This unit explores the mechanisms for ensuring accountability in ML development and deployment, including ethical guidelines, regulatory frameworks, and certification processes. Students will gain an understanding of the importance of transparency and accountability in ML systems and the various approaches to achieving it. Üben mit sofortiger Rückmeldung, kostenlos und ohne Anmeldung.

Weitere Themen

• What is Machine Learning Ethics?
• Ethical Challenges in Machine Learning
• Stakeholders and Responsibilities
• Sources of Bias in ML
• Mitigating Bias in ML
• Evaluating Fairness in ML
• Data Privacy Concerns in ML
• Privacy-Preserving Techniques in ML