Transparency Measures

• 1: User Data Access
• 2: Decision-Making Explanations
• 3: Ethical AI Usage
• 3.1: AI Model Auditing
• 3.2: Algorithmic Decision Validation

1 - User Data Access

• Portability: Provide CSV or JSON exports of personal data.
• Visualization: Offer dashboards showing day-to-day or week-to-week progression.
• Access Logs: Reveal details of when and by whom data was viewed.

2 - Decision-Making Explanations

• Algorithms: Summarize how heuristic or ML models interpret user signals.
• Insights: Provide straightforward, user-friendly interpretations (e.g., “Your attention improved by 10% after rest”).
• User Education: Create FAQs or tutorials explaining complex analytics.

3 - Ethical AI Usage

3.1 - AI Model Auditing

• Fairness Checks: Look for demographic or socioeconomic biases.
• Public Reporting: Publish summaries of auditing outcomes and steps taken to fix imbalances.
• Third-Party Audits: Invite external specialists for impartial review.

3.2 - Algorithmic Decision Validation

• Human Oversight: Permit educators to override automated recommendations.
• Feedback Mechanisms: Allow students or parents to contest data-based evaluations.
• Continuous Monitoring: Update AI models as new data or patterns emerge.