Duration: 6 weeks

Description: Navigate the ethical landscape of generative AI, highlighting the importance of recognizing and mitigating biases that can emerge in AI-generated content.

• Week 1: Introduction to Bias in AI Systems
• Week 2: Detecting Bias in Data Sets
• Week 3: Ethical Implications of Generative AI Outputs
• Week 4: Strategies for Mitigating AI Bias
• Week 5: Fairness, Accountability, and Transparency in AI
• Week 6: Bias in Industry-specific Applications

Learning Objectives:

• Define the fundamental sources and varieties of biases present in artificial intelligence (Knowledge).
• Analyze concrete instances where AI biases have led to significant ramifications in real-world scenarios (Analysis).
• Formulate methodologies to identify and mitigate biases inherent in datasets used for training AI (Synthesis).
• Explain the importance and benefits of ensuring datasets are diverse and inclusive (Comprehension).
• Evaluate the broader societal and moral repercussions of AI outputs that carry inherent biases (Evaluation).
• Investigate and reflect upon detailed case studies illustrating misaligned AI behaviors and their consequences (Analysis).
• Apply best practices and utilize tools effectively to minimize bias in AI modeling (Application).
• Demonstrate proficiency in hands-on exercises designed to correct biased outputs of models (Application).
• Interpret the core principles guiding fairness and transparency within AI and their significance (Analysis).
• Compare and contrast various frameworks aimed at ensuring accountability in AI systems (Analysis).
• Identify challenges associated with AI bias across different industries, such as finance and healthcare (Knowledge).
• Design and strategize approaches to counteract biases in domain-specific AI applications (Synthesis).