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AII 30000 Collaborative Human–AI Systems

3 credits

  • Prerequisites: None
  • Delivery: On-Campus, Online
  • This course introduces human-AI interaction design for systems that solve problems neither humans nor artificial intelligence could solve alone.

    Topics include interpretability, transparency, trust, and AI ethics. Student projects focus on developing applications where AI provides cognitive and perceptual augmentation to humans.

    Learning Outcomes

    1. Assess the benefits and limitations of AI in relation to human intelligence.
    2. Apply human-centered methods to characterize AI requirements in different domains and application areas.
    3. Design and develop symbiotic human–AI systems that balance the information processing power of computational systems with human intelligence and decision making.
    4. Evaluate the advantages, disadvantages, challenges, and ramifications of human–AI augmentation.
    5. Analyze the impact of a human–AI system on the individual, organization, and society from the standpoint of fairness and bias, ethical principles, and legal implications.
    6. Identify issues of fairness and bias in scenarios involving human-AI decision making to inform the functional and interface design of ethical collaborative systems.
    7. Examine how to quantify bias in human–AI systems and to use algorithms to mitigate it, in light of their ethical implications.
    8. Write a data policy for specific data and information flows in human–AI systems, considering various technologies such as facial recognition, natural language processing, and predictive analytics.