Applied AI Systems and Ethics

This unit explores advanced AI methods (decision trees, deep learning, generative models, and computer vision) and their application in real-world domains such as drones, autonomous vehicles, and sensory augmentation. It also addresses safety, privacy, accessibility, and societal implications to prepare learners to design responsible, deployable AI solutions.

Learning Objectives

• Analyze and compare traditional machine learning and deep learning/generative approaches, selecting appropriate models and architectures for specified application scenarios (e.g., vision, self-driving, drone operations).
• Apply deep learning, generative AI, and edge-processing techniques to develop a prototype solution that includes multimedia or image-based components and report basic performance metrics and trade-offs.
• Demonstrate the design and implementation of safety, privacy, and accessibility measures by creating correction mechanisms, data protection strategies, and accommodations for special needs in an AI system prototype.
• Evaluate societal, ethical, and reliability implications of AI systems—including bias, data mistruths, hype, and emergent behaviors—and propose concrete mitigation and human-centered design strategies.