AI for Worker Collective Action

Übersicht

Delve into the transformative power of AI in supporting gig workers through collective action. This insightful presentation unveils a framework that leverages Large Language Models (LLMs) and worker-owned data to develop technologies aimed at enhancing the working environments of platforms such as Upwork and Amazon Mechanical Turk.

Discover innovative strategies to empower gig workers and elevate working conditions within the digital labor markets.

• Provider:

  YouTube

• Categories:

  Artificial Intelligence Courses, Computer Science Courses

Lehrplan

• 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

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Fachgebiete

Computer Science