Ethical Considerations

• 1: Core Philosophy Integration
• 1.1: Individual Growth Focus
• 1.2:
• 2: Handling Missing Data Ethically
• 2.1: Non-Penalization Strategies
• 2.2: Respecting User Choices
• 3: Informed Consent Processes
• 3.1: Consent Documentation
• 3.2: User Communication Strategies
• 4: Transparency Measures
• 4.1: User Data Access
• 4.2: Decision-Making Explanations
• 4.3: Ethical AI Usage
• 4.3.1: AI Model Auditing
• 4.3.2: Algorithmic Decision Validation

1 - Core Philosophy Integration

1.1 - Individual Growth Focus

• Baseline Metrics: Compare a learner primarily against their own historical performance.
• Trajectory Emphasis: Emphasize steady improvement over time.
• Personalized Goals: Align personal targets with each user’s capabilities or pace

1.2 -

2 - Handling Missing Data Ethically

2.1 - Non-Penalization Strategies

• Fair Assessments: Avoid negative outcomes for incomplete sensor data.
• Confidence Annotation: Label results with clarity about data completeness.
• Equality of Opportunity: Preserve access to system benefits regardless of data volume.

2.2 - Respecting User Choices

• Optional Participation: Allow opting out of specific streams (e.g., wearable or microphone data).
• Informed Decisions: Educate users on how more data can yield deeper insights.
• Privacy Respect: Honor requests to disable or remove any data source.

3 - Informed Consent Processes

3.1 - Consent Documentation

• Clarity: Use plain language.
• Updates: Allow re-consent when protocol or data usage changes significantly.
• Record Keeping: Maintain version logs of user consent.

3.2 - User Communication Strategies

• Engagement: Emphasize proven benefits such as targeted interventions.
• Transparency: Clearly define each data stream’s frequency and purpose.
• Feedback Requests: Encourage questions or concerns about data handling.

4 - Transparency Measures

4.1 - 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.

4.2 - 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.

4.3 - Ethical AI Usage

4.3.1 - 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.

4.3.2 - 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.