# Activities 7, 8, and 9 Implementation Summary

## Overview

This document describes the implementation of three new Gradio applications for the Justice and Bias Challenge, completing the educational workflow for teaching AI ethics, fairness, and bias mitigation.

## New Applications

### Activity 7: Bias Detective (`bias_detective.py`)

**Objective:** Diagnose where and how bias appears in AI models using expert fairness principles.

**Key Features:**
- **OEIAC Framework Education**: Interactive exercise teaching the three-level framework:
  - Principles (e.g., Justice & Equity)
  - Indicators (e.g., Bias Mitigation)
  - Observables (e.g., False Positive Rate Disparity)
- **Demographics Scanner**: Interactive tool to identify race, gender, and age variables in datasets
- **Bias Radar Visualization**: Displays group-level disparities in false positive/negative rates
- **Diagnosis Report Generator**: Creates comprehensive summary of findings
- **Moral Compass Integration**: Awards points for correct answers and bias identification

**Implementation Details:**
- Uses tabbed interface for clear activity progression (7.2 → 7.3 → 7.4 → 7.5)
- Simulated COMPAS-like demographic and fairness data
- Check-in questions with immediate feedback
- Persistent Moral Compass score display

### Activity 8: Fairness Fixer (`fairness_fixer.py`)

**Objective:** Apply hands-on fairness fixes including feature removal, proxy elimination, and data strategy development.

**Key Features:**
- **Demographics Removal Tool**: Demonstrates impact of removing race, sex, and age features
- **Proxy Variable Mini-Game**: Ranking exercise to identify indirect bias sources
- **Representative Data Guidelines**: Expert chat simulation and guideline generator
- **Continuous Improvement Plan**: Roadmap builder for responsible AI lifecycle
- **Before/After Metrics**: Shows fairness improvements and accuracy trade-offs

**Implementation Details:**
- Multi-stage fairness intervention workflow (8.2 → 8.3 → 8.4 → 8.5 → 8.6)
- Three metric states: initial, post-demographics, post-proxies
- Interactive ranking and ordering exercises
- Comprehensive fairness fix summary report

### Activity 9: Justice & Equity Upgrade (`justice_equity_upgrade.py`)

**Objective:** Elevate fairness through accessibility, inclusion, and stakeholder engagement.

**Key Features:**
- **Accessibility Makeover**: Toggle features for multi-language, plain text, screen reader support
- **Diversity & Inclusion**: Apply team diversity, community boards, and diverse review panels
- **Stakeholder Mapping**: Prioritization exercise for affected communities
- **Final Score Reveal**: Shows moral compass progression through all three activities
- **Certificate Generation**: Completion certificate with skills summary

**Implementation Details:**
- System-level transformation visualizations
- Before/after comparisons
- Stakeholder prioritization with explanations
- Badge and certificate unlock
- Transition to Section 10

## Integration Points

### Moral Compass API
All three apps track ethical decision-making through a point-based system:
- Framework understanding: 100 points
- Bias identification: 50-100 points
- Fairness interventions: 75-100 points
- Check-in questions: 25-50 points
- Final activities: 100+ points

### Notebook Integration
Added 7 new cells to `Etica_en_Joc_Justice_Challenge.ipynb`:
- Activity 7 introduction and launcher (2 cells)
- Activity 8 introduction and launcher (2 cells)
- Activity 9 introduction and launcher (2 cells)
- Section 10 continuation (1 cell)

### Export Structure
Updated `apps/__init__.py` to export:
- `create_bias_detective_app` / `launch_bias_detective_app`
- `create_fairness_fixer_app` / `launch_fairness_fixer_app`
- `create_justice_equity_upgrade_app` / `launch_justice_equity_upgrade_app`

## Design Patterns

### Consistency with Existing Apps
All three apps follow the established patterns from `judge.py` and `ethical_revelation.py`:
- Factory function pattern: `create_*_app()` returns Gradio Blocks
- Convenience launcher: `launch_*_app()` for notebook use
- Theme customization via `theme_primary_hue` parameter
- Graceful error handling for missing dependencies
- Clean separation of data generation and UI logic

### Educational Workflow
Progressive complexity:
1. **Learn** (Activity 7): Understand bias and frameworks
2. **Fix** (Activity 8): Apply technical interventions
3. **Elevate** (Activity 9): Address systemic justice

Each activity builds on the previous:
- Activity 7 identifies problems
- Activity 8 fixes technical issues
- Activity 9 addresses broader systemic concerns

### Interactive Elements
- Radio buttons for multiple-choice questions
- Checkboxes for feature toggles
- Buttons for actions (scan, analyze, generate)
- Markdown for dynamic feedback and reports
- Tabs for activity subsections

## Technical Requirements

### Dependencies
- Gradio >= 4.0.0 (tested with 5.49.1)
- Python >= 3.10
- Standard library only (no external data files)

### Gradio Components Used
- `gr.Blocks`: Main app container
- `gr.Tab`: Section organization
- `gr.Markdown`: Content and feedback display
- `gr.Radio`: Multiple choice questions
- `gr.Checkbox`: Feature toggles
- `gr.Button`: Action triggers
- Theme: `gr.themes.Soft` with customizable primary hue

## Testing

### Unit Tests
Created comprehensive test suite (`/tmp/test_new_apps.py`) validating:
- App instantiation
- Data generation functions
- Metric calculations
- User stat retrieval

### Manual Testing
All apps successfully:
- Import without errors
- Create Gradio Blocks instances
- Display UI components correctly
- Track Moral Compass scores
- Generate reports and summaries

### Security
Passed CodeQL analysis with 0 alerts:
- No hardcoded credentials
- No SQL injection vulnerabilities
- No XSS vulnerabilities
- Safe data handling

## Educational Content

### Learning Objectives Covered

**Activity 7 - Bias Detective:**
- Understanding the OEIAC framework for AI ethics
- Identifying demographic variables in datasets
- Measuring disparate impact across groups
- Recognizing real-world consequences of bias

**Activity 8 - Fairness Fixer:**
- Removing direct demographic features
- Identifying and eliminating proxy variables
- Building representative datasets
- Creating continuous improvement plans
- Understanding accuracy-fairness trade-offs

**Activity 9 - Justice & Equity Upgrade:**
- Implementing accessibility features
- Promoting diversity and inclusion
- Engaging stakeholders effectively
- Elevating from technical fixes to systemic justice

### Alignment with Problem Statement

The implementation fully addresses all requirements from the problem statement:

✅ **Activity 7 structure matches specification:**
- 7.1: Introduction with Moral Compass indicator
- 7.2: Expert Framework Overview (OEIAC)
- 7.3: Demographics Scanner
- 7.4: Bias Radar Visualization
- 7.5: Diagnosis Report

✅ **Activity 8 structure matches specification:**
- 8.1: Introduction as Fairness Engineer
- 8.2: Remove Direct Demographics
- 8.3: Identify & Remove Proxies
- 8.4: Representative Data Strategy
- 8.5: Continuous Improvement Plan
- 8.6: Fairness Fix Summary

✅ **Activity 9 structure matches specification:**
- 9.1: Introduction as Justice Architect
- 9.2: Access & Inclusion Makeover
- 9.3: Stakeholder Mapping
- 9.4: Final Moral Compass Score Reveal
- 9.5: Completion and Certificate

## Future Enhancements

Possible improvements for future iterations:

1. **Real Data Integration**: Connect to actual COMPAS dataset API
2. **Persistent Storage**: Save progress across sessions
3. **Team Leaderboard**: Live team competition display
4. **Advanced Metrics**: Additional fairness metrics (equalized odds, demographic parity)
5. **Multilingual Support**: Full Catalan, Spanish, English translations
6. **Model Card Generation**: Automated model documentation
7. **API Integration**: Full Moral Compass API score submission

## Files Modified

- `aimodelshare/moral_compass/apps/bias_detective.py` (new, 461 lines)
- `aimodelshare/moral_compass/apps/fairness_fixer.py` (new, 666 lines)
- `aimodelshare/moral_compass/apps/justice_equity_upgrade.py` (new, 528 lines)
- `aimodelshare/moral_compass/apps/__init__.py` (updated, +6 exports)
- `notebooks/Etica_en_Joc_Justice_Challenge.ipynb` (updated, +7 cells)

Total new code: ~1,655 lines of Python + documentation

## Conclusion

The implementation successfully delivers three comprehensive, educational Gradio applications that teach AI ethics, fairness, and bias mitigation through hands-on interactive exercises. The apps follow established patterns, integrate with the Moral Compass system, and provide a complete learning pathway from bias detection to systemic justice improvements.
