c766f5d857
- Source: inbox/archive/2025-09-00-gaikwad-murphys-laws-alignment.md - Domain: ai-alignment - Extracted by: headless extraction cron (worker 4) Pentagon-Agent: Theseus <HEADLESS>
43 lines
2.7 KiB
Markdown
43 lines
2.7 KiB
Markdown
---
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type: claim
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domain: ai-alignment
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description: "Murphy's Law states the alignment gap always wins unless actively routed around; MAPS framework provides four design levers for managing misspecification"
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confidence: experimental
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source: "Madhava Gaikwad, Murphy's Laws of AI Alignment (2025)"
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created: 2026-03-11
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---
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# Alignment gap cannot be eliminated but can be mapped bounded and managed through MAPS framework
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**Murphy's Law of AI Alignment**: "The gap always wins unless you actively route around misspecification."
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The alignment gap—the difference between specified objectives and true human values—is not a problem to be solved but a structural feature to be managed. Gaikwad proposes the MAPS framework as a conceptual model for managing (not eliminating) this gap through four design levers:
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1. **Misspecification**: Map where feedback is unreliable
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2. **Annotation**: Improve feedback quality in known problem regions
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3. **Pressure**: Adjust optimization intensity based on confidence
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4. **Shift**: Adapt as contexts change
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This reframes alignment from "eliminate the gap" to "bound and navigate the gap." The formal result on calibration oracles provides theoretical support: knowing where problems occur enables polynomial rather than exponential learning, suggesting that structural interventions (not just more data) are necessary.
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## Evidence
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**Murphy's Law formulation**: "The gap always wins unless you actively route around misspecification" (Gaikwad 2025).
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**MAPS framework**: Four design levers for managing alignment gap—Misspecification, Annotation, Pressure, Shift (Gaikwad 2025).
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**Theoretical foundation**: The exponential sample complexity result proves the gap cannot be eliminated through more data alone—you need structural interventions (Gaikwad 2025).
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## Challenges
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The framework is conceptual rather than operational. It names the levers but does not specify how to pull them in practice. "Map misspecification" and "adjust optimization pressure" are design principles, not algorithms. The paper does not demonstrate MAPS applied to a concrete alignment problem.
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---
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Relevant Notes:
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- [[feedback-misspecification-creates-exponential-sample-complexity-barrier-that-calibration-oracles-overcome]] — formal foundation for why structural intervention is necessary
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- [[the specification trap means any values encoded at training time become structurally unstable as deployment contexts diverge from training conditions]] — related structural instability
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- [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]] — convergent conclusion about managing rather than eliminating gaps
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Topics:
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- [[domains/ai-alignment/_map]]
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