Clearbox AI Model Assessment

Our Model Assessment is designed to facilitate the machine learning development pipeline by providing an automated way to analyze models by highlighting strong points and limitations, detecting undesired behavior and assessing generalizability.

Clearbox AI Model Assessment

Based on our proprietary technology

The core of our technology is a variational autoencoder which is used to generate the synthetic data used within the assessment process and to select the most relevant information that needs to be evaluated by model owners.

Try our Demo

Explore our AI Model Assessment with different datasets and models.

Technical Whitepaper

Download our Technical Whitepaper to find out more about our technology.

Data drift and outliers detection

Data scientists can use our Model Assessment to quantify data drift and to explain outliers present in the model training and assessment datasets. This can highlight how ready is a model to operate in a production environment.

Model limitations and robustness check

Our tool looks for model limitation by dividing the assessment set into subgroups based on the similarities learnt by the generative model. The model performance for each subgroup can change considerably and by using this kind of clustering it is possible to have a better overview about potential model limitations and the presence of irreducible uncertainty. The overall model robustness is also tested by feeding new input perturbations produced by the generative model.

Interpretability assessment

Being able to interpret the output of a model is a crucial part of an assessment procedure. Receiving additional information about the model’s decision process can help the model owner not only understand mistakes but also to detect undesired behaviour and explain model bias. Our tool automatically selects the most representative points in the assessment set whose explanations need to be evaluated by a human.