Automated Testing of AI Models

10/07/2021
by   Swagatam Haldar, et al.
0

The last decade has seen tremendous progress in AI technology and applications. With such widespread adoption, ensuring the reliability of the AI models is crucial. In past, we took the first step of creating a testing framework called AITEST for metamorphic properties such as fairness, robustness properties for tabular, time-series, and text classification models. In this paper, we extend the capability of the AITEST tool to include the testing techniques for Image and Speech-to-text models along with interpretability testing for tabular models. These novel extensions make AITEST a comprehensive framework for testing AI models.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/11/2021

Testing Framework for Black-box AI Models

With widespread adoption of AI models for important decision making, ens...
research
06/16/2020

Quality Management of Machine Learning Systems

In the past decade, Artificial Intelligence (AI) has become a part of ou...
research
09/23/2020

Text Classification with Novelty Detection

This paper studies the problem of detecting novel or unexpected instance...
research
01/14/2022

Tools and Practices for Responsible AI Engineering

Responsible Artificial Intelligence (AI) - the practice of developing, e...
research
06/24/2020

A Methodology for Creating AI FactSheets

As AI models and services are used in a growing number of highstakes are...
research
02/08/2023

Computational Models of Solving Raven's Progressive Matrices: A Comprehensive Introduction

As being widely used to measure human intelligence, Raven's Progressive ...
research
08/13/2021

Robustness testing of AI systems: A case study for traffic sign recognition

In the last years, AI systems, in particular neural networks, have seen ...

Please sign up or login with your details

Forgot password? Click here to reset