Uses labor statistics and task-level data to score occupations by AI exposure. Visualizes risk levels, job changes, and emerging AI-driven opportunities.
Problem
Workers, employers, and policymakers are uncertain about how AI will reshape the labor market. Many occupations face varying levels of automation risk, but these risks are difficult to quantify without analyzing task-level data and job content. Without clarity, career planning and workforce development efforts rely on speculation, leaving individuals and organizations unprepared for the coming changes.
Overview
This project builds a comprehensive dashboard assessing how exposed different occupations are to AI-driven automation. It combines job descriptions, task-level datasets, and labor statistics to produce interpretable risk scores and visualizations, helping users understand where automation pressures are most likely to emerge. The dashboard provides both macro-level trends and granular insights into specific roles and industries.
How It Works (Approach)
Using O*NET and BLS data, the system evaluates the tasks within each occupation and assigns AI exposure scores using embeddings, skill similarity analysis, and existing research frameworks. The methodology considers both the technical feasibility of automating tasks and the economic incentives for doing so. Results are mapped across geographies and industries to reveal occupational and regional patterns, with time-series analysis showing how risks evolve over time.
Impact / Value
Workers gain insight into career risks and opportunities, enabling proactive skill development and career transitions. Employers better anticipate changing skill needs and can invest in workforce training programs. Policymakers can design targeted workforce programs and support systems. The project turns abstract concerns about automation into concrete, data-driven insights that inform real-world decisions.
Key Features
- Occupation-level AI exposure scoring
- Task-level analysis of automation susceptibility
- Interactive geospatial mapping
- Time-series tracking of labor market changes
- Industry and region-based filtering options