# Model Science: getting serious about verification, explanation and control of AI systems

## TL;DR

This paper advocates for a new discipline called Model Science that centers on analyzing, verifying, explaining, and controlling AI models to ensure their safety, alignment, and reliability across various contexts.

## Contribution

It introduces a conceptual framework for Model Science with four key pillars: Verification, Explanation, Control, and Interface, shifting focus from data to models themselves.

## Key findings

- Proposes strict, context-aware evaluation protocols for models.
- Defines explanation methods exploring internal model operations.
- Suggests interactive tools for human-AI calibration.

## Abstract

The growing adoption of foundation models calls for a paradigm shift from Data Science to Model Science. Unlike data-centric approaches, Model Science places the trained model at the core of analysis, aiming to interact, verify, explain, and control its behavior across diverse operational contexts. This paper introduces a conceptual framework for a new discipline called Model Science, along with the proposal for its four key pillars: Verification, which requires strict, context-aware evaluation protocols; Explanation, which is understood as various approaches to explore of internal model operations; Control, which integrates alignment techniques to steer model behavior; and Interface, which develops interactive and visual explanation tools to improve human calibration and decision-making. The proposed framework aims to guide the development of credible, safe, and human-aligned AI systems.