---
id: wiki-2026-0508-ai-for-social-good
title: AI for Social Good (AI4SG)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [AI4SG, AI for Good, social impact AI, public-interest AI, humanitarian AI, SDG AI]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: conceptual
tags: [ai4good, social-impact, sdg, humanitarian, climate-ai, public-interest, ai-ethics]
raw_sources: []
last_reinforced: 2026-05-09
github_commit: pending
inferred_by: Claude Opus 4.7 (manual cleanup 2026-05-09)
tech_stack:
  language: process / multidisciplinary
  applicable_to: [Non-profit, Research, Government, Corporate Social Responsibility]
---

# AI for Social Good (AI4SG)

## 📌 한 줄 통찰 (The Karpathy Summary)
> **AI 의 commercial 외 사용**. 매 UN SDG (climate, health, education, equity) 의 AI 응용. 매 vendor 의 lab + non-profit + government 의 partnership. **Hype 보다 partnership + data + sustainability 가 중요**.

## 📖 구조화된 지식 (Synthesized Content)

### 정의 + scope
AI 의 application 의 social benefit 목표:
- 매 UN SDG (Sustainable Development Goals) 의 mapping.
- Non-profit / NGO / government partnership 의 흔함.
- 매 commercial value < social value.

매 typical area:
- Climate & sustainability.
- Healthcare (특히 underserved).
- Education (digital divide).
- Disaster response.
- Conservation.
- Accessibility.
- Agriculture (food security).

### UN SDG 의 AI mapping

#### SDG 3: Health
- **Diagnosis**: malaria detection (mobile + ML), TB X-ray screening.
- **Outbreak prediction**: 매 epidemic 의 early signal.
- **Drug discovery**: 매 rare disease 의 candidate.
- **Mental health**: chatbot support (Wysa, Woebot).
- **매 example**: Google's diabetic retinopathy screening (India, Thailand).

#### SDG 13: Climate
- **Forest monitoring**: 매 satellite imagery 의 deforestation detect.
- **Energy optimization**: grid balance, demand prediction.
- **Climate model**: 매 weather / temperature.
- **Methane leak detect**: satellite + ML.
- **매 example**: Google's flood forecasting (India, Bangladesh).

#### SDG 4: Education
- **Personalized learning**: Khanmigo, Duolingo Max.
- **Translation**: real-time multi-lingual.
- **Literacy**: 매 student 의 reading support.
- **Access**: low-bandwidth countries.
- **매 example**: AI tutor 의 1.7B underserved.

#### SDG 11: Cities / Disaster
- **Disaster routing**: 매 evacuation optimize.
- **Population displacement**: satellite + social media.
- **Damage assessment**: 매 earthquake / flood.
- **매 example**: Google Crisis Response.

#### SDG 14, 15: Biodiversity
- **Species identification**: iNaturalist (10M user).
- **Anti-poaching**: 매 patrol route + acoustic detection.
- **Coral reef monitoring**.
- **매 example**: Wildbook (whale shark identification).

#### SDG 5, 10: Equity
- **Bias detect**: 매 system 의 audit.
- **Voice for marginalized**: low-resource language.
- **Accessibility**: 매 disability (vision, hearing).
- **매 example**: Project Euphonia (atypical speech).

### 매 organization 의 program
- **Google AI for Social Good**: $25M+ funding.
- **Microsoft AI for Earth / Health / Accessibility**.
- **IBM Sustainability Accelerator**.
- **Anthropic Claude for Climate / Health / Education**.
- **OpenAI Nonprofit grants**.
- **DeepMind AlphaFold (free)**: protein structure.
- **UNICEF MagicBox**.
- **Partnership on AI**.

### 매 framework / methodology

#### Theory of Change
1. 매 social problem 의 root cause.
2. 매 intervention (AI 의 specific role).
3. 매 outcome (short / long-term).
4. 매 measurement.
5. 매 stakeholder (beneficiary, partner, funder).

#### Co-design
- 매 affected community 의 participation.
- 매 design 의 representation.
- 매 deployment 의 local trust.
- 매 outcome 의 feedback.

→ "Nothing about us without us".

#### Human Rights Impact Assessment (HRIA)
- 매 AI 의 deployment 의 human rights effect.
- Privacy, freedom of expression, equality.
- UN B-Tech Project.

### 매 challenge

#### Data scarcity
- 매 underserved region 의 data 부족.
- 매 sensitive (health) 의 collection 어려움.
- Synthetic data, transfer learning, federated learning.

#### Sustainability
- 매 pilot 의 funding 끝 → 매 deployment 의 abandon.
- Local capacity building.
- Open-source.

#### Bias
- 매 training data 의 Western / urban bias.
- 매 underserved 의 misrepresent.
- Local validation.

#### Ethics / consent
- 매 vulnerable 의 informed consent.
- 매 data sovereignty (indigenous data).
- 매 deployment 의 community approval.

#### Verification
- 매 claim 의 evidence.
- "AI4SG washing" (marketing 의 hype + reality 부족).
- 매 outcome 의 measurement 어려움.

### 매 implementation pattern

#### Phase 1: Discovery
- Problem definition (community + experts).
- Data audit.
- Stakeholder mapping.
- Feasibility.

#### Phase 2: Co-design
- Local team partnership.
- Iterative prototype.
- 매 community 의 feedback.

#### Phase 3: Pilot
- Small-scale deploy.
- 매 outcome 의 measurement.
- 매 unintended effect 의 monitor.

#### Phase 4: Scale
- 매 partner 의 capacity build.
- Open-source 의 enable.
- Sustainability (funding, governance).

#### Phase 5: Sustain / Transition
- 매 local ownership.
- Continuous improvement.
- 매 exit plan.

### Critique

#### "AI Solutionism"
- 매 social problem 의 root cause 가 social, not technical.
- 매 AI 의 surface fix.
- 매 tech-driven solution 의 limit.

#### "AI Colonialism"
- 매 Western / Global North 의 deploy + Global South.
- 매 local agency 의 erasure.
- Data extractivism.

#### "Pilotitis"
- 매 pilot 의 abundance + scale 의 부족.
- 매 academic / company 의 self-promote.
- 매 sustainable impact 의 부족.

→ Critical perspective + design 의 integration 가 답.

## 💻 패턴 (응용)

### Federated learning (privacy)
```python
# 매 hospital 의 own data + central model.
import flwr as fl

class HospitalClient(fl.client.NumPyClient):
    def __init__(self, model, local_data):
        self.model = model
        self.data = local_data
    
    def fit(self, parameters, config):
        self.model.set_weights(parameters)
        self.model.fit(self.data)
        return self.model.get_weights(), len(self.data), {}

# 매 hospital 의 data 가 own.
# 매 model update 의 share.
fl.client.start_numpy_client(server_address='central:8080', client=HospitalClient(...))
```

→ 매 patient data 의 hospital 의 own. Central model 의 collective learning.

### Low-resource translation (NLLB)
```python
from transformers import pipeline

# Meta NLLB 200 language
translator = pipeline('translation', model='facebook/nllb-200-distilled-600M')

# 매 underserved language
result = translator('Hello', src_lang='eng_Latn', tgt_lang='swh_Latn')
print(result)
```

→ 매 community 의 mother tongue.

### Satellite imagery analysis (deforestation)
```python
# 매 region 의 매 month 의 satellite image
# Diff = deforestation rate
import rasterio
from sentinelhub import SHConfig, BBoxSplitter

# Sentinel-2 의 10m resolution
config = SHConfig()
config.sh_client_id = '...'

# 매 area 의 매 month image
images = fetch_sentinel(area, dates=monthly_2024)
deforestation_mask = ml_model.predict(images)
```

→ Forest watch 의 ML.

### Disaster response (population)
```python
# 매 social media + satellite + cell tower data
import pandas as pd

def estimate_displacement(events):
    cell_density_before = load_ctd('before-event')
    cell_density_after = load_ctd('after-event')
    
    # 매 cell 의 population shift
    delta = cell_density_after - cell_density_before
    return delta
```

→ Refugee / displacement track.

### Health (medical imaging, low-resource)
```python
# 매 mobile-friendly model
import tensorflow as tf

model = tf.keras.applications.MobileNetV3Small(weights='imagenet')
# Fine-tune on disease classification

# Quantize for edge
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
quantized = converter.convert()

# 매 doctor 의 phone 의 deploy
```

→ Off-grid / low-connectivity.

### Accessibility (ASR for atypical speech)
```python
# Project Euphonia (Google) 식
# 매 user 의 own data + base ASR
from transformers import WhisperForConditionalGeneration

model = WhisperForConditionalGeneration.from_pretrained('openai/whisper-base')

# Fine-tune on user's own atypical speech
# (small dataset, transfer learning).
```

→ Cerebral palsy / ALS 의 communication.

### Co-design checklist
```yaml
# Pre-deployment audit
co_design:
  - Local team 의 partnership: Y/N
  - Affected community 의 input: Y/N
  - Pilot 의 small + measurable: Y/N
  - Outcome 의 transparent disclosure: Y/N
  - Local capacity building: Y/N
  - Sustainable funding: Y/N
  - Exit plan / transition: Y/N
  - Open-source / shared: Y/N
```

### Impact measurement
```python
# 매 outcome 의 quantify
class ImpactTracker:
    def __init__(self):
        self.baseline = self.measure_baseline()
    
    def track(self, intervention_period):
        post = self.measure_after()
        delta = post - self.baseline
        
        # 매 confounder 의 control (RCT 가 ideal)
        return {
            'metric': 'lives_saved',
            'baseline': self.baseline,
            'post': post,
            'delta': delta,
            'confidence': self.compute_confidence(),
        }
```

→ 매 honest reporting (vs hype).

## 🤔 의사결정 기준 (Decision Criteria)

| 상황 | 추천 |
|---|---|
| Problem 가 social structural | AI 의 limit + structural solution |
| Tech 가 augment | AI4SG 의 perfect fit |
| Vulnerable population | Co-design + ethics review |
| 매 region 의 data 부족 | Federated / synthetic / transfer |
| Privacy critical | Federated / on-device |
| Off-grid | Edge / mobile / quantize |
| Sustainability concern | Local capacity + open-source |

**기본값**: Co-design + impact measurement + sustainability plan + ethics review. 매 pilot 의 scale path.

## ⚠️ 모순 및 업데이트 (Contradictions & Updates)
- **Solutionism vs structural**: 매 social problem 의 tech 의 limit.
- **Pilot vs scale**: 매 academic / company 의 pilot 의 abundance + scale 의 부족.
- **Open-source vs sustainability**: 매 open 의 funding model 어려움.
- **Local vs global**: 매 local context 의 specific need vs global model 의 generality.
- **Corporate motive**: 매 vendor 의 social good 의 marketing vs sincere commitment.
- **AI ethics 의 cost**: 매 ethics review 의 development friction.
- **매 SDG 의 hype**: 매 vendor 의 SDG checkbox + 매 actual impact 의 부족.

## 🔗 지식 연결 (Graph)
- 부모: [[AI-Ethics]]
- 응용: [[Federated-Learning]]
- 관련: [[AI Humanism]] · [[AI Accountability]] · [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]]

## 🤖 LLM 활용 힌트 (How to Use This Knowledge)

**언제 이 지식을 쓰는가:**
- 매 nonprofit / NGO 의 AI partnership.
- 매 corporate CSR 의 AI program design.
- 매 SDG 의 AI mapping.
- 매 grant proposal 의 framing.
- 매 pilot 의 sustainability planning.

**언제 쓰면 안 되는가:**
- Specific country 의 regulation (local expert).
- Crisis 의 immediate response (humanitarian agency).
- Technical implementation 의 detail (engineer).
- Cynicism 의 platform (constructive critique 만).

## ❌ 안티패턴 (Anti-Patterns)
- **Solutionism**: 매 social problem 의 tech 의 fix.
- **Colonial deploy**: local agency 의 erasure.
- **Pilotitis**: 매 pilot 의 scale 의 plan 부족.
- **AI4SG washing**: marketing 의 hype + reality 부족.
- **Co-design 의 token**: 매 community input 의 superficial.
- **Open-source 의 abandon**: maintenance 의 부족.
- **Outcome 의 unmeasured**: claim 의 evidence X.
- **Ethics review 의 skip**: vulnerable 의 harm.

## 🧪 검증 상태 (Validation)
- **정보 상태:** verified (concept-level).
- **출처 신뢰도:** B (UN Global Pulse, Partnership on AI, Stanford HAI, Google AI for Social Good reports).
- **검토 이유:** Manual cleanup. 매 specific 프로그램 의 detail 가 evolving.

## 🧬 중복 검사 (Duplicate Check)
- **기존 유사 문서:** [[AI Humanism]] (related), [[AI-Ethics]] (parent), [[AI 거버넌스 정책(AI Usage Policy)|AI-Governance-Policy]] (related).
- **처리 방식:** KEEP (specific application focus).
- **처리 이유:** AI4SG 가 distinct application area + methodology.

## 🕓 변경 이력 (Changelog)
| 날짜 | 변경 내용 | 처리 방식 | 신뢰도 |
|------|-----------|-----------|--------|
| 2026-05-08 | P-Reinforce Phase 1 정규화 | UPDATE | A |
| 2026-05-09 | Manual cleanup — SDG mapping + code pattern + 비판 + 안티패턴 + co-design 추가 | UPDATE | B |
