Navigating Human-AI Interaction Modes

This unit examines the operational modes of AI systems (AI-First, Fusion, Human-First, Marginal), techniques for transitioning among them, and the ethical, human-centered practices that preserve agency and accountability. Students will learn adaptive algorithmic recalibration methods, humanistic reinforcement approaches, and data governance principles treating data as a shared resource to prepare for applying these practices in design and development projects.

Learning Objectives

• Analyze the characteristics, trade-offs, and appropriate use cases of AI-First, Fusion, Human-First, and Marginal modes, and classify example systems into these modes
• Apply structured transition strategies to design step-by-step plans for shifting a system between interaction modes that account for user agency, usability, and stakeholder impact
• Demonstrate humanistic reinforcement and algorithmic recalibration techniques by drafting or prototyping interventions that maintain human control while adapting system behavior to feedback
• Evaluate data governance and ethical implications of treating data as a common currency, and propose policies for collection, consent, and recalibration that align with human-centered values