---
id: wiki-2026-0508-activism
title: Activism
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Advocacy, Social Movement, Civic Engagement, Tech Activism]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: applied
tags: [activism, ethics, social-movement, civic-tech, ai-ethics]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: N/A
  framework: civic-tech tooling
---

# Activism

## 매 한 줄
> **"매 organized action toward social/political change"**. Activism = 매 collective effort 의 power structure 의 challenge — 매 protest, lobbying, boycott, mutual aid, civic-tech 의 spectrum. 2026 의 매 algorithmic platform + AI policy 의 central battleground 의 emerge — 매 EU AI Act, US executive order, labor union (Hollywood SAG-AFTRA AI clause) 의 outcome 의 activism-driven.

## 매 핵심

### 매 tactic spectrum
- **Institutional**: lobbying, policy comment, litigation (EFF, ACLU model).
- **Direct action**: protest, strike, blockade, boycott.
- **Mutual aid**: 매 community 의 direct support — 매 state 의 bypass.
- **Cultural**: art, media, narrative shift.
- **Digital / civic-tech**: open data, FOIA tooling, mapping, OSINT.

### 매 movement lifecycle (Stages of a Social Movement, Blumer 1969)
1. **Emergence** — 매 unrest, scattered grievance.
2. **Coalescence** — 매 leadership + identity.
3. **Bureaucratization** — 매 formal org.
4. **Decline** — 매 success / repression / co-optation / mainstream.

### 매 tech-activism domain (2026 hot)
- **AI ethics**: bias audit, dataset transparency, algorithmic accountability.
- **Labor**: gig worker union (Uber, Doordash), tech worker walkout (Google AI ethics 2018+, OpenAI ex-staff).
- **Privacy**: surveillance pushback (Pegasus, Clearview), e2e encryption defense.
- **Climate tech**: Tech Won't Build It, Stop Cop City.
- **Open source**: copyleft, fair-source debate, AI-output licensing.
- **Disinformation**: fact-check infra, election integrity.

### 매 응용
1. Coalition building (cross-org alliance).
2. Narrative campaign (framing, messaging).
3. Policy intervention (model regulation comment, e.g. NIST AI RMF).
4. Whistleblowing infra (SecureDrop, Signal).
5. Crisis mapping (Ushahidi, Bellingcat OSINT).

## 💻 패턴

### Secure communication (org-internal)
```bash
# 매 Signal — 매 default for activist coordination
# 매 disappearing message + safety number verify + screen-lock
# 매 Wire / Element (Matrix) 의 self-host alternative
```

### FOIA / public records request (US)
```python
# muckrock-style template
import requests
req = {
    "agency_id": 123,
    "title": "Records re: facial recognition vendor contracts 2024-2026",
    "documents_requested": "All contracts, MOUs, RFPs related to FRT...",
    "fee_waiver_request": True,  # public interest
}
# muckrock.com API 의 submit 또는 direct email per agency
```

### OSINT verification (geolocation)
```python
# 매 image metadata + reverse geocode + sun-shadow angle
from PIL import Image
from PIL.ExifTags import GPSTAGS, TAGS
img = Image.open("photo.jpg")
exif = img._getexif() or {}
gps = next((v for t, v in exif.items() if TAGS.get(t) == "GPSInfo"), {})
# 매 cross-ref Sentinel-2 satellite imagery + OpenStreetMap
```

### Mutual aid request board (simple)
```python
# Flask + sqlite — 매 community fridge / ride share / bail fund
@app.post("/request")
def post_request():
    db.execute("INSERT INTO need (kind, area, contact, ts) VALUES (?, ?, ?, ?)",
               (request.form["kind"], request.form["area"], hashed(request.form["contact"]), now()))
```

### Algorithmic audit (bias)
```python
from sklearn.metrics import confusion_matrix
def disparate_impact(y_pred, y_true, group):
    rates = {}
    for g in set(group):
        mask = group == g
        rates[g] = (y_pred[mask] == 1).mean()
    return min(rates.values()) / max(rates.values())  # 매 <0.8 의 80% rule violation
```

### Petition / call-tool (5Calls-style)
```ts
// 매 lookup rep by zip → display script → log call
const reps = await fetch(`/api/reps?zip=${zip}`).then(r => r.json());
// reps: [{ name, phone, party, district }]
```

## 매 결정 기준
| 상황 | Tactic |
|---|---|
| Policy window open (bill in committee) | Lobbying + public comment + coalition letter |
| Corporate harm, public-facing | Boycott + media campaign + shareholder resolution |
| Acute injustice (police violence) | Protest + legal observers + bail fund |
| Long-term cultural shift | Narrative / art / education |
| Tech worker concern | Internal organizing + ethical board + walkout (last resort) |
| State surveillance | Encryption advocacy + legal (EFF model) |

**기본값**: 매 multi-tactic + 매 sustained (movement >> moment) + 매 affected community 의 leadership.

## 🔗 Graph
- 부모: [[Civic Engagement]]
- 응용: [[Algorithmic Accountability]]
- Adjacent: [[AI Ethics]]

## 🤖 LLM 활용
**언제**: 매 policy doc summarization, 매 outreach draft, 매 large-corpus FOIA review.
**언제 X**: 매 surveillance target identification, 매 deepfake disinfo generation, 매 confidential source data 의 cloud LLM 의 upload.

## ❌ 안티패턴
- **Slacktivism**: 매 hashtag 의 only — 매 sustained organizing 의 X.
- **Voluntourism**: 매 affected community 의 lead-not 의 outsider 의 dominate.
- **Burnout culture**: 매 unsustainable pace — 매 movement 의 collapse.
- **OPSEC fail**: 매 plain-text channel, 매 metadata leak — 매 source 의 endanger.
- **Performative ally**: 매 statement 의 only without resource shift.
- **Astroturfing**: 매 fake grassroots — 매 trust 의 destroy 의 long-term.
- **Single-tactic monoculture**: 매 only protest 또는 only lobby — 매 multi-vector 의 effective.

## 🧪 검증 / 중복
- Verified (Blumer 1969 *Collective Behavior*; Tarrow *Power in Movement* 3rd ed; EFF / Access Now annual reports; AI Now Institute reports 2018-2025).
- 신뢰도 B (매 normative + tactical mix — context-dependent).

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 2026 tech-activism focus + civic-tech patterns |
