Abstract: A system for bias estimation in Artificial Intelligence (AI) models using a pre-trained unsupervised deep neural network, comprising a bias vector generator implemented by at least one processor that executes an unsupervised DNN with a predetermined loss function. The bias vector generator is adapted to store a given ML model to be examined, with predetermined features; store a test-set of one or more test data samples being input data samples; receive a feature vector consisting of one or more input samples; output a bias vector indicating the degree of bias for each feature, according to said one or more input samples. The system also comprises a post-processor which is adapted to receive a set of bias vectors generated by said bias vector generator; process said bias vectors; calculate a bias estimation for every feature of said ML model, based on predictions of said ML model; provide a final bias estimation for each examined feature.

Abstract: A system for the assessment of robustness and fairness of AI-based ML models, comprising a data/model profiler for creating an evaluation profile in the form of data and model profiles, based on the dataset and the properties of the ML model; a test recommendation engine that receives data and model profiles from the data/model profiler and recommends the relevant tests to be performed; a test repository that contains all the tests that can be examined; a test execution environment for gathering data related to all the tests that were recommended by the test recommendation engine; a final fairness score aggregation module for aggregating the executed tests results into a final fairness score of the examined model and dataset.