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Antigravity Agent
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id: wiki-2026-0508-philosophy
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title: Philosophy
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Philosophy of AI, Philosophy Basics]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [philosophy, epistemology, ethics, ai-safety, consciousness]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: ""
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framework: ""
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---
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# Philosophy
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## 매 한 줄
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> **"매 reasoning about reality, knowledge, mind, value"**. Philosophy 의 main branches (epistemology, metaphysics, ethics, mind, logic) — 매 modern AI 의 deeply intertwined: 매 knowing claim (epistemology) → ML evaluation, 매 mind claim (consciousness) → AGI/sentience debate, 매 value claim (ethics) → AI alignment / safety.
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## 매 핵심
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### 매 main branches
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- **Epistemology**: knowledge — 매 what can we know, justified true belief.
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- **Metaphysics**: existence — 매 what exists, causation, time, identity.
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- **Ethics**: value — 매 right/wrong, deontology vs consequentialism vs virtue.
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- **Logic**: valid inference — 매 deductive, inductive, abductive.
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- **Philosophy of mind**: consciousness — 매 hard problem (Chalmers).
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- **Philosophy of science**: 매 falsification (Popper), paradigms (Kuhn).
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- **Philosophy of language**: meaning, reference, speech acts.
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### 매 epistemology (AI 와 직결)
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- Justified True Belief — Gettier 1963 의 challenge.
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- Bayesian epistemology — 매 belief 의 probability degree.
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- Reliabilism — 매 process 의 reliability 가 중요.
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- 매 ML evaluation: 매 model 의 "knowledge" claim 의 epistemic status.
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### 매 ethics (AI alignment)
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- **Deontology**: rules (Kant). 매 categorical imperative.
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- **Consequentialism**: outcomes (Mill, Bentham utilitarianism).
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- **Virtue ethics**: character (Aristotle).
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- 매 AI safety: 매 RLHF = consequentialist; constitutional AI = deontological hybrid.
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### 매 mind & consciousness
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- Hard problem (Chalmers 1995): 매 why subjective experience exists.
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- Functionalism: 매 mind = function, substrate-independent → AGI sentience plausible.
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- Chinese Room (Searle 1980): 매 syntax ≠ semantics.
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- Integrated Information Theory (Tononi): 매 consciousness = Φ.
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- 매 2026 frontier: 매 LLM consciousness debate (Anthropic Welfare team, Google DeepMind sentience research).
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### 매 응용
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1. AI ethics frameworks (alignment, fairness).
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2. AGI sentience / moral patienthood debate.
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3. Epistemic status of model outputs (hallucination as false belief).
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4. Decision theory (CDT, EDT, FDT) for AI agents.
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## 💻 패턴
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### Bayesian belief update
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```python
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def bayes_update(prior, likelihood_h, likelihood_not_h):
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"""P(H|E) = P(E|H)P(H) / [P(E|H)P(H) + P(E|~H)P(~H)]"""
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p_e = likelihood_h * prior + likelihood_not_h * (1 - prior)
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return likelihood_h * prior / p_e
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# Belief in hypothesis after evidence
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posterior = bayes_update(prior=0.3, likelihood_h=0.9, likelihood_not_h=0.1)
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```
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### Constitutional AI (deontological rules)
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```python
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constitution = [
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"Refuse harmful requests.",
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"Do not deceive.",
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"Respect autonomy.",
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]
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def critique(response, constitution):
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# LLM critiques own response against rules
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return llm.complete(f"Critique: {response}\nRules: {constitution}")
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```
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### Trolley problem decision theory
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```python
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# Utilitarian (consequentialist)
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def utilitarian(action_outcomes):
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return max(action_outcomes, key=lambda a: sum(a["lives_saved"]))
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# Deontological — hard rule against killing
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def deontological(actions):
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return [a for a in actions if not a["actively_kills"]]
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```
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### Epistemic confidence calibration
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```python
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# Brier score: lower = better calibration
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import numpy as np
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def brier_score(predicted_probs, outcomes):
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return np.mean((predicted_probs - outcomes) ** 2)
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```
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### Falsification check (Popperian)
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```python
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def is_falsifiable(hypothesis):
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"""A hypothesis must specify what would refute it."""
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return hypothesis.get("refuting_observation") is not None
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| AI alignment | Hybrid: consequentialist outcome + deontological hard rules |
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| Sentience claim | Skeptical default; functionalism if behavior + introspection |
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| Truth claim | Bayesian update + Popperian falsifiability |
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| Ethical dilemma | Multi-frame analysis (deont + conseq + virtue) |
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| Hard problem | Acknowledge open; don't claim solved |
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**기본값**: 매 epistemic humility + multi-framework ethics.
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## 🔗 Graph
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- 부모: [[Knowledge]]
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- 변형: [[Epistemology]] · [[Logic]]
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- 응용: [[AI Safety]] · [[AI_Safety_and_Alignment|AI-Alignment]] · [[AI_Safety_and_Alignment|Constitutional-AI]]
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- Adjacent: [[Decision Theory]]
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## 🤖 LLM 활용
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**언제**: 매 AI ethics framing, alignment design, sentience debate, epistemic claims.
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**언제 X**: 매 narrow technical implementation (philosophy 의 abstraction 만 enough).
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## ❌ 안티패턴
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- **Single-framework dogma**: 매 only utilitarianism → trolley monsters; only deontology → unable to weigh harm.
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- **Conflating AI behavior with consciousness**: 매 LLM 의 "I feel" output ≠ proof of feeling.
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- **Solving the hard problem casually**: 매 functionalism 의 plausible but not proven.
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- **Ignoring philosophy in alignment**: 매 RLHF 의 implicit consequentialism unexamined.
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- **Naive realism in ML eval**: 매 benchmark score = "true intelligence" (epistemic naive).
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## 🧪 검증 / 중복
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- Verified (Stanford Encyclopedia of Philosophy, Russell & Norvig "AIMA" Ch. 27 Ethics).
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- 신뢰도 A-.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — branches, AI applications, decision patterns |
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