Here’s What We’ll Cover:
Ethics & Bias in Machine Learning
- Understanding biases in data and algorithms.
- Fairness, transparency, and accountability in ML models.
- Ethical considerations: privacy, security, societal impacts.
Curriculum
- 7 Sections
- 50 Lessons
- 10 Weeks
Expand all sectionsCollapse all sections
- Accountability2
- Privacy, Cybersecurity and Social Responsibility: Ethical Considerations6
- Bias and Ethical Concerns in Machine Learning17
- 3.1Introduction
- 3.2What Is Bias in AI?
- 3.3How Does Bias Creep Into AI Systems?
- 3.4Biased Real-World Data
- 3.5Lack of Detailed Guidance or Frameworks for Bias Identification
- 3.6Biased Third-Party AI Systems
- 3.7Nondiverse Teams
- 3.8Nonidentification of Sensitive Data Attributes and Related Correlations
- 3.9Unclear Policies
- 3.10Mitigation
- 3.11Promote a Culture of Ethics
- 3.12Promote Diversity
- 3.13Prepare a Balanced Data Set
- 3.14Account for Bias in AI Modeling
- 3.15Make Periodic Assessments
- 3.16Enable Explainable AI
- 3.17Conclusion
- Understanding biases in data and algorithms.9
- Fairness3
- Transparency3
- Ethics in Data Science and Proper Privacy and Usage of Data10
