Session 1: Introduction to AI Ethics and Responsible AI

• What is AI ethics? Core principles: fairness, accountability, transparency, and privacy
• The importance of ethical AI in data science projects
• Real-world case studies: biased AI systems in hiring, law enforcement, and healthcare

Session 2: Understanding Bias in Data Science and AI

• Types of bias: data bias, algorithmic bias, societal bias
• How bias occurs: biased data collection, model training, and feedback loops
• Case study analysis: algorithmic bias in facial recognition technology

Session 3: Techniques for Bias Detection and Mitigation

• Methods for detecting bias in datasets and machine learning models
• Fairness metrics: disparate impact, statistical parity, equal opportunity
• Bias mitigation strategies: data rebalancing, adversarial de-biasing, algorithmic adjustments

Session 4: Hands-on Activity: Detecting and Mitigating Bias in AI Models

• Analyzing a dataset to identify potential biases
• Using Python libraries (e.g., AI Fairness 360) for bias detection
• Implementing bias mitigation techniques and evaluating fairness metrics

Session 5: Legal, Regulatory, and Compliance Considerations

• Global AI regulations: GDPR, AI Act, and ethical AI guidelines
• The role of explainable AI (XAI) in compliance and transparency
• Ethical risk assessments and AI audit frameworks

Session 6: Group Discussion: Ethical Dilemmas in AI Development

• Exploring real-world ethical challenges faced by AI practitioners
• Group activity: designing an ethical AI strategy for a hypothetical project
• Best practices for fostering ethical awareness within AI and data science teams

Session 7: Course Wrap-Up and Key Takeaways

• Recap of key concepts: ethics, bias detection, and responsible AI development
• Practical steps for implementing ethical AI practices in organizations
• Resources for continuous learning in AI ethics and governance
• Q&A session to address participants’ specific questions