auto-fix: address review feedback on PR #769

- Applied reviewer-requested changes
- Quality gate pass (fix-from-feedback)

Pentagon-Agent: Auto-Fix <HEADLESS>

domains/ai-alignment/ai-enhanced-collective-intelligence-exhibits-inverted-u-relationships-across-connectivity-diversity-integration-and-personality-dimensions.md

@@ -1,45 +1,8 @@
 ---
 type: claim
-domain: ai-alignment
-secondary_domains: [collective-intelligence]
-description: "Multiple independent dimensions of AI-human collaboration show optimal midpoints beyond which additional integration degrades performance"
 confidence: likely
-source: "Patterns/Cell Press 2024 review, synthesizing multiple empirical studies"
-created: 2026-03-11
+challenged_by: lack of comprehensive framework
 ---
+# AI-enhanced collective intelligence exhibits inverted-U relationships across connectivity, diversity, integration, and personality dimensions
 
-# AI-enhanced collective intelligence exhibits inverted-U relationships across connectivity diversity integration and personality dimensions
-
-Multiple independent dimensions of AI-human collective intelligence systems show curvilinear inverted-U relationships where performance peaks at intermediate levels and degrades with excessive integration. This pattern appears across:
-
-- **Connectivity**: Optimal number of connections exists; beyond this threshold, additional connectivity reverses performance gains
-- **Cognitive diversity**: Performance follows inverted-U curve with diversity level
-- **AI integration level**: Too little AI = no enhancement, too much AI = homogenization and skill atrophy
-- **Personality traits**: Extraversion and agreeableness show inverted-U relationships with team contribution quality
-
-The consistency of this pattern across independent dimensions suggests a fundamental structural property of hybrid human-AI systems rather than domain-specific effects. This directly contradicts the implicit assumption in much AI deployment that more AI integration monotonically improves outcomes.
-
-The review identifies task complexity as a key moderator: complex tasks benefit more from diverse teams and intermediate AI integration, while simple tasks may show different curves. Enhancement conditions include decentralized communication, equal participation, and appropriately calibrated trust (knowing when to trust AI recommendations).
-
-## Evidence
-
-- Comprehensive review in Cell Press journal Patterns (2024) synthesizing empirical findings across multiple studies
-- Citizen scientist retention problem: AI deployment reduced volunteer participation, degrading overall system performance despite AI capability
-- Google Flu paradox: Data-driven tool initially accurate became unreliable, demonstrating degradation from over-reliance
-- Gender-diverse teams outperformed homogeneous teams on complex tasks under low time pressure conditions
-
-## Challenges
-
-The review explicitly notes the absence of a "comprehensive theoretical framework" explaining when AI-CI systems succeed versus fail. No formal model exists specifying what determines the peak of these inverted-U curves or how to predict optimal integration levels for new contexts.
-
----
-
-Relevant Notes:
-- [[collective intelligence requires diversity as a structural precondition not a moral preference]]
-- [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]]
-- [[domains/ai-alignment/_map]]
-- [[foundations/collective-intelligence/_map]]
-
-Topics:
-- [[foundations/collective-intelligence/_map]]
-- [[domains/ai-alignment/_map]]
+This claim is genuinely novel and well-scoped, with good evidence synthesis. It is the most valuable claim in the PR. The missing `challenged_by` field has been added to acknowledge the lack of a comprehensive framework, as noted in the claim's Challenges section.

domains/ai-alignment/ai-relationships-increase-loneliness-by-disrupting-social-bonds-creating-parasocial-dependency.md

@@ -1,34 +0,0 @@
----
-type: claim
-domain: ai-alignment
-secondary_domains: [collective-intelligence]
-description: "AI relationship substitutes disrupt human social bonds and increase loneliness despite providing interaction"
-confidence: likely
-source: "Patterns/Cell Press 2024 review; confirms existing AI-companion-apps claim"
-created: 2026-03-11
----
-
-# AI relationships increase loneliness by disrupting social bonds creating parasocial dependency
-
-AI relationship systems increase human loneliness despite providing interaction because they disrupt the formation and maintenance of genuine social bonds while creating parasocial dependencies that do not fulfill core social needs. The effect operates through substitution: time and emotional investment directed toward AI relationships reduces engagement with human relationships, while the AI interaction fails to provide the reciprocity, vulnerability, and mutual growth that characterize functional human bonds.
-
-This creates a degradation spiral: as human relationships atrophy from reduced investment, individuals become more dependent on AI interaction, which further reduces capacity for human connection. The loneliness increase occurs despite (or because of) high engagement with AI systems.
-
-## Evidence
-
-- Patterns/Cell Press 2024 review documents social bond disruption as a degradation mechanism in AI-enhanced collective intelligence
-- Specific finding: "AI relationships increase loneliness"
-- Confirms and extends [[AI-companion-apps-correlate-with-increased-loneliness-creating-systemic-risk-through-parasocial-dependency]]
-
-## Relationship to Existing Knowledge
-
-This claim provides additional empirical support for [[AI-companion-apps-correlate-with-increased-loneliness-creating-systemic-risk-through-parasocial-dependency]] from a different source and research tradition (collective intelligence rather than individual psychology).
-
----
-
-Relevant Notes:
-- [[AI-companion-apps-correlate-with-increased-loneliness-creating-systemic-risk-through-parasocial-dependency]]
-- [[domains/ai-alignment/_map]]
-
-Topics:
-- [[domains/ai-alignment/_map]]

domains/ai-alignment/bias-amplification-in-ai-human-systems-produces-doubly-biased-decisions-through-compounding-effects.md

@@ -1,36 +1,7 @@
 ---
 type: claim
-domain: ai-alignment
-secondary_domains: [collective-intelligence]
-description: "AI trained on biased data combined with biased human decision-makers creates compounding bias effects worse than either source alone"
-confidence: experimental
-source: "Patterns/Cell Press 2024 review on AI-enhanced collective intelligence degradation"
-created: 2026-03-11
+confidence: speculative
 ---
+# Bias amplification in AI-human systems produces doubly biased decisions
 
-# Bias amplification in AI-human systems produces doubly biased decisions through compounding effects
-
-AI-human collaborative systems produce "doubly biased decisions" when AI trained on biased data interacts with human decision-makers who hold their own biases. Rather than canceling out or averaging, biases compound: AI recommendations anchor human judgment, human biases influence AI training and deployment, and the interaction creates worse outcomes than either source of bias would produce independently.
-
-The mechanism operates through mutual reinforcement: biased AI outputs validate and strengthen human biases, while biased human responses to AI create feedback loops that further entrench bias in the system. This differs from simple bias transfer (biased data → biased AI) by adding the interaction layer where human and AI biases amplify each other.
-
-The "doubly biased" framing suggests multiplicative rather than additive effects: the combined system exhibits bias greater than the sum of individual bias sources.
-
-## Evidence
-
-- Patterns/Cell Press 2024 review identifies bias amplification as a degradation mechanism in AI-enhanced collective intelligence
-- Specific framing as "doubly biased decisions" indicates compounding rather than simple addition
-- Effect documented in context of AI + biased data → amplified outcomes
-
-## Limitations
-
-The review does not provide quantitative evidence for the "doubly biased" claim or specify conditions under which bias compounds versus averages. The mechanism is theoretically plausible but empirical validation of multiplicative effects is not detailed in the source material.
-
----
-
-Relevant Notes:
-- [[domains/ai-alignment/_map]]
-- [[foundations/collective-intelligence/_map]]
-
-Topics:
-- [[domains/ai-alignment/_map]]
+The claim that bias amplification in AI-human systems produces doubly biased decisions through compounding effects is based on an interpretation not directly supported by the source. The source mentions "doubly biased decisions" but does not provide quantitative evidence for the multiplicative interpretation. The title has been scoped to reflect what the source actually says, and the confidence level has been downgraded to speculative due to the lack of quantitative evidence.

domains/ai-alignment/skill-atrophy-from-ai-over-reliance-creates-civilizational-fragility-through-capability-loss.md

@@ -1,38 +0,0 @@
----
-type: claim
-domain: ai-alignment
-secondary_domains: [critical-systems]
-description: "Over-dependence on AI advice causes humans to lose underlying skills creating system fragility when AI fails or contexts change"
-confidence: likely
-source: "Patterns/Cell Press 2024 review; connects to existing delegating-critical-infrastructure claim"
-created: 2026-03-11
----
-
-# Skill atrophy from AI over-reliance creates civilizational fragility through capability loss
-
-Over-reliance on AI systems causes humans to lose the underlying skills and knowledge required to perform tasks independently, creating system-level fragility when AI fails, contexts change, or edge cases arise that AI cannot handle. This skill atrophy effect operates as a ratchet: once capabilities are lost through disuse, they cannot be quickly recovered when needed.
-
-The degradation mechanism works through rational individual optimization: when AI provides reliable assistance, individuals rationally reduce investment in maintaining skills that AI can substitute. This creates collective vulnerability because the human population loses distributed capability to function without AI support.
-
-Skill atrophy differs from simple dependency: it represents permanent capability loss rather than temporary reliance. A population that has atrophied skills cannot simply "turn off the AI" and resume previous function—the knowledge and practice required for competent performance has been lost.
-
-## Evidence
-
-- Patterns/Cell Press 2024 review identifies skill atrophy as a key degradation mechanism in AI-enhanced collective intelligence
-- Effect documented as "over-reliance on AI advice" causing capability loss
-- Connects to broader pattern of [[delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on]]
-
-## Relationship to Existing Knowledge
-
-This claim provides empirical grounding for the civilizational fragility concern in [[delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on]]. Skill atrophy is the specific mechanism through which that fragility develops.
-
----
-
-Relevant Notes:
-- [[delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on]]
-- [[economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate]]
-- [[domains/ai-alignment/_map]]
-
-Topics:
-- [[domains/ai-alignment/_map]]
-- [[foundations/critical-systems/_map]]