---
id: wiki-2026-0508-algorithmic-transparency
title: Algorithmic Transparency
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [AI transparency, model transparency, algorithmic openness, ML explainability]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: conceptual
tags: [transparency, xai, explainability, auditability, ai-governance, model-card, open-source]
raw_sources: []
last_reinforced: 2026-05-09
github_commit: pending
---

# Algorithmic Transparency

## 📌 한 줄 통찰
> **"매 black box 의 light"**. 매 input + algorithm + output 의 visibility. **Disclosure → Explainability → Auditability** 의 3 layer. 매 user trust + regulatory compliance.

## 📖 핵심

### 매 3 layer

#### Layer 1: Disclosure (basic)
- 매 AI 의 use 의 fact.
- 매 purpose.
- 매 data source (general).
- 매 user 의 inform.

#### Layer 2: Explainability (model)
- 매 prediction 의 reasoning.
- SHAP / LIME / counterfactual.
- Attention visualization.
- Feature importance.

#### Layer 3: Auditability (regulator / public)
- 매 model 의 detail (weights, training).
- 매 audit log.
- 매 third-party verify.
- 매 reproducibility.

### 매 transparency 의 type

#### Voluntary
- 매 vendor 의 self-disclose.
- Model card (Mitchell 2019).
- Datasheet for datasets.
- Public benchmark.

#### Required (regulation)
- EU AI Act 의 high-risk.
- GDPR Article 22 (right to explanation).
- NYC LL144 (hiring AI audit).
- China 의 generative AI registration.

#### Open source
- 매 weight 의 release.
- 매 training data 의 (often partial).
- 매 architecture.

### 매 transparency 의 spectrum

| Level | Example |
|---|---|
| 1. Closed | GPT-4 (architecture 미공개) |
| 2. Documented | GPT-4 (paper 약간) |
| 3. Open weight | Llama 3, Mistral (weight 공개, training 미공개) |
| 4. Reproducible | OLMo (data + code 공개) |
| 5. Auditable | 매 third-party 의 audit |

→ 매 model 의 different level.

### 매 user-facing disclosure

#### "AI used"
- 매 chatbot 의 explicit.
- 매 generated content 의 watermark.
- 매 deepfake 의 disclosure (regulation).

#### "Why this decision?"
- 매 loan / hire 의 reason.
- GDPR right to explanation.

#### "Data used"
- 매 train data summary.
- Wikipedia, web crawl, etc.
- 매 sensitive 의 disclose.

### 매 model card (Mitchell 2019)

Component:
1. Model details (name, version, type).
2. Intended use (primary, out-of-scope).
3. Performance (per-group).
4. Training data.
5. Evaluation data.
6. Ethical consideration.
7. Caveat / recommendation.

→ 매 standard.

### 매 datasheet (Gebru 2018)

Dataset 의 documentation:
1. Motivation.
2. Composition.
3. Collection process.
4. Preprocessing / labeling.
5. Uses.
6. Distribution.
7. Maintenance.

### 매 trade-off

#### IP / competitive
- 매 full disclosure 의 trade secret 잃음.
- 매 vendor 의 reluctance.

#### Security
- 매 full disclosure 의 adversarial attack.
- 매 jailbreak 의 easier.

#### Privacy
- 매 training data 의 individual identification.
- 매 GDPR 의 conflict.

#### User overload
- 매 too much info 의 overwhelm.
- 매 simplified summary 필요.

### 매 best practice

#### Frontier model
- 매 model card.
- 매 capability + limit.
- 매 known risk.
- 매 evaluation result.

#### Production AI
- 매 user-facing disclosure.
- 매 explainability (SHAP / LIME).
- 매 audit log.
- 매 appeal channel.

#### Open-source
- 매 weight.
- 매 training data (or summary).
- 매 reproducibility.

## 💻 Code

### Model card (yaml)
```yaml
model_name: ChurnPredictor
version: 3.1.0
created: 2026-05-09
license: MIT

intended_use: |
  Predict customer churn for SaaS billing dashboard.

intended_users: |
  Customer success team.

out_of_scope:
  - Automatic cancellation
  - Pricing decisions

training_data:
  source: 2025-2026 production users.
  size: 1.2M samples.
  bias_warning: |
    - 80% US customer (geographic bias).
    - 65% B2B SaaS (industry bias).

performance:
  overall: { accuracy: 0.87, auc: 0.91 }
  by_group:
    - { group: 'US', accuracy: 0.88 }
    - { group: 'EU', accuracy: 0.83 }   # disparity
    - { group: 'APAC', accuracy: 0.79 }

ethical_consideration: |
  - 매 prediction 의 customer success review.
  - 매 false positive 의 outreach cost.

review_cycle: quarterly
```

### Datasheet
```yaml
dataset_name: customer_churn_v3
version: 2026-05
size: 1.2M rows
license: Internal

motivation: |
  Train ML model to predict churn.

composition:
  features:
    - login_frequency: int
    - subscription_tier: enum
    - support_tickets: int
    - payment_method: enum
  
  protected_attributes:
    - country
    - industry
    - account_size

collection:
  source: production database
  method: SQL extract + anonymize
  consent: ToS agreement

preprocessing:
  - PII removed
  - Outliers winsorized

uses:
  recommended:
    - Churn prediction
  not_recommended:
    - Cross-customer analysis (re-identification risk)
```

### XAI 의 user-facing
```python
import shap
import streamlit as st

@app.route('/predictions/<id>/explain')
def explain(id):
    decision = db.predictions.find(id)
    
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values([decision.features])
    
    top_features = sorted(
        zip(feature_names, shap_values[0]),
        key=lambda x: -abs(x[1])
    )[:5]
    
    return {
        'prediction': decision.value,
        'date': decision.timestamp,
        'top_factors': [
            {'feature': name, 'impact': float(impact)}
            for name, impact in top_features
        ],
        'how_to_appeal': '/appeal',
    }
```

### Audit log
```python
@trace
async def predict(features, user_id):
    pred = model.predict(features)
    
    await db.audit_log.insert({
        'user_id': user_id,
        'features_hash': sha256(features),
        'prediction': pred.value,
        'confidence': pred.confidence,
        'model_version': MODEL_VERSION,
        'timestamp': datetime.now(),
    })
    
    return pred
```

### User disclosure (chatbot)
```tsx
function ChatHeader() {
  return (
    <div className="ai-disclosure">
      🤖 You're chatting with an AI assistant powered by Claude.
      <a href="/about-ai">Learn more</a>
    </div>
  );
}
```

## 🤔 결정 기준

| Context | Transparency level |
|---|---|
| Internal tool | Audit log + model card |
| Customer-facing | + User disclosure |
| Regulated (medical, legal) | + Audit + explainability + appeal |
| Frontier (general AI) | + Capability disclosure + safety eval |
| Open-source | + Weight + training summary |

**기본값**: Disclosure + audit log + per-prediction explanation. 매 high-stakes 의 더 strict.

## 🔗 Graph
- 부모: [[AI-Ethics]] · [[AI-Governance]] · [[AI-Accountability]]
- 변형: [[Explainable-AI-XAI]] · [[Model-Card]] · [[Datasheet-for-Datasets]]
- 응용: [[GDPR-Article-22]] · [[EU-AI-Act-Transparency]] · [[NYC-LL144]]
- Tools: [[SHAP]] · [[LIME]] · [[Model-Card-Toolkit-Google]]
- Adjacent: [[Open-Source-AI]] · [[Algorithmic-Fairness]] · [[Right-to-Explanation]]

## 🤖 LLM 활용
**언제**: 매 production AI 의 transparency design. 매 user trust 의 build.
**언제 X**: Specific legal compliance (lawyer). Trade secret area.

## ❌ 안티패턴
- **No disclosure**: trust 잃음.
- **Full disclosure + privacy violation**: balance.
- **Model card 의 stale**: 매 release 의 update.
- **"AI 의 use" 의 hide**: deception.
- **Explainability 의 fake**: post-hoc rationalize.

## 🧪 검증 / 중복
- Verified.
- 신뢰도 B.
- Related: [[AI-Accountability]] · [[Algorithmic-Fairness]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-09 | Manual cleanup — 3 layer + spectrum + model card / datasheet code |
