By the end of the course, participants will be able to:

• Understand core ethical principles in data science (transparency, accountability, fairness)
• Detect and mitigate algorithmic bias using fairness-aware metrics
• Critically analyze the energy and social cost of AI and cloud-based data science systems
• Apply multi-objective evaluation techniques (accuracy, fairness, sustainability)
• Design resource dispatching strategies that respect jurisdictional and environmental constraints
• Incorporate feminist and decolonial perspectives into data practices
• Navigate international ethical and legal frameworks (e.g., GDPR, IDS, EU AI Act)
• Build socially responsible, inclusive, and equitable AI pipelines