Humans and AI: Understanding LLM Impact on Human Decision Making

Overview

Explore the synergistic potential between humans and LLMs in decision-making tasks, examining active learning, ML fairness, and collaborative approaches to AI implementation.

Syllabus

• Introduction to Large Language Models (LLMs)
Overview of LLM capabilities and limitations
Evolution and development of LLMs
Key LLM technologies and toolkits
• Human-AI Collaboration in Decision Making
Synergistic potential of human-LLM interaction
Case studies of successful human-AI partnerships
Frameworks for integrating LLMs in decision workflows
• Active Learning and LLMs
Introduction to active learning principles
Techniques for leveraging LLMs in active learning
Human-in-the-loop models and feedback mechanisms
• Fairness and Bias in Machine Learning
Understanding ML fairness and ethical considerations
Bias detection and mitigation strategies in LLMs
Regulatory and societal implications of AI fairness
• Implementing Collaborative AI Approaches
Designing AI systems for human collaboration
Challenges and solutions in AI-human teamwork
Tools and platforms for building collaborative AI systems
• Practical Applications and Case Studies
Industry-specific applications and impact of LLMs
Comparative analysis of AI and human decision making
Success stories and lessons learned from real-world implementations
• Future Trends in Human-AI Interaction
Emerging technologies and their implications for decision making
Long-term impacts of LLM integration in society
Predictions and future outlook for collaborative AI systems
• Conclusion and Course Recap
Summary of key concepts and learnings
Discussion on the evolving role of humans and AI
Reflection on ethical and practical aspects of LLM deployment

Subjects

Data Science