{"schema_version":1,"research_id":"eoa_inverse_innovation_exp04_external_evaluation_20260802","source_assessment_id":"negative_space_design__tech_ethics_ai_governance:PROPOSAL_FIRST:v0","cell_id":"negative_space_design__tech_ethics_ai_governance","search_queries":["site:nist.gov AI RMF human oversight diverse multidisciplinary independent review","site:eur-lex.europa.eu 2024/1689 Article 14 human oversight automation bias","site:whitehouse.gov OMB M-25-21 AI governance board human oversight","algorithmic decision support human decision making automation bias high stakes government study","independent judgment before group discussion avoid information cascades governance decision meeting research","nominal group technique silent generation ideas before discussion official guidance","Delphi method anonymous independent judgments avoid dominance group decision prior art","premortem prospective hindsight identify risks before decision research Klein","EUR-Lex Regulation EU 2024/1689 Article 14 human oversight automation bias official","site:op.europa.eu impact human oversight discrimination AI-supported decision-making 1411 professionals","site:omb.gov M-25-21 agency AI Governance Board convene official","independent initial judgment group deliberation hidden profile study primary research"],"sources":[{"source_id":"S1","title":"AI Risk Management Framework Core","publisher":"U.S. National Institute of Standards and Technology","url":"https://airc.nist.gov/airmf-resources/airmf/5-sec-core/","source_class":"OFFICIAL_GUIDANCE","publication_date":"2023-01-26","accessed_at":"2026-08-02","claims_supported":["AI risk management should incorporate diverse and multidisciplinary perspectives.","Executive leadership is responsible for AI deployment-risk decisions.","Organizations should foster critical thinking, document impacts, and define human-oversight roles.","Independent review can mitigate internal bias and conflicts of interest."]},{"source_id":"S2","title":"Regulation (EU) 2024/1689 (Artificial Intelligence Act)","publisher":"Official Journal of the European Union / EUR-Lex","url":"https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32024R1689","source_class":"GOVERNMENT_OR_REGULATOR","publication_date":"2024-07-12","accessed_at":"2026-08-02","claims_supported":["Article 14 requires effective natural-person oversight of high-risk AI systems.","Overseers must remain aware of automation bias, correctly interpret outputs, and be able to disregard, override, reverse, or interrupt them.","Oversight must be proportionate to risk, autonomy, and context.","The regulation requires appropriate human-machine interfaces and preservation of information needed to interpret system outputs."]},{"source_id":"S3","title":"OMB Memorandum M-25-21: Accelerating Federal Use of AI through Innovation, Governance, and Public Trust","publisher":"Executive Office of the President, Office of Management and Budget","url":"https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-21-Accelerating-Federal-Use-of-AI-through-Innovation-Governance-and-Public-Trust.pdf","source_class":"OFFICIAL_GUIDANCE","publication_date":"2025-04-03","accessed_at":"2026-08-02","claims_supported":["Each covered federal agency must convene an AI Governance Board or use an existing governance body.","Boards must include senior leadership and representatives for legal, privacy, civil rights, cybersecurity, data, budget, and other relevant functions.","Agencies must establish accountable governance and minimum risk-management practices for high-impact AI.","Agencies are identifiable potential authorizers for bounded evaluations of governance-board workflows."]},{"source_id":"S4","title":"The Impact of Human Oversight on Discrimination in AI-Supported Decision-Making","publisher":"Publications Office of the European Union / Joint Research Centre","url":"https://op.europa.eu/en/publication-detail/-/publication/68b91f8f-cf0a-11ef-be2a-01aa75ed71a1/language-en","source_class":"PRIMARY_RESEARCH","publication_date":"2025-01-08","accessed_at":"2026-08-02","claims_supported":["A mixed-method study included an experiment with 1,411 HR and banking professionals in Germany and Italy.","Overseers were equally likely to follow discriminatory and fair generic AI advice.","Human oversight alone did not prevent discrimination from the generic AI.","Participants sought guidance on overriding AI recommendations, and experts emphasized systemic socio-technical oversight design."]},{"source_id":"S5","title":"Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts","publisher":"Ben Green and Yiling Chen / arXiv","url":"https://arxiv.org/abs/2012.05370","source_class":"PRIMARY_RESEARCH","publication_date":"2020-12-09","accessed_at":"2026-08-02","claims_supported":["An experiment with 2,140 participants found that displaying algorithmic risk assessments changed how people weighted risk in simulated pretrial and lending decisions.","The induced decision-process shift increased racial disparity in simulated pretrial detention by 1.9 percentage points.","The shift increased loan rejection and reduced simulated government aid by 8.3 percentage points.","Decision-support signals can alter value tradeoffs rather than merely improve predictive accuracy."]},{"source_id":"S6","title":"Nominal Group Technique (NGT): Nominal Brainstorming Steps","publisher":"American Society for Quality","url":"https://asq.org/quality-resources/nominal-group-technique","source_class":"AUTHORITATIVE_SECONDARY","publication_date":"undated","accessed_at":"2026-08-02","claims_supported":["Nominal Group Technique is an established structured group process in which participants first think and write silently, then share and discuss ideas.","The technique is recommended when some participants are more vocal, some think better in silence, participation is uneven, or the issue is controversial.","A typical silent-writing interval lasts five to ten minutes and prohibits discussion."]},{"source_id":"S7","title":"Quality of Group Decisions by Board Members: A Hidden-Profile Experiment","publisher":"Emerald Publishing / University of Groningen repository","url":"https://pure.rug.nl/ws/portalfiles/portal/167315039/10_1108_MD_07_2020_0893.pdf","source_class":"PRIMARY_RESEARCH","publication_date":"2021","accessed_at":"2026-08-02","claims_supported":["In a hidden-profile experiment involving 141 nonprofit board members, only one fifth of groups selected the objectively best option.","Initial majority preference strongly influenced final decisions.","Two structured discussion procedures increased perceived reflection or time spent but did not improve objective decision quality.","The authors warn that unproven procedures can create a false sense of security and recommend explicitly eliciting members' individual information."]},{"source_id":"S8","title":"Twenty-Five Years of Hidden Profiles in Group Decision Making: A Meta-Analysis","publisher":"SAGE Publications, Personality and Social Psychology Review","url":"https://journals.sagepub.com/doi/10.1177/1088868311417243","source_class":"PRIMARY_RESEARCH","publication_date":"2011-09-06","accessed_at":"2026-08-02","claims_supported":["The meta-analysis covered 65 studies, 101 independent effects, and 3,189 groups.","Groups mentioned substantially more shared than unique information.","Hidden-profile groups were eight times less likely to find the solution than full-information groups.","Greater coverage of unique information was associated with better decision quality."]}],"problem_evidence":{"support":"MODERATE","rationale":"The general problem is visible and consequential: algorithmic recommendations alter human judgments, ordinary human oversight can fail to stop discriminatory advice, and experienced boards can converge on an initial majority while failing to integrate distributed information. Regulation explicitly recognizes automation bias as an oversight hazard. However, no opened source measures the candidate's exact antecedent state—AI deployment boards simultaneously seeing sponsor recommendations, composite ratings, peer comments, and facilitator framing before independent assessment—or the prevalence of ambiguous blank risk fields. The causal bridge from adjacent experimental settings to this exact governance workflow is plausible but unverified.","source_ids":["S2","S4","S5","S7","S8"]},"stakeholder_evidence":{"support":"MODERATE","rationale":"Covered U.S. federal agencies are identifiable authorizers because M-25-21 requires agency AI Governance Boards with multidisciplinary representation, while EU providers and deployers of covered high-risk systems have explicit human-oversight obligations. NIST also assigns responsibility to executive leadership and recommends independent review. These sources establish institutional need for accountable oversight, but none requests this protected-absence intervention or commits staff, data, or funding to test it.","source_ids":["S1","S2","S3","S4"]},"prior_art":{"proximity":"SUBSTANTIAL_COLLISION","closest_analogues":[{"name":"Nominal Group Technique","similarity":"Already uses a bounded silent interval for independent writing before group sharing and discussion, particularly to reduce dominance and increase participation.","remaining_difference":"It does not specifically require temporarily hiding sponsor recommendations, composite AI-risk scores, peer comments, or interface chrome; it also lacks diagnostic empty-state labels and a preserved pre/post-reintroduction audit trail.","source_ids":["S6"]},{"name":"Hidden-profile board-decision interventions","similarity":"Directly addresses boards' failure to pool unique information, initial-majority bias, structured dissent, and elicitation of individually held information.","remaining_difference":"The tested advocacy and decisional-balance procedures operate during discussion; they do not isolate the incremental effect of recoverably withholding conclusion-like signals before discussion.","source_ids":["S7","S8"]},{"name":"AI RMF independent and multidisciplinary review","similarity":"Establishes independent review, diverse perspectives, documented impacts, critical thinking, and defined oversight roles as recognized AI-governance practices.","remaining_difference":"It specifies governance outcomes rather than a two-pass interface and facilitation protocol or its comparative effectiveness.","source_ids":["S1"]},{"name":"EU AI Act human-oversight controls","similarity":"Requires interfaces and procedures that let overseers understand limitations, resist automation bias, interpret outputs, override them, and stop systems.","remaining_difference":"It does not prescribe hiding sponsor or aggregate signals during deployment review, independent pre-discussion notes, or pre/post judgment comparison.","source_ids":["S2"]}],"distinctive_claim_remaining":"Holding the complete evidence packet, decision criteria, reviewer roster, time budget, and mandatory independent-writing requirement constant, temporarily and recoverably withholding sponsor recommendations, composite ratings, peer comments, notifications, and nonessential facilitator framing will increase correctly evidence-grounded and reviewer-differentiated identification of unresolved impacts or unassessable evidence states, relative to full-information independent writing, without increasing safety-critical omissions, evidence-retrieval failures, state misinterpretation, accessibility failures, or perceived coercion.","confidence":"HIGH"},"implementation_evidence":{"support":"MODERATE","rationale":"The intervention is technically straightforward as a prototype: role-based visibility, timed state transitions, one-action evidence recovery, explicit state labels, immutable pre/post notes, and logged overrides are conventional workflow features. Existing guidance supports multidisciplinary governance, accountability, effective human-machine interfaces, proportional safeguards, and cessation or rollback when risk cannot be mitigated. Feasibility nevertheless depends on local case-data permissions, records-retention and discovery rules, accessibility testing, confidentiality protections for dissent, union or personnel policy where applicable, and board-chair authorization. No source verifies integration with a specific organization's workflow, and the silence component requires trauma- and power-sensitive facilitation.","source_ids":["S1","S2","S3","S4","S6","S7"]},"scores":{"meaningful_impact":{"score":4,"rationale":"A successful intervention could improve detection of unresolved harms before consequential deployments; automation-bias and board-decision evidence shows the failure mode can affect rights and resource allocation. Realized impact is unmeasured.","source_ids":["S2","S4","S5","S7"]},"stakeholder_pull":{"score":3,"rationale":"Mandated governance boards and human-oversight duties create credible need and authority, but no adopter has expressed demand for this specific protocol or offered resources.","source_ids":["S1","S2","S3"]},"incremental_advantage":{"score":3,"rationale":"The recoverable hiding of conclusion-like signals, diagnostic empty states, and preserved pre/post notes could add value beyond mandatory independent writing, but that increment has not been tested and established silent-writing methods are already close.","source_ids":["S6","S7","S8"]},"distinctiveness_plausibility":{"score":2,"rationale":"The integrated AI-governance implementation is specific, but its core causal structure substantially overlaps Nominal Group Technique, independent review, and hidden-profile countermeasures.","source_ids":["S1","S6","S7","S8"]},"technical_implementability":{"score":4,"rationale":"A non-production prototype needs ordinary workflow and access-control functions rather than new AI research. The main uncertainties concern accessibility, secure note handling, evidence recovery, and integration rather than basic construction.","source_ids":["S1","S2","S3"]},"adoption_authority_feasibility":{"score":4,"rationale":"Governance-board chairs, agency heads, data owners, and compliance or research authorities are identifiable. A sanitized shadow test leaves live deployment authority unchanged, although approvals and records rules remain local dependencies.","source_ids":["S2","S3"]},"evidence_readiness":{"score":3,"rationale":"The candidate has an explicit comparator, outcomes, falsifiers, safety stops, and a small shadow-test design. It lacks workflow-prevalence data, an authorized partner, validated case-scoring rubrics, and direct intervention results.","source_ids":["S4","S5","S6","S7","S8"]},"safety_net_benefit":{"score":4,"rationale":"If effective, the protocol could surface uncertainty, minority concerns, and risks to affected people before authorization while retaining evidence and rollback. It could also expose dissent or create pressure, so benefit depends on confidentiality and accessibility safeguards.","source_ids":["S1","S2","S4","S7"]},"scalability":{"score":4,"rationale":"The two-pass pattern could be implemented in common review platforms and standardized across cases, but context-specific evidence taxonomies, legal retention rules, accessibility needs, and urgency exceptions limit plug-and-play scaling.","source_ids":["S1","S2","S3","S6"]}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"10K_TO_50K","scope":"Design and run one counterbalanced, non-production usability study with six to eight authorized reviewers and two sanitized archived cases; includes protocol design, a lightweight prototype, facilitation, scoring, and a short analysis.","confidence":"MODERATE","assumptions":["Uses existing meeting and form infrastructure or a low-code prototype.","Archived cases are already sanitized and approved for research use.","Participants contribute approximately one working day each across preparation, sessions, and debriefing.","Excludes live deployment decisions, procurement, and production security certification."],"source_ids":["S3","S6","S7"]},"initial_deployment_startup":{"band_2026_usd":"50K_TO_250K","scope":"Prepare a production-capable implementation for one governance program, including workflow configuration, role-based access, audit logging, accessibility review, privacy and records review, training materials, and limited integration.","confidence":"LOW","assumptions":["One organization and one existing governance platform are in scope.","No custom enterprise identity system or major data migration is required.","Legal, privacy, security, accessibility, and affected-stakeholder representatives review the design.","Estimate is resource-equivalent, not a vendor quote."],"source_ids":["S1","S2","S3"]},"operational_launch":{"band_2026_usd":"250K_TO_1M","scope":"Launch across one medium-to-large organization with multiple review teams, production integration, security testing, retention controls, facilitator training, change management, monitoring, and independent evaluation support.","confidence":"LOW","assumptions":["Launch covers several business units but not a multinational enterprise-wide rollout.","Existing AI inventory and governance bodies can be reused.","Sensitive-note access and discovery controls require engineering and legal work.","No model development or new decision authority is included."],"source_ids":["S1","S2","S3"]},"annual_recurring":{"band_2026_usd":"50K_TO_250K","scope":"Annual administration for one organization: platform maintenance, access reviews, training, accessibility regression checks, protocol audits, outcome monitoring, case-taxonomy updates, and periodic independent review.","confidence":"LOW","assumptions":["The feature remains within an existing platform and support contract.","One part-time program owner plus periodic engineering, legal, privacy, accessibility, and evaluation effort is sufficient.","Major regulatory remediation, litigation, and expansion to new jurisdictions are excluded."],"source_ids":["S1","S2","S3"]}},"verified_pipeline_gates":{"externally_supported_problem":{"status":"YES","reason":"Multiple experimental and regulatory sources support automation bias, discriminatory reliance, initial-majority effects, and failures to surface distributed information, although exact target-workflow prevalence remains unknown.","source_ids":["S2","S4","S5","S7","S8"]},"externally_credible_adopter_or_authorizer":{"status":"YES","reason":"Federal agency AI Governance Boards, their senior chairs, and covered EU providers or deployers are identifiable governance actors with formal oversight responsibilities; a specific willing partner is not yet secured.","source_ids":["S2","S3"]},"distinct_testable_incremental_claim":{"status":"YES","reason":"The claim isolates recoverable withholding plus facilitation silence from the strongest rival—mandatory independent writing with all signals visible—and specifies both benefit and harm outcomes.","source_ids":["S6","S7","S8"]},"bounded_next_evidence_step":{"status":"YES","reason":"A six-to-eight-person, two-case, counterbalanced shadow usability study is finite, non-production, comparator-based, and has explicit falsifiers and halt conditions.","source_ids":["S4","S5","S6","S7"]},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The proposed first step uses sanitized archived cases, preserves primary evidence and urgent safety information, leaves deployment authority with the board, logs overrides, and restores the ordinary interface on retrieval, accessibility, distress, or safety failure. Local privacy, records, and research approvals must still be obtained before testing.","source_ids":["S1","S2","S3"]},"credible_cost_scope_and_range":{"status":"YES","reason":"All four estimates define distinct resource-equivalent scopes and explicit assumptions. Confidence is only moderate for the first study and low for production bands because no implementation partner, platform, jurisdiction, or vendor quote exists.","source_ids":["S1","S2","S3"]}},"next_evidence_step":"With written approval from one governance-board chair and data owner, run a counterbalanced shadow usability study involving six to eight authorized reviewers and two sanitized archived AI-deployment cases. Randomize case order and condition so every reviewer completes (A) full-information mandatory independent writing and (B) the protected-deliberation-void prototype. Before discussion, score blinded notes against an expert-created case key for correctly grounded unresolved impacts, correct distinctions among examined-no-concern, unexamined, and unavailable evidence, and safety-critical omissions. Also measure evidence-retrieval success and latency, comprehension of why signals are absent, time, accessibility failures, perceived pressure, and override use. Falsify the incremental claim if condition B produces no meaningful gain over A in grounded or differentiated concerns, or produces any material increase in missed safety facts, state confusion, inaccessible evidence, or coercive distress. Do not use outputs for the live case record or deployment disposition.","blocking_evidence":["No direct prevalence evidence shows how often target AI-governance boards expose sponsor recommendations, aggregate ratings, peer comments, and continuous facilitator framing before independent judgment.","No comparative evidence establishes that hiding conclusion-like signals adds benefit beyond mandatory independent writing, Nominal Group Technique, or other structured elicitation.","No governance-board chair, data owner, privacy or records authority, or participant group has committed to the proposed shadow study.","No validated case-level scoring rubric or minimum practically important effect has been established for 'correctly grounded unresolved impacts.'","Accessibility, confidentiality, retaliation, records-retention, discovery, and perceived-coercion risks have not been tested in the intended organization.","Production engineering and recurring-cost bands lack platform-specific requirements, staffing rates, and vendor quotations."],"research_disposition":"PARTNERED_RESEARCH_PROGRAM","world_novelty_boundary":"This bounded search found established silent independent generation, independent review, human-oversight controls, hidden-profile research, and board-decision procedures that substantially overlap the proposal. It did not find a directly evaluated package combining temporary recoverable hiding of sponsor and aggregate signals, diagnostic risk-field empty states, facilitation silence, and preserved pre/post notes in AI deployment reviews. That absence is not evidence of world novelty. Patentability, freedom to operate, exhaustive product and standards coverage, market size, and realized impact remain unmeasured.","arm":"PROPOSAL_FIRST","candidate_version":0,"controller_recommendation":{"action":"STOP_EMPIRICAL_RESEARCH_NEEDED","repairable":true,"material_progress_observed":true,"progress_targets":["Secure written participation from one authorized AI-governance board, its chair, and the relevant data, privacy, records, and research authorities.","Audit the partner's current review workflow to verify that the proposed crowded antecedent state and ambiguous blank-field problem actually occur.","Pre-register the full-information independent-writing comparator, randomized case order, blinded scoring rubric, minimum practically important effect, and harm non-inferiority thresholds.","Build and accessibility-test a non-production prototype with one-action evidence recovery, explicit absence labels, immutable pre/post notes, logged overrides, and immediate rollback.","Run the six-to-eight-reviewer, two-case counterbalanced shadow study and stop if benefit is null or safety, comprehension, accessibility, or coercion thresholds fail.","Only if the usability study passes, design a larger powered shadow evaluation against independent writing and Nominal Group Technique before any live operational use."],"reason":"Web evidence establishes a consequential adjacent problem, credible authorizers, substantial prior-art collision, and a narrow falsifiable increment. The decisive remaining questions—whether the exact workflow problem occurs and whether recoverable withholding outperforms independent writing without safety or accessibility loss—require partner access, proprietary workflow observation, and live human testing rather than further bounded web search."}}