Ethical Data Science
Responsible data practices.
Privacy
Minimize data collection. Anonymize, pseudonymize. GDPR, CCPA compliance. Consent.
Fairness
Bias in data: historical inequities. Bias in algorithms: systematic errors.
Audit for disparate impact. Mitigate with preprocessing, in-processing, post-processing.
Transparency
Explain model decisions. Document data and methods. Auditable processes.
Key Takeaways
- Minimize data, maximize privacy
- Audit for fairness
- Be transparent about methods