DeepAI AI Chat
Log In Sign Up

Ensuring Dataset Quality for Machine Learning Certification

by   Sylvaine Picard, et al.

In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verification process, and apply it on a signal recognition system from the railway domain. In addi-tion, we also give a list of recommendations for the collection and management of datasets. This work is one step towards the dataset engineering process that will be required for ML to be used on safety critical systems.


Empowering the trustworthiness of ML-based critical systems through engineering activities

This paper reviews the entire engineering process of trustworthy Machine...

Toward Certification of Machine-Learning Systems for Low Criticality Airborne Applications

The exceptional progress in the field of machine learning (ML) in recent...

Towards Probability-based Safety Verification of Systems with Components from Machine Learning

Machine learning (ML) has recently created many new success stories. Hen...

SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

Ensuring safety and explainability of machine learning (ML) is a topic o...

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Machine learning has evolved into an enabling technology for a wide rang...

Satyam: Democratizing Groundtruth for Machine Vision

The democratization of machine learning (ML) has led to ML-based machine...