a shallow screen."},{"hypothesis_id":"H2","search_queries":["AI governance dashboard missing data zero no incidents empty state observability","monitoring dashboard distinguish no data missing telemetry zero data official documentation","site:grafana.com/docs no data state alerting official","site:docs.aws.amazon.com CloudWatch missing data zero metric official"],"sources":[{"source_id":"H2-S1","title":"Risk Posture Dashboard","publisher":"GraphnAI","url":"https://www.graphnai.com/docs/guides/risk-posture-dashboard","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["A risk-posture dashboard explicitly distinguishes a zero score meaning no findings from a 'No data' state meaning analytics have not run.","Its empty-state troubleshooting identifies unsynced identity data, an unrun analytics pipeline, and database connectivity as separate causes with separate next actions."]},{"source_id":"H2-S2","title":"Building dashboards for operational visibility","publisher":"Amazon Web Services","url":"https://d1.awsstatic.com/builderslibrary/pdfs/building-dashboards-for-operational-visibility-johnoshea.pdf","source_class":"COMMERCIAL_FIRST_PARTY","claims_supported":["AWS warns that empty sparse-metric graphs confuse operators and recommends emitting explicit safe zero values so missing telemetry remains distinguishable from absence of an error condition.","AWS recommends explanatory context and links to diagnostic resources beside dashboard graphs."]},{"source_id":"H2-S3","title":"Configuring how CloudWatch alarms treat missing data","publisher":"Amazon Web Services","url":"https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/alarms-and-missing-data.html","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["CloudWatch distinguishes not-breaching, breaching, ignored, missing, and insufficient-data conditions.","The documentation states that treatment must depend on metric purpose to avoid misleading health indications."]},{"source_id":"H2-S4","title":"Alerting and recording rules","publisher":"Grafana Labs","url":"https://grafana.com/docs/loki/latest/alert/","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["Grafana-managed alerting explicitly handles error and no-data states as distinct conditions."]}],"closest_analogue":"The GraphnAI risk-posture dashboard combined with CloudWatch-style missing-data semantics.","overlap":"These systems already type operational emptiness by cause, distinguish valid zero or no findings from missing data and evaluation failure, preserve provenance or diagnostic context, and provide state-specific recovery actions. GraphnAI places the pattern directly in a risk-posture dashboard.","remaining_difference":"The hypothesis adds AI-incident-specific labels such as access denial and a scenario-test target of 30% fewer false 'no incident' conclusions. That is a domain-specific taxonomy and validation plan, not a materially different interface mechanism.","classification":"OBVIOUS_COLLISION","disposition":"REJECT","rationale":"The closest first-party dashboard analogue already distinguishes no findings, no data, incomplete processing, and infrastructure failure, while mature observability products implement the same typed-absence semantics. The proposed intervention collides at the component level."},{"hypothesis_id":"H3","search_queries":["AI governance board decision packet concise executive summary appendix official guidance","board paper guidance concise decision focused appendices official governance institute","AI governance board reporting template risks incidents decisions appendix","board meeting packet executive summary appendices decision critical information official guidance"],"sources":[{"source_id":"H3-S1","title":"Board AI Reporting Template","publisher":"AI Leadership Development","url":"https://aild.org/learn/board-ai-reporting-template/","source_class":"COMMERCIAL_FIRST_PARTY","claims_supported":["The template structures a quickly reviewable AI board update around an executive summary, evidence, unresolved risks, incidents, control gaps, decisions, and escalation.","It explicitly warns against burying incidents or exceptions in appendices."]},{"source_id":"H3-S2","title":"Board papers","publisher":"Governance Institute of Australia","url":"https://governanceinstitute.com.au/app/uploads/2023/12/govinst_guidance-board_papers_2021-1.pdf","source_class":"OFFICIAL_GUIDANCE","claims_supported":["The guidance prioritizes quality over quantity and warns that excessive information does not assist board decision-making.","It provides preparation guidance and a sample board-paper structure while emphasizing relevance, accuracy, and later third-party reviewability."]},{"source_id":"H3-S3","title":"AI Cyber Governance Framework Implementation Guide","publisher":"Health Sector Coordinating Council Cybersecurity Working Group","url":"https://healthsectorcouncil.org/wp-content/uploads/2026/05/AI-Cyber-Governance-Framework-Implementation-Guide.pdf","source_class":"OFFICIAL_GUIDANCE","claims_supported":["The guide includes a structured Board AI Risk Reporting Template with a short standalone executive summary, material changes, incidents, open actions, evidence, decisions, and a definitions appendix.","It separates board-facing decision material from supporting definitions while retaining material risk and incident information in the main report."]}],"closest_analogue":"Existing AI board-reporting templates that put material risks, incidents, evidence, and requested decisions in a concise main briefing while relegating supporting definitions or detail to appendices.","overlap":"The analogue already performs the proposed editorial cut: it reduces volume, foregrounds actionable evidence and unresolved exceptions, preserves decision-critical caveats in the main packet, and keeps secondary material recoverable in an appendix.","remaining_difference":"The proposed seeded-caveat comprehension experiment and its 15% threshold are evaluation details. The packet architecture itself is already directly instantiated in AI-governance guidance.","classification":"OBVIOUS_COLLISION","disposition":"REJECT","rationale":"Both general board-paper guidance and AI-specific reporting templates already prescribe the same guarded compression and recoverable appendix structure. The hypothesis is useful but plainly collides with established practice."},{"hypothesis_id":"H4","search_queries":["safety critical interface declutter focus mode hide noncritical controls human override AI","human factors emergency interface display declutter safety critical controls standards","display decluttering safety critical interface primary study operator performance","AI assisted decision override interface simulation human factors study"],"sources":[{"source_id":"H4-S1","title":"Appendix F: Display Standard","publisher":"NASA","url":"https://www.nasa.gov/reference/appendix-f-vol-2/","source_class":"STANDARD","claims_supported":["NASA limits displayed information to what is needed for the task and situation awareness.","It requires key information to remain visible, safety-critical controls to be safeguarded, and command status to be positively indicated."]},{"source_id":"H4-S2","title":"10.0 Crew Interfaces","publisher":"NASA","url":"https://www.nasa.gov/reference/10-0-crew-interfaces-vol-2/","source_class":"STANDARD","claims_supported":["NASA requires simultaneous presentation of critical task information and safe human override and shutdown capabilities.","It requires mode-change notification, explanations and limitations for decision aids, and safe recovery from automation failure."]},{"source_id":"H4-S3","title":"AC 120-76E — Authorization for Use of Electronic Flight Bags","publisher":"Federal Aviation Administration","url":"https://www.faa.gov/regulations_policies/advisory_circulars/index.cfm/go/document.information/documentID/1042829","source_class":"GOVERNMENT_OR_REGULATOR","claims_supported":["FAA guidance treats accessibility, usability, and reliability of operational displays as authorization-relevant properties in safety-critical aviation workflows."]},{"source_id":"H4-S4","title":"AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions","publisher":"arXiv","url":"https://arxiv.org/abs/2304.08804","source_class":"PRIMARY_RESEARCH","claims_supported":["Human-AI decision research treats correct override of wrong recommendations as a distinct outcome from adherence and overall accuracy.","The paper argues that interventions must be assessed for both reliance behavior and decision quality."]}],"closest_analogue":"NASA’s task-focused safety-critical crew-interface pattern: show only task-needed information, keep critical information and override controls continuously available, signal mode changes, and support recovery.","overlap":"Both approaches reduce secondary display competition, preserve safety-critical evidence and controls, maintain human override authority, distinguish operating modes, and evaluate timely, accurate action under automation.","remaining_difference":"The searched analogues do not establish the specific transient and instantly reversible focus mode during rehearsals of high-risk AI-assisted decisions. The testable difference is whether temporarily hiding only noncritical chrome improves correct-override time across novice and expert operators without changing evidence inspection, appropriate reliance, mode awareness, or control recovery.","classification":"POSSIBLE_DISTINCTION","disposition":"ADVANCE","rationale":"Safety-critical interface principles strongly anticipate the guardrails, but the combination of temporary reversible decluttering, AI-recommendation override, rehearsal conditions, and joint recovery/reliance outcomes was not directly instantiated in the shallow search. It merits a deeper analogue review rather than rejection."},{"hypothesis_id":"H5","search_queries":["AI portfolio governance reserve capacity red teaming shadow evaluation nondeployment pilot capacity","responsible AI pilot sandbox shadow mode before deployment official guidance","innovation portfolio allocate capacity discovery experiments not deployment reserve capacity","AI governance predeployment testing red teaming stakeholder consultation organizational capacity framework"],"sources":[{"source_id":"H5-S1","title":"Govern — NIST AI RMF Playbook","publisher":"National Institute of Standards and Technology","url":"https://airc.nist.gov/airmf-resources/playbook/govern/","source_class":"OFFICIAL_GUIDANCE","claims_supported":["NIST recommends policies for allocating risk-management resources according to risk tolerance, separating development from testing, enabling independent course correction, and approving, conditionally approving, disapproving, or decommissioning AI systems.","NIST identifies independent red-teaming and effective challenge as organizational risk-management practices."]},{"source_id":"H5-S2","title":"Guidance to set up your organization's AI governance process","publisher":"Microsoft","url":"https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai/govern","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["Microsoft recommends sandbox environments for initial experiments before validation and production-catalog review.","The guidance calls for stakeholder consultation and structured workload-level risk assessment before deployment."]},{"source_id":"H5-S3","title":"OpenAI's Approach to External Red Teaming for AI Models and Systems","publisher":"OpenAI","url":"https://arxiv.org/abs/2503.16431","source_class":"PRIMARY_RESEARCH","claims_supported":["External red teaming can discover novel risks, stress-test mitigations, add domain expertise, and provide independent assessment before deployment.","The paper notes that this work is resource intensive and that red-team findings can seed reusable evaluations."]},{"source_id":"H5-S4","title":"Guidance to set your organization's responsible AI policies","publisher":"Microsoft","url":"https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai/responsible-ai-policies","source_class":"OFFICIAL_GUIDANCE","claims_supported":["Microsoft recommends adequately resourcing governance teams and embedding design-review, testing, and prelaunch checkpoints into AI workflows.","It calls for formal governance sign-off for high-risk systems and continuing audit and incident-response preparation."]}],"closest_analogue":"Risk-tiered governance resource allocation combined with sandboxed experimentation, independent red-teaming, and prelaunch approval checkpoints.","overlap":"Existing guidance already requires or recommends nondeployment work before release, dedicated testing and oversight resources, independent challenge, sandbox experiments, stakeholder input, and deployment approval or rejection criteria.","remaining_difference":"The bounded distinction is portfolio-level ring-fencing: reserving a stated share of annual pilot capacity exclusively for nondeployment inquiry, protecting it from live-use commitments, and releasing or reallocating it only under explicit criteria. A deeper review should ask whether any AI portfolio standard, regulatory sandbox, corporate policy, or published program already mandates a fixed or minimum capacity share with those protections and whether portfolio-level outcome data exist.","classification":"POSSIBLE_DISTINCTION","disposition":"ADVANCE","rationale":"The component activities are established, but the shallow search found them as per-system lifecycle controls or risk-weighted resourcing, not as a protected organization-year capacity reserve. The portfolio ring-fence is a concrete, independently researchable distinction."}],"nominated_ids":["H4","H5"],"replenishment_recommended":false}