Skip to content
#

interpretable-models

Here are 13 public repositories matching this topic...

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.

  • Updated Dec 13, 2025
  • Python

An analytical essay on why prediction-based models fail in reflexive, unstable systems. This article argues that accuracy collapses when models influence behavior, and proposes equilibrium and force-based modeling as a more robust framework for understanding pressure, instability, and transitions in AI-shaped systems.

  • Updated Dec 13, 2025

A systems-thinking essay that reframes failure as a gradual transition rather than a discrete outcome. It explains how pressure accumulation, weakening buffers, and hidden instability precede visible collapse, and why prediction-based models arrive too late to prevent failure in human-centered systems.

  • Updated Dec 14, 2025

An interpretable early-warning engine that detects academic instability before grades collapse. Instead of predicting performance, it models pressure accumulation, buffer strength, and transition risk using attendance, engagement, and study load to explain fragility and identify high-leverage interventions.

  • Updated Dec 14, 2025
  • Python

Visual Intelligence is a desktop app that extracts text from images and PDFs in Turkish and English using Tesseract OCR. It offers advanced features like text summarization, keyword extraction, table and QR/barcode detection. The app has a modern, user-friendly interface built with TailwindCSS and Vanilla JS.

  • Updated Dec 15, 2025
  • HTML

Improve this page

Add a description, image, and links to the interpretable-models topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the interpretable-models topic, visit your repo's landing page and select "manage topics."

Learn more