AI for Worker Collective Action

Overview

Discover how AI can empower gig workers through collective action. Learn about a framework using LLMs and worker-owned data to create technologies that improve working conditions on platforms like Upwork and Amazon Mechanical Turk.

Syllabus

• Introduction to AI and Worker Collective Action
Overview of AI and its applications
The gig economy: platforms and their impact
Collective action: concepts and examples
• Understanding Large Language Models (LLMs)
What are LLMs and how do they work?
Use cases of LLMs in various industries
Limitations and ethical considerations
• Worker-Owned Data
The importance of data ownership
Data sovereignty and privacy
Case studies: successful worker data initiatives
• AI Framework for Empowering Gig Workers
Designing AI tools for collective bargaining
Role of LLMs in improving worker conditions
Building transparent and accountable AI systems
• Improving Working Conditions on Gig Platforms
Analysis of platforms like Upwork and Mechanical Turk
Identifying pain points and opportunities for AI intervention
Developing worker-centered technology solutions
• Case Studies and Real-World Applications
Successful implementations of AI for worker action
Lessons learned from previous initiatives
Potential pitfalls and how to avoid them
• Workshops and Practical Sessions
Hands-on exercises with AI tools for collective action
Group projects on creating AI-driven solutions for gig workers
Feedback and critique sessions
• Ethical and Social Implications
Addressing biases in AI models
Ensuring equity in technology deployment
Long-term impacts on the gig economy and workers
• Final Project: Designing an AI Solution for Gig Workers
Proposal development and presentation
Peer review and collaborative feedback
Refinement and future implementation plans
• Course Wrap-Up and Future Directions
Recap of key learnings
Emerging trends in AI and labor
Opportunities for further research and action

Subjects

Computer Science