---
id: wiki-2026-0508-algorithmic-fairness
title: Algorithmic Fairness
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [AI fairness, ML bias, fair ML, algorithmic bias, group fairness]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: conceptual
tags: [fairness, bias, ml-ethics, disparate-impact, audit, ai-governance, compas, gender-shades]
raw_sources: []
last_reinforced: 2026-05-09
github_commit: pending
---

# Algorithmic Fairness

## 📌 한 줄 통찰
> **"매 group 의 differential impact 의 measure + mitigate"**. 매 ML system 의 bias = data + algorithm + deployment. **Pre-processing / In-processing / Post-processing** 의 3 stage 의 fairness intervention.

## 📖 핵심

### 매 fairness 의 definition

#### 1. Group fairness
- **Demographic parity**: 매 group 의 same positive rate.
- **Equal opportunity**: 매 group 의 same TPR.
- **Equalized odds**: TPR + FPR 둘 다 same.
- **Calibration**: 매 score 의 same meaning.

→ 매 mathematically incompatible (impossibility theorem).

#### 2. Individual fairness
- 매 similar individual 의 similar treatment.
- "Similar" 의 definition 어려움.

#### 3. Counterfactual fairness
- 매 prediction 의 unchanged if protected attribute 변경.
- 매 causal model 필요.

### 매 famous case

#### COMPAS (recidivism)
- ProPublica 2016.
- 매 black defendant 의 false positive rate 2x.
- 매 risk score 의 racial bias.

#### Gender Shades (face recognition)
- Joy Buolamwini, Timnit Gebru 2018.
- 매 dark-skinned female 의 error rate 35% (vs light male 1%).

#### Amazon hiring AI (2018)
- 매 resume + woman keyword 의 penalty.
- 매 historical bias 의 reproduce.

→ 매 abandon.

#### Apple Card (2019)
- 매 credit limit 의 woman 의 lower (same financial profile).

#### Healthcare risk score (2019)
- 매 black patient 의 lower risk score (same need).
- 매 historical 의 healthcare expenditure (proxy bias).

### 매 source of bias

#### Data
- **Historical**: 매 past discrimination.
- **Representation**: 매 underrepresented group.
- **Measurement**: 매 different signal quality per group.

#### Algorithm
- 매 objective function 의 majority bias.
- 매 feature selection.
- 매 hyperparameter tuning.

#### Deployment
- 매 user feedback loop.
- 매 differential adoption.
- 매 contextual mismatch.

### 매 mitigation strategy

#### Pre-processing (data)
- 매 reweight sample.
- 매 generate synthetic minority.
- 매 protected attribute 의 remove (often insufficient — proxy).

#### In-processing (training)
- 매 fairness constraint 의 add to loss.
- Adversarial debiasing.
- 매 prejudice remover.

#### Post-processing (output)
- 매 threshold 의 group-specific.
- 매 score calibration.
- Rejection option classification.

### 매 audit / measurement

#### Disparate impact
- 4/5 rule (US EEOC).
- 매 minority 의 selection rate < 80% of majority = potential discrimination.

#### AIF360 (IBM)
- 매 70+ fairness metric.
- 매 9 mitigation algorithm.
- Open source.

#### Aequitas (Univ. Chicago)
- 매 audit toolkit.

#### Google What-If Tool
- 매 interactive exploration.

### 매 regulation
- **EU AI Act**: 매 high-risk 의 bias check.
- **NYC Local Law 144**: hiring AI 의 annual audit.
- **EEOC** (US): employment discrimination.
- **GDPR Article 22**: 매 automated decision 의 human review.

### 매 organizational practice

#### Pre-deployment
- 매 audit.
- 매 disparate impact analysis.
- 매 adversarial test.
- 매 model card 의 disclosure.

#### Production
- 매 monitoring.
- 매 user feedback.
- 매 quarterly review.

#### Incident
- 매 user 의 complaint.
- 매 root cause.
- 매 remediation.

## 💻 Code

### Disparate impact (AIF360)
```python
from aif360.datasets import BinaryLabelDataset
from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric

# Wrap your dataset
dataset = BinaryLabelDataset(
    df=df,
    label_names=['hired'],
    protected_attribute_names=['gender'],
    favorable_label=1,
    unfavorable_label=0,
)

# Pre-train metric
metric = BinaryLabelDatasetMetric(
    dataset,
    unprivileged_groups=[{'gender': 0}],
    privileged_groups=[{'gender': 1}],
)
print(f"Disparate impact: {metric.disparate_impact()}")
# < 0.8 = potential bias (4/5 rule)

# After model: classification metric
classified = BinaryLabelDataset(...)  # with predictions
clf_metric = ClassificationMetric(
    dataset, classified,
    unprivileged_groups=[{'gender': 0}],
    privileged_groups=[{'gender': 1}],
)
print(f"Equal opportunity diff: {clf_metric.equal_opportunity_difference()}")
print(f"Avg odds diff: {clf_metric.average_odds_difference()}")
```

### Reweighting (pre-processing)
```python
from aif360.algorithms.preprocessing import Reweighing

rw = Reweighing(
    unprivileged_groups=[{'gender': 0}],
    privileged_groups=[{'gender': 1}],
)
dataset_rw = rw.fit_transform(dataset)

# Train on reweighted data
model = train(dataset_rw)
```

### Adversarial debiasing (in-processing)
```python
from aif360.algorithms.inprocessing import AdversarialDebiasing
import tensorflow as tf

sess = tf.Session()
debiased = AdversarialDebiasing(
    privileged_groups=[{'gender': 1}],
    unprivileged_groups=[{'gender': 0}],
    scope_name='debiased',
    debias=True,
    sess=sess,
)
debiased.fit(dataset_train)
preds = debiased.predict(dataset_test)
```

### Threshold optimization (post-processing)
```python
from aif360.algorithms.postprocessing import EqOddsPostprocessing

eq_odds = EqOddsPostprocessing(
    unprivileged_groups=[{'gender': 0}],
    privileged_groups=[{'gender': 1}],
)
eq_odds.fit(dataset_val, predictions_val)
predictions_balanced = eq_odds.predict(predictions_test)
```

### Fairness in CI
```python
def fairness_test(model, X_test, y_test, groups):
    """매 release 의 fairness gate."""
    accuracies = {}
    for group_value in np.unique(groups):
        mask = groups == group_value
        accuracies[group_value] = model.score(X_test[mask], y_test[mask])
    
    disparity = max(accuracies.values()) - min(accuracies.values())
    if disparity > 0.05:
        raise FairnessFailure(f"Disparity: {disparity:.2%}")
```

### Counterfactual test
```python
def counterfactual_test(model, instance, protected_attr='gender'):
    """매 attribute 의 flip 의 prediction change."""
    pred_original = model.predict([instance])
    
    flipped = instance.copy()
    flipped[protected_attr] = 1 - flipped[protected_attr]
    pred_flipped = model.predict([flipped])
    
    if pred_original != pred_flipped:
        return f"Bias detected: {protected_attr} flip changes prediction"
```

## 🤔 결정 기준

| Risk level | Mitigation |
|---|---|
| Low (spam filter) | Audit log + monitor |
| Medium (recommendation) | + Disparate impact check |
| High (hiring, lending) | + Pre/in/post-processing |
| Critical (criminal justice, medical) | + Strict regulation + human review |

**기본값**: 4/5 rule check + per-group accuracy + counterfactual test + disclosure.

## 🔗 Graph
- 부모: [[AI-Ethics]] · [[AI-Accountability]]
- 변형: [[Group-Fairness]]

## 🤖 LLM 활용
**언제**: 매 ML system 의 deployment review. 매 audit. 매 high-risk 의 design.
**언제 X**: Specific legal advice (lawyer). Specific implementation 의 detail.

## ❌ 안티패턴
- **"Just remove protected attribute"**: 매 proxy 의 still bias.
- **Single fairness metric**: 매 trade-off 의 ignore.
- **No audit**: silent bias.
- **Historical data 의 trust**: 매 past discrimination 의 amplify.
- **Disparate impact 의 fix only**: 매 individual 의 unfair still.

## 🧪 검증 / 중복
- Verified.
- 신뢰도 B (academic + industry consensus).
- Related: [[AI-Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-09 | Manual cleanup — fairness type + famous case + AIF360 code + 결정 |
