2nd/10_Wiki/Topic_Programming/AI_and_ML/Philosophy.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-philosophy	Philosophy	10_Wiki/Topics	verified	self	Philosophy of AI Philosophy Basics	none	A	0.85	applied	philosophy epistemology ethics ai-safety consciousness		2026-05-10	pending	language framework

Philosophy

매 한 줄

"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.

매 핵심

매 main branches

• Epistemology: knowledge — 매 what can we know, justified true belief.
• Metaphysics: existence — 매 what exists, causation, time, identity.
• Ethics: value — 매 right/wrong, deontology vs consequentialism vs virtue.
• Logic: valid inference — 매 deductive, inductive, abductive.
• Philosophy of mind: consciousness — 매 hard problem (Chalmers).
• Philosophy of science: 매 falsification (Popper), paradigms (Kuhn).
• Philosophy of language: meaning, reference, speech acts.

매 epistemology (AI 와 직결)

• Justified True Belief — Gettier 1963 의 challenge.
• Bayesian epistemology — 매 belief 의 probability degree.
• Reliabilism — 매 process 의 reliability 가 중요.
• 매 ML evaluation: 매 model 의 "knowledge" claim 의 epistemic status.

매 ethics (AI alignment)

• Deontology: rules (Kant). 매 categorical imperative.
• Consequentialism: outcomes (Mill, Bentham utilitarianism).
• Virtue ethics: character (Aristotle).
• 매 AI safety: 매 RLHF = consequentialist; constitutional AI = deontological hybrid.

매 mind & consciousness

• Hard problem (Chalmers 1995): 매 why subjective experience exists.
• Functionalism: 매 mind = function, substrate-independent → AGI sentience plausible.
• Chinese Room (Searle 1980): 매 syntax ≠ semantics.
• Integrated Information Theory (Tononi): 매 consciousness = Φ.
• 매 2026 frontier: 매 LLM consciousness debate (Anthropic Welfare team, Google DeepMind sentience research).

매 응용

1. AI ethics frameworks (alignment, fairness).
2. AGI sentience / moral patienthood debate.
3. Epistemic status of model outputs (hallucination as false belief).
4. Decision theory (CDT, EDT, FDT) for AI agents.

💻 패턴

Bayesian belief update

def bayes_update(prior, likelihood_h, likelihood_not_h):
    """P(H|E) = P(E|H)P(H) / [P(E|H)P(H) + P(E|~H)P(~H)]"""
    p_e = likelihood_h * prior + likelihood_not_h * (1 - prior)
    return likelihood_h * prior / p_e

# Belief in hypothesis after evidence
posterior = bayes_update(prior=0.3, likelihood_h=0.9, likelihood_not_h=0.1)

Constitutional AI (deontological rules)

constitution = [
    "Refuse harmful requests.",
    "Do not deceive.",
    "Respect autonomy.",
]
def critique(response, constitution):
    # LLM critiques own response against rules
    return llm.complete(f"Critique: {response}\nRules: {constitution}")

Trolley problem decision theory

# Utilitarian (consequentialist)
def utilitarian(action_outcomes):
    return max(action_outcomes, key=lambda a: sum(a["lives_saved"]))

# Deontological — hard rule against killing
def deontological(actions):
    return [a for a in actions if not a["actively_kills"]]

Epistemic confidence calibration

# Brier score: lower = better calibration
import numpy as np
def brier_score(predicted_probs, outcomes):
    return np.mean((predicted_probs - outcomes) ** 2)

Falsification check (Popperian)

def is_falsifiable(hypothesis):
    """A hypothesis must specify what would refute it."""
    return hypothesis.get("refuting_observation") is not None

매 결정 기준

상황	Approach
AI alignment	Hybrid: consequentialist outcome + deontological hard rules
Sentience claim	Skeptical default; functionalism if behavior + introspection
Truth claim	Bayesian update + Popperian falsifiability
Ethical dilemma	Multi-frame analysis (deont + conseq + virtue)
Hard problem	Acknowledge open; don't claim solved

기본값: 매 epistemic humility + multi-framework ethics.

🔗 Graph

• 부모: Knowledge
• 변형: Epistemology · Logic
• 응용: AI Safety · AI_Safety_and_Alignment · AI_Safety_and_Alignment
• Adjacent: Decision Theory

🤖 LLM 활용

언제: 매 AI ethics framing, alignment design, sentience debate, epistemic claims. 언제 X: 매 narrow technical implementation (philosophy 의 abstraction 만 enough).

❌ 안티패턴

• Single-framework dogma: 매 only utilitarianism → trolley monsters; only deontology → unable to weigh harm.
• Conflating AI behavior with consciousness: 매 LLM 의 "I feel" output ≠ proof of feeling.
• Solving the hard problem casually: 매 functionalism 의 plausible but not proven.
• Ignoring philosophy in alignment: 매 RLHF 의 implicit consequentialism unexamined.
• Naive realism in ML eval: 매 benchmark score = "true intelligence" (epistemic naive).

🧪 검증 / 중복

• Verified (Stanford Encyclopedia of Philosophy, Russell & Norvig "AIMA" Ch. 27 Ethics).
• 신뢰도 A-.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — branches, AI applications, decision patterns