2nd/10_Wiki/Topics/AI_and_ML/Human Centered AI (HCAI).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-human-centered-ai-hcai	Human-Centered AI (HCAI)	10_Wiki/Topics	verified	self	HCAI human-centered AI Shneiderman augmented intelligence AI-assisted copilot pattern	none	A	0.94	applied	ai hcai hci ux augmented-intelligence copilot		2026-05-10	pending	language applicable_to AI / UX Product AI Design UX

Human-Centered AI (HCAI)

매 한 줄

"매 AI 가 humans 의 replace 의 X — 매 humans 의 augment". Shneiderman 2022. 매 high human control + high computer automation 의 동시. 매 modern: 매 Copilot, Cursor, Notion AI 의 design pattern. 매 vs autonomous AI.

매 핵심

매 framework (Shneiderman 2D)

• 매 X-axis: human control (low → high).
• 매 Y-axis: automation (low → high).
• HCAI = 매 right-top quadrant: 매 high control + high automation.

매 4 modes (Endsley)

• Manual.
• Decision support.
• Consensual (AI suggests, human approves).
• Monitored automation.
• Full automation.

매 응용

1. Copilot (code).
2. Cursor / Continue (IDE).
3. Notion AI (writing).
4. Medical AI (clinician support).
5. Photoshop generative fill.
6. CAD AI tools.

매 design principles

• Reliable (high reliability + safety).
• Safe (graceful degradation).
• Trustworthy (explainable + auditable).
• Transparent (about AI involvement).
• Reversible (undo).
• Auditable (log decisions).

💻 패턴

Suggestion + accept (Copilot pattern)

function aiSuggest(context: Context): Suggestion[] {
  const candidates = llm.generate(context, n=3);
  return candidates.map(c => ({
    text: c.text,
    confidence: c.confidence,
    explanation: c.explanation,
  }));
}

// 매 UI: 매 ghost text + Tab 의 accept, Esc 의 reject

Confidence-aware UX

function renderSuggestion(s: Suggestion) {
  if (s.confidence > 0.9) return <strong>{s.text}</strong>;  // 매 confident
  if (s.confidence > 0.7) return <span>{s.text}</span>;
  return <span style={{opacity: 0.5}}>{s.text}? <small>(uncertain)</small></span>;
}

Explainable suggestion

def llm_suggest_with_reason(context, llm):
    return llm.generate(f"""
{context}

Output:
- suggestion: <code or text>
- reason: <why this — 1 sentence>
- confidence: <0-1>
- alternatives: <2-3 alternatives>
""")

Reversible action

class UndoStack {
  private stack: Action[] = [];
  
  do(action: Action) {
    action.execute();
    this.stack.push(action);
  }
  
  undo() {
    const last = this.stack.pop();
    last?.reverse();
  }
}

AI disclosure (transparency)

<div class="ai-output">
  <p>{output}</p>
  <small class="ai-disclosure">Generated with AI. Verify before using.</small>
</div>

Audit log

def log_ai_decision(input, output, model_version, user_decision):
    db.audit.insert({
        'timestamp': now(),
        'user_id': current_user.id,
        'model': model_version,
        'input_hash': hash(input),
        'output': output,
        'user_action': user_decision,  # 매 accepted | modified | rejected
    })

Graceful degradation

def feature_with_ai_fallback(input):
    try:
        return ai_enhance(input)
    except (RateLimitError, ServiceDown):
        return manual_default(input)

Calibrated confidence display

function confidenceLabel(score) {
  if (score > 0.95) return { label: 'High confidence', color: 'green' };
  if (score > 0.8) return { label: 'Likely', color: 'blue' };
  if (score > 0.6) return { label: 'Possible', color: 'orange' };
  return { label: 'Uncertain — verify', color: 'red' };
}

Diff-based AI edit (review)

// 매 LLM proposes change → user sees diff → approves/rejects
function showDiffForApproval(original: string, proposed: string) {
  return {
    diff: computeDiff(original, proposed),
    actions: ['Accept', 'Reject', 'Edit'],
  };
}

Per-user adaptation

class PersonalizedAI:
    def __init__(self, user):
        self.user = user
        self.preferences = load_preferences(user)
    
    def suggest(self, context):
        base = llm.generate(context)
        return adapt_to_style(base, self.preferences.style)
    
    def feedback(self, suggestion, accepted):
        self.preferences.update(suggestion, accepted)

Multi-step approval (agent)

def hcai_agent(goal, tools):
    plan = llm.plan(goal)
    for step in plan:
        approval = ask_user(f'Approve: {step}?')
        if not approval: return 'cancelled'
        result = execute(step, tools)
        update_user(result)

AI vs human comparison (audit)

def compare_ai_vs_human(items, ai_outputs, human_outputs):
    return {
        'agreement': sum(1 for a, h in zip(ai_outputs, human_outputs) if a == h) / len(items),
        'ai_overrides_kept': count(item.ai_overridden_by_human for item in items),
    }

Critique mode

def ai_critique(user_work, llm):
    return llm.generate(f"""Critique constructively. Don't rewrite.

User work:
{user_work}

Output:
- strengths: ...
- weaknesses: ...
- specific suggestions: ...""")

매 결정 기준

상황	Mode
Code editor	Suggestion + accept
Medical diagnosis	Decision support
Writing	Critique + suggest
Auto-routing	Monitored automation
High-stakes	Full HITL
Low-stakes ambient	Background AI

기본값: 매 high control + high automation + 매 reversible + 매 explainable + 매 disclosure + 매 audit log.

🔗 Graph

• 부모: AI · Human Computer Interaction
• 변형: Augmented-Intelligence
• 응용: Human-in-the-loop (HITL) · AI_Safety_and_Alignment
• Adjacent: Ethics & AI · Emotional-AI (Affective Computing) · Excessive Agency

🤖 LLM 활용

언제: 매 모든 AI product. 매 productivity. 매 medical. 언제 X: 매 fully autonomous infra.

❌ 안티패턴

• AI replaces human: 매 trust loss.
• Hide AI involvement: 매 transparency 의 violate.
• No undo: 매 destructive.
• Confidence theater: 매 fake high score.
• Auto-accept default: 매 human disempower.

🧪 검증 / 중복

• Verified (Shneiderman HCAI 2022, Endsley levels of automation).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Shneiderman + 매 suggest / disclosure / undo / audit code