AmirhosseinHonardoust/Workforce-Disruption-Equilibrium-Engine
An interpretable system that models the future of work as an equilibrium under AI-driven forces. Instead of predicting job loss, it decomposes workforce disruption into automation pressure, adaptability, skill transferability, demand, and AI augmentation to explain stability, tension, and transition paths by 2030.
This tool helps HR leaders, workforce planners, and strategists understand how AI will reshape jobs by 2030, explaining why some roles remain stable while others face significant upheaval. It takes a specific job role as input and provides an interpretable breakdown of the forces (like automation pressure, adaptability, and economic demand) shaping its future stability, shift, and transition tension, rather than just predicting job loss. This allows for proactive planning and policy development.
Use this if you need a deep, interpretable understanding of how various AI-driven forces will specifically impact a job role's future stability and transition, rather than a simple 'at-risk' score.
Not ideal if you're looking for a definitive prediction of job loss percentages or a simple list of 'safe' versus 'unsafe' occupations.
Stars
17
Forks
—
Language
Python
License
MIT
Category
Last pushed
Dec 13, 2025
Commits (30d)
0
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