{"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 perspectives governance independent review","site:eur-lex.europa.eu Regulation EU 2024 1689 human oversight automation bias Article 14","site:whitehouse.gov OMB M-25-21 AI governance board Chief AI Officer risk review","independent judgment before group discussion anchoring group decision making primary study hidden profile","algorithmic recommendation anchoring human decision makers experiment risk assessment automation bias primary study","AI assisted decision making anchoring bias explanations experiment human oversight study","Delphi method anonymous independent judgments controlled feedback RAND official","nominal group technique silent generation ideas before discussion original research","site:iso.org ISO IEC 42001 AI management systems official governance risk","site:w3.org WAI WCAG hidden controls status messages accessibility official","site:bls.gov software developers median pay 2025 compliance managers wage","site:gov.uk AI assurance portfolio independent review governance board artificial intelligence","BLS Occupational Employment Wage Statistics software developers median annual wage May 2025","BLS management analysts median annual wage 2025 compliance officers","GSA contractor labor rates software engineer 2026 schedule","\"Deciding Fast and Slow\" time-based de-anchoring experiment PDF","\"Quality of group decisions by board members\" hidden profile full text","\"Algorithmic Risk Assessments Can Alter Human Decision-Making Processes\" paper government loans 8.3%"],"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 use diverse and multidisciplinary perspectives.","Executive leadership is responsible for AI deployment risk decisions.","Organizations should foster critical thinking, document impacts, and define human-oversight processes.","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 human 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.","Transparency, accessibility, logging, and comprehensible instructions are required for covered high-risk systems."]},{"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":"GOVERNMENT_OR_REGULATOR","publication_date":"2025-04-03","accessed_at":"2026-08-02","claims_supported":["CFO Act agencies must convene multidisciplinary AI governance boards chaired at Deputy Secretary level with the CAIO as vice-chair.","CAIOs must establish independent review before risk acceptance for high-impact AI uses.","Limited pilots require CAIO certification and central tracking.","Agencies must document and manage high-impact AI risks and safely discontinue noncompliant uses."]},{"source_id":"S4","title":"Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making","publisher":"IBM Research / Proceedings of the ACM on Human-Computer Interaction","url":"https://research.ibm.com/publications/deciding-fast-and-slow-the-role-of-cognitive-biases-in-ai-assisted-decision-making--1","source_class":"PRIMARY_RESEARCH","publication_date":"2022-04-07","accessed_at":"2026-08-02","claims_supported":["Anchoring occurs in AI-assisted decision-making.","Two user experiments found that time-based de-anchoring, including time allocation paired with explanation, can improve performance when AI confidence is low and the AI is wrong.","Temporally managing exposure to an AI signal is close prior art to the proposed protected interval."]},{"source_id":"S5","title":"Quality of group decisions by board members: a hidden-profile experiment","publisher":"Emerald Publishing, Management Decision","url":"https://www.sciencedirect.com/org/science/article/pii/S0025174721000434","source_class":"PRIMARY_RESEARCH","publication_date":"2021","accessed_at":"2026-08-02","claims_supported":["In an experiment involving 141 Dutch nonprofit board members, only about one-fifth of groups selected the objectively best option under hidden-profile conditions.","Initial majority preference strongly influenced the collective decision.","Participants remained confident and satisfied despite poor objective performance.","The tested discussion procedures improved perceived reflection but not objective decision quality."]},{"source_id":"S6","title":"RAND Methodological Guidance for Conducting and Critically Appraising Delphi Panels","publisher":"RAND Corporation","url":"https://www.rand.org/pubs/tools/TLA3082-1.html","source_class":"AUTHORITATIVE_SECONDARY","publication_date":"2023","accessed_at":"2026-08-02","claims_supported":["Delphi is an established iterative, anonymous, structured group process for decisions under uncertainty and incomplete information.","Delphi collects expert judgments before controlled exposure to others' answers.","Independent elicitation followed by feedback is substantial procedural prior art."]},{"source_id":"S7","title":"ISO/IEC 42001:2023 — Artificial intelligence management systems","publisher":"International Organization for Standardization","url":"https://www.iso.org/standard/42001","source_class":"STANDARD","publication_date":"2023-12","accessed_at":"2026-08-02","claims_supported":["ISO/IEC 42001 specifies requirements for establishing, implementing, maintaining, and continually improving an AI management system.","The standard addresses responsible AI governance, risk treatment, traceability, transparency, and reliability.","Organizations developing, providing, or using AI are identifiable potential adopters of governance workflow controls."]},{"source_id":"S8","title":"Understanding WCAG 2.2 Success Criterion 4.1.3: Status Messages","publisher":"World Wide Web Consortium Web Accessibility Initiative","url":"https://www.w3.org/WAI/WCAG22/Understanding/status-messages","source_class":"STANDARD","publication_date":"2025","accessed_at":"2026-08-02","claims_supported":["The disappearance or absence of visible content can convey state in ways unavailable to nonsighted users unless equivalent programmatic information is provided.","Expanded, collapsed, hidden, and restored interface states must be exposed to assistive technology through appropriate semantics.","User testing is advisable because excessive alerts can also harm accessibility."]},{"source_id":"S9","title":"National employment and wage data by occupation, May 2025","publisher":"U.S. Bureau of Labor Statistics","url":"https://www.bls.gov/news.release/ocwage.t01.htm","source_class":"OFFICIAL_ORGANIZATION_DATA","publication_date":"2026","accessed_at":"2026-08-02","claims_supported":["The May 2025 mean annual wage was $148,100 for software developers and $117,490 for web and digital-interface designers.","Professional labor is the dominant resource-equivalent cost driver for a prototype and production integration."]},{"source_id":"S10","title":"Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts","publisher":"Authors / ACM (arXiv copy)","url":"https://arxiv.org/abs/2012.05370","source_class":"PRIMARY_RESEARCH","publication_date":"2020-12-09","accessed_at":"2026-08-02","claims_supported":["A 2,140-participant experiment found that displaying algorithmic risk assessments changed decision processes in simulated government settings.","The intervention increased modeled racial disparity in pretrial detention by 1.9 percentage points and reduced government aid by 8.3 percentage points through increased risk aversion.","Decision-support signals can alter value tradeoffs rather than merely improve prediction accuracy."]},{"source_id":"S11","title":"Understanding the Impact of Human Oversight on Discriminatory Outcomes in AI-Supported Decision-Making","publisher":"European Conference on Artificial Intelligence / SSRN","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5113355","source_class":"PRIMARY_RESEARCH","publication_date":"2024-09-09","accessed_at":"2026-08-02","claims_supported":["A mixed-method study involving 1,411 HR and banking professionals found that overseers were equally likely to follow fair and discriminatory AI advice.","Human oversight alone did not prevent discrimination by a generic AI.","Participants asked for better guidance on when to override AI recommendations, and experts called for socio-technical oversight design."]}],"problem_evidence":{"support":"MODERATE","rationale":"External evidence strongly establishes the underlying mechanisms: algorithmic recommendations can change high-stakes judgments, automation bias is a recognized regulatory concern, board groups can follow initial majorities while remaining overconfident, and nominal oversight may fail to prevent discrimination. NIST and OMB also require critical, multidisciplinary, and sometimes independent review. However, no opened source measures the candidate's exact observable state inside real AI deployment boards—simultaneous sponsor recommendation, composite score, peer comments, and facilitation—or establishes its prevalence. The exact workflow problem therefore remains an externally plausible but unmeasured extrapolation.","source_ids":["S1","S2","S5","S10","S11"]},"stakeholder_evidence":{"support":"STRONG","rationale":"OMB identifies concrete authorizers and adopters: agency AI governance boards, Deputy Secretary-level chairs, CAIO vice-chairs, and CAIO-certified pilots. It expressly requires independent review before risk acceptance for high-impact AI uses. EU law creates additional demand for effective, automation-bias-aware oversight, while NIST and ISO describe organizational governance processes. No specific agency or company has committed to this particular prototype, so pull is institutional rather than product-specific.","source_ids":["S1","S2","S3","S7","S11"]},"prior_art":{"proximity":"SUBSTANTIAL_COLLISION","closest_analogues":[{"name":"Time-based de-anchoring in AI-assisted decision-making","similarity":"Temporally controls exposure or response time to reduce anchoring from an AI recommendation and has already been tested in two user experiments.","remaining_difference":"It does not test a multidisciplinary AI deployment board, temporarily hide sponsor and peer signals together, distinguish multiple meanings of blank risk fields, or preserve paired pre/post deliberation notes.","source_ids":["S4"]},{"name":"Delphi structured expert elicitation","similarity":"Obtains independent or anonymous expert judgments before controlled exposure to other experts' responses under uncertainty.","remaining_difference":"Delphi is usually iterative expert elicitation or consensus-building, not a short deployment-approval interface state with recoverable primary evidence, safety overrides, and live reintroduction.","source_ids":["S6"]},{"name":"Private initial judgment before board discussion","similarity":"The board hidden-profile experiment had members privately record an initial preference before collective discussion, closely matching independent writing followed by deliberation.","remaining_difference":"It did not manipulate visibility of sponsor recommendations, aggregate scores, peer comments, or facilitator speech; its discussion procedures did not improve objective decision quality.","source_ids":["S5"]},{"name":"Independent review under NIST AI RMF and OMB M-25-21","similarity":"Existing governance guidance already calls for independent review, documented risk processes, multidisciplinary participation, and critical thinking before risk acceptance.","remaining_difference":"Neither source prescribes a protected sparse interface, temporary signal withholding, diagnostic empty states, or preservation of before-and-after judgments.","source_ids":["S1","S3"]},{"name":"Human-oversight and automation-bias controls under EU AI Act Article 14","similarity":"Requires interfaces and procedures that support effective oversight, awareness of over-reliance, correct interpretation, and override authority.","remaining_difference":"The Act does not prescribe withholding conclusion-like signals or silent independent note formation before collective review.","source_ids":["S2"]}],"distinctive_claim_remaining":"Holding the evidence packet, decision criteria, reviewer identities, time budget, and mandatory independent-writing requirement constant, temporarily withholding the sponsor recommendation, composite score, peer comments, notifications, and nonessential facilitator framing will increase the number and differentiation of correctly case-grounded unresolved-impact and evidence-gap findings captured before discussion, without increasing safety-critical omissions, context-retrieval failures, accessibility failures, misunderstanding of the interface state, or perceived coercive pressure.","confidence":"HIGH"},"implementation_evidence":{"support":"MODERATE","rationale":"A nonproduction prototype is technically straightforward: role-based visibility, timed state transitions, one-action evidence recovery, explicit categorical fields, immutable pre/post notes, and logged override paths use ordinary web and workflow capabilities. NIST, OMB, ISO, and the EU AI Act support documented governance, review, logging, and human authority. WCAG shows that hiding and restoring content requires careful semantic and assistive-technology handling. Feasibility remains unverified for actual review platforms, records-retention rules, privacy treatment of sensitive dissent, labor agreements, accessibility accommodations, emergency review timelines, and organizational authorization.","source_ids":["S1","S2","S3","S7","S8","S9"]},"scores":{"meaningful_impact":{"score":4,"rationale":"If the mechanism reveals otherwise buried harms before consequential deployments, avoided impact could be substantial. Evidence establishes decision distortion but not realized benefit from this workflow.","source_ids":["S2","S10","S11"]},"stakeholder_pull":{"score":4,"rationale":"OMB requires identifiable governance boards and independent review, and professional overseers report needing better override guidance. No adopter has requested this exact product.","source_ids":["S3","S11"]},"incremental_advantage":{"score":3,"rationale":"The comparison against mandatory independent writing is meaningful, but time-based de-anchoring, Delphi, and private pre-discussion judgments already cover much of the causal structure.","source_ids":["S4","S5","S6"]},"distinctiveness_plausibility":{"score":2,"rationale":"The exact bundle and diagnostic empty-state audit trail may be distinctive, but its core sequence—independent judgment, delayed social information, then discussion—is established prior art.","source_ids":["S4","S5","S6"]},"technical_implementability":{"score":4,"rationale":"The prototype requires conventional interface-state, access-control, logging, and form features; the main technical risks are accessibility, evidence recoverability, and secure note storage.","source_ids":["S8","S9"]},"adoption_authority_feasibility":{"score":3,"rationale":"OMB clearly locates federal authority in CAIOs and governance boards and permits certified limited pilots, but an actual board, data owner, records officer, and accessibility authority have not agreed to participate.","source_ids":["S3"]},"evidence_readiness":{"score":3,"rationale":"Mechanism evidence and comparators are adequate for a prototype test, but target-workflow prevalence, effect size, outcome coding reliability, and live-board acceptability require proprietary workflow observation and testing.","source_ids":["S4","S5","S10","S11"]},"safety_net_benefit":{"score":4,"rationale":"The workflow could surface unresolved harms and make unassessed states visible before authorization, while logged overrides and immediate restoration offer rollback. It could also conceal context or pressure reviewers if poorly implemented.","source_ids":["S1","S2","S8"]},"scalability":{"score":3,"rationale":"A configurable workflow could be reused across review boards, but institutional records rules, platform integrations, decision taxonomies, accessibility needs, and urgent-review exceptions will require local adaptation.","source_ids":["S3","S7","S8"]}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"10K_TO_50K","scope":"Build a nonproduction clickable or lightweight functional prototype, sanitize two archived cases, obtain approvals, and conduct a counterbalanced usability study with 6–8 authorized reviewers.","confidence":"MODERATE","assumptions":["Approximately 120–260 mixed hours of design, development, governance research, study administration, and analysis.","Existing archived cases can be sanitized without extensive legal discovery.","Reviewer time is counted as a resource-equivalent cost.","No production security authorization or platform integration is included."],"source_ids":["S3","S9"]},"initial_deployment_startup":{"band_2026_usd":"50K_TO_250K","scope":"Production-grade implementation for one governance workflow, including role-based visibility, audit logging, evidence recovery, accessibility remediation, privacy and records review, security testing, and administrator controls.","confidence":"MODERATE","assumptions":["Approximately 600–1,300 professional hours across software, interface design, security, accessibility, legal/privacy, and governance roles.","An existing authenticated review platform and records system are available for integration.","The deployment is limited to one organization and one review pathway."],"source_ids":["S2","S3","S8","S9"]},"operational_launch":{"band_2026_usd":"250K_TO_1M","scope":"Launch across several review teams or business units, with production integration, training, change management, case migration, monitoring, independent evaluation, and incident/rollback procedures.","confidence":"LOW","assumptions":["Approximately 1,500–4,000 professional hours plus reviewer training and change-management time.","Two to four workflow variants require configuration.","No major procurement replacement or classified-data environment is included.","An adequately powered shadow evaluation is included before live use."],"source_ids":["S3","S7","S8","S9"]},"annual_recurring":{"band_2026_usd":"50K_TO_250K","scope":"Ongoing product administration, support, security and accessibility review, audit-log governance, taxonomy maintenance, training refreshes, and periodic outcome evaluation.","confidence":"LOW","assumptions":["Approximately 0.25–0.75 combined FTE plus hosting and periodic specialist reviews.","The feature runs within an existing platform rather than as a standalone enterprise system.","Sensitive notes have a defined retention and access policy.","Costs exclude downstream remediation of harms discovered by reviewers."],"source_ids":["S1","S7","S8","S9"]}},"verified_pipeline_gates":{"externally_supported_problem":{"status":"YES","reason":"Multiple primary and official sources support automation bias, decision-signal effects, group majority influence, and shortcomings of nominal human oversight, although exact prevalence in AI deployment boards is unmeasured.","source_ids":["S2","S5","S10","S11"]},"externally_credible_adopter_or_authorizer":{"status":"YES","reason":"OMB identifies agency AI governance boards, Deputy Secretary-level chairs, and CAIOs as governance authorities; CAIOs can certify limited pilots and must establish independent review before high-impact risk acceptance.","source_ids":["S3"]},"distinct_testable_incremental_claim":{"status":"YES","reason":"The proposal isolates temporary withholding as the treatment while holding independent writing and evidence constant, with measurable benefit and harm outcomes. Existing prior art makes this contrast necessary and interpretable.","source_ids":["S4","S5","S6"]},"bounded_next_evidence_step":{"status":"YES","reason":"A two-case, 6–8 reviewer counterbalanced shadow usability test is bounded, reversible, nonoperational, and uses a strong full-information independent-writing comparator.","source_ids":["S3","S4","S5"]},"no_unresolved_safety_or_authority_stop":{"status":"UNCERTAIN","reason":"The shadow design avoids live decision authority and permits immediate rollback, but no partner has resolved consent, records retention, confidentiality of dissent, accessibility accommodations, or data-owner authorization. These are preconditions, not issues that web evidence can close.","source_ids":["S3","S8"]},"credible_cost_scope_and_range":{"status":"YES","reason":"All four estimates state included scope and labor assumptions and are consistent with official professional wage evidence, but organization-specific integration and compliance costs remain uncertain.","source_ids":["S9"]}},"next_evidence_step":"After written approval from one governance chair or CAIO, the archived-case data owner, privacy/records officials, and accessibility lead, run a counterbalanced nonproduction usability study with 6–8 authorized reviewers and two sanitized archived deployment cases. Each participant completes one case using full-information mandatory independent writing and the other using protected withholding; randomize order and case-condition assignment. Before discussion, measure unique correctly case-grounded concerns, correct classification of examined-no-concern versus unexamined or unavailable evidence, safety-critical facts recalled, evidence-retrieval success, completion time, accessibility failures, understanding of why content is absent, and perceived pressure. Blind two coders to condition and report agreement. Stop and restore the ordinary interface for any unrecoverable evidence, safety-critical omission attributable to withholding, inaccessible state transition, or distress. Treat the mechanism as infeasible if any safety-critical omission is attributable to it, if more than one reviewer misunderstands the state, or if necessary evidence cannot be recovered immediately. Treat absence of a directional improvement in differentiated grounded findings as a reason not to fund a larger trial, not as proof of no effect. If feasibility thresholds pass, preregister and power a multi-case shadow trial against both full-information independent writing and a consider-the-opposite prompt before any live deployment use.","blocking_evidence":["No direct observation establishes how often real AI deployment reviews expose sponsor recommendations, aggregate scores, peer comments, and facilitator framing before independent judgment.","No identified organization has committed reviewers, archived cases, platform access, or data-owner approval.","The incremental effect beyond mandatory independent writing has not been measured in an AI governance workflow.","The candidate has no validated coding rubric or estimated effect size for correctly grounded unresolved impacts.","Accessibility of timed hiding, evidence recovery, and state reintroduction has not been tested with disabled reviewers.","Privacy, records-retention, privilege, labor, and anti-retaliation treatment of preserved dissent notes remain organization- and jurisdiction-specific.","The cost of integrating with a real governance platform and security environment is unknown.","Effects under urgent decisions, remote/hybrid meetings, strong sponsor hierarchy, and culturally different interpretations of silence are unknown."],"research_disposition":"PARTNERED_RESEARCH_PROGRAM","world_novelty_boundary":"This evaluation searched only bounded public web sources. It found substantial procedural prior art but no opened source describing the exact combined AI-deployment-review implementation. That is not evidence of world novelty. Patentability, freedom to operate, market size, realized impact, and exhaustive product or literature novelty remain unmeasured.","arm":"PROPOSAL_FIRST","candidate_version":0,"controller_recommendation":{"action":"STOP_EMPIRICAL_RESEARCH_NEEDED","repairable":true,"material_progress_observed":true,"progress_targets":["Secure a named governance-board or CAIO partner plus written data-owner, privacy/records, and accessibility authorization for a sanitized shadow study.","Document the target workflow to establish whether conclusion-like signals actually precede independent judgment and how blank fields are currently interpreted.","Produce an accessible prototype with one-action evidence recovery, explicit state semantics, immutable pre/post notes, logged overrides, and immediate rollback.","Predefine blinded outcome coding, inter-rater reliability, feasibility thresholds, safety halts, and the full-information independent-writing comparator.","Complete the 6–8 reviewer counterbalanced usability study without attributable safety-critical omissions, inaccessible transitions, unrecoverable evidence, or coercive distress.","If feasibility passes, estimate variance and preregister an adequately powered multi-case shadow trial comparing protected withholding with independent writing and consider-the-opposite prompting."],"reason":"Web research verifies a consequential underlying problem, a credible federal authorizer class, strong adjacent standards, and a falsifiable incremental claim. It also reveals substantial collision with time-based de-anchoring, Delphi, and private pre-discussion judgment. The remaining question—whether temporary recoverable absence adds benefit beyond independent writing without safety or accessibility costs—requires proprietary workflow observation and live participant testing, so further bounded web search cannot determine success."}}