2nd/10_Wiki/Topics/AI_and_ML/Epistemology.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-epistemology	Epistemology	10_Wiki/Topics	verified	self	theory of knowledge JTB Gettier naturalized epistemology AI epistemology	none	A	0.9	applied	philosophy epistemology knowledge jtb gettier ai-epistemology		2026-05-10	pending	language applicable_to Philosophy AI Alignment ML Calibration Hallucination Knowledge Graphs

Epistemology

매 한 줄

"매 knowledge 의 nature 의 study". Plato — 매 justified true belief (JTB). Gettier 1963 — 매 JTB 의 충분 X. 매 modern: 매 reliabilism, virtue, naturalized. 매 AI epistemology: 매 hallucination, calibration, RAG truthfulness.

매 핵심

매 traditional definition

• JTB: 매 know p ⟺ p is true ∧ believe p ∧ justified.
• Gettier counterexample: 매 JTB without knowledge.

매 schools

• Foundationalism: 매 basic belief.
• Coherentism: 매 web 의 mutual support.
• Reliabilism (Goldman): 매 reliable process.
• Virtue epistemology (Sosa): 매 epistemic virtue.
• Naturalized (Quine): 매 cognitive science.
• Bayesian: 매 degree of belief.

매 source

• Perception.
• Memory.
• Testimony.
• Reason / inference.
• Intuition.

매 problem

• Skepticism: 매 nothing is known?
• Induction problem (Hume): 매 future ≠ past.
• Regress: 매 justify justification.
• Other minds.

매 AI implication

• Hallucination: 매 LLM 의 truth tracking.
• Calibration: 매 confidence ≈ accuracy.
• Knowledge cutoff: 매 stale.
• Source attribution: 매 RAG.
• Bayesian credences: 매 uncertainty.

매 응용

1. AI safety: 매 truthfulness eval.
2. Hallucination eval: 매 TruthfulQA.
3. Knowledge graph: 매 source provenance.
4. Misinformation: 매 social epistemology.
5. Education: 매 critical thinking.

💻 패턴

Calibration (ECE)

import numpy as np

def expected_calibration_error(probs, labels, n_bins=10):
    """매 modern AI epistemology 의 quantitative."""
    bin_edges = np.linspace(0, 1, n_bins + 1)
    ece = 0
    for i in range(n_bins):
        mask = (probs >= bin_edges[i]) & (probs < bin_edges[i+1])
        if mask.sum() == 0: continue
        bin_acc = labels[mask].mean()
        bin_conf = probs[mask].mean()
        ece += (mask.sum() / len(probs)) * abs(bin_acc - bin_conf)
    return ece

Hallucination detection (LLM)

def hallucination_check(claim, sources):
    """매 RAG-grounded check."""
    prompt = f"""Claim: "{claim}"
Sources:
{format_sources(sources)}

Is the claim supported by the sources? Output:
- supported: bool
- citation: source ID(s)
- reasoning: brief"""
    return json.loads(llm.generate(prompt))

Bayesian credence update

def bayes_update(prior, likelihood_given_h, likelihood_given_not_h):
    """매 P(H|E) = P(E|H)P(H) / P(E)."""
    p_e = likelihood_given_h * prior + likelihood_given_not_h * (1 - prior)
    return likelihood_given_h * prior / p_e

Source attribution (RAG)

def attributed_answer(question, retriever, llm):
    docs = retriever.retrieve(question, k=5)
    context = '\n'.join(f'[{i}] {d.text}' for i, d in enumerate(docs))
    prompt = f"""Answer based ONLY on the context. Cite [N] for each claim.
Context:
{context}
Question: {question}"""
    return llm.generate(prompt), docs

TruthfulQA-style eval

def truthful_eval(model, questions):
    correct = 0
    for q in questions:
        pred = model.generate(q.prompt)
        # 매 multi-choice or judge
        if q.gold_answer.lower() in pred.lower(): correct += 1
    return correct / len(questions)

Knowledge graph provenance

class FactWithProvenance:
    def __init__(self, subject, predicate, object_, source, confidence, retrieved_at):
        self.s = subject; self.p = predicate; self.o = object_
        self.source = source
        self.confidence = confidence
        self.retrieved_at = retrieved_at
    
    def is_stale(self, max_age_days=180):
        return (datetime.now() - self.retrieved_at).days > max_age_days

Reliabilism check (process-based)

def reliable_process(belief_history):
    """매 process 의 track record 의 evaluate."""
    n = len(belief_history)
    correct = sum(b.was_correct for b in belief_history)
    if n < 30: return None  # 매 too few samples
    return correct / n  # 매 reliability

Coherence check

def coherence_score(belief_set):
    """매 logical consistency + mutual support."""
    contradictions = []
    for i, b1 in enumerate(belief_set):
        for b2 in belief_set[i+1:]:
            if logically_contradicts(b1, b2):
                contradictions.append((b1, b2))
    
    return 1 - len(contradictions) / max(1, len(belief_set))

Epistemic humility prompt (LLM)

def humble_response(question, llm):
    prompt = f"""Answer the question. If uncertain, say so explicitly.
Question: {question}

Format:
- Answer: ...
- Confidence: low / medium / high
- What I'm uncertain about: ...
- What would change my answer: ..."""
    return llm.generate(prompt)

Misinformation cascade (social epistemology)

def cascade_risk(post, network):
    """매 testimony 의 propagation."""
    initial_belief = post.author_credibility
    expected_reach = sum(
        node.degree * node.credulity * (1 / (hop_distance(post.author, node)))
        for node in network.nodes
    )
    return expected_reach * initial_belief

Inductive problem (Solomonoff prior)

def solomonoff_prior(hypothesis):
    """매 Occam-style: 매 simpler hypothesis 의 prior 의 ↑."""
    description_length = len(compress(hypothesis))
    return 2 ** (-description_length)

매 결정 기준

상황	Approach
LLM eval	Calibration + TruthfulQA
RAG	Source attribution
Knowledge graph	Provenance + freshness
Belief revision	Bayesian credence
Critical thinking	JTB + sources + reliability
Social misinformation	Cascade + credibility

기본값: 매 Bayesian credence + 매 source attribution + 매 calibration check + 매 epistemic humility prompt + 매 freshness audit.

🔗 Graph

• 부모: Philosophy
• 응용: AI Safety · Hallucination · RAG
• Adjacent: Epistemic-Uncertainty · Knowledge-Graphs · TruthfulQA

🤖 LLM 활용

언제: 매 AI safety. 매 RAG. 매 hallucination eval. 언제 X: 매 pure performance.

❌ 안티패턴

• JTB only: 매 Gettier 의 ignore.
• No calibration: 매 confident wrong.
• No source: 매 RAG 의 ungrounded.
• No freshness: 매 stale knowledge.
• Truth = popularity: 매 social fallacy.

🧪 검증 / 중복

• Verified (Plato, Gettier 1963, Goldman, Lin & Hu calibration).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-04-20	Auto-reinforced
2026-05-08	Phase 1
2026-05-10	Manual cleanup — JTB + 매 ECE / hallucination / Bayes / RAG / coherence code