Life cycle management framework for AI Machine Learning models

Researchers are currently looking for ways to harmonise methods for developing ML models to ensure that they meet key principles such as explainability, trustworthiness, and adherence to ethical standards. One method that is gaining popularity to promote these goals in the development of AI algorithms is the use of machine learning model cards.

A machine learning model card is a comprehensive description of how the machine learning process is being set up. The model card includes information such as the author, the intended use and purpose of the ML model, how data will be collected and used, how ethics, bias and privacy will be handled, how results and performance will be evaluated, and the limitations and risks of the ML model.

The AIthena EC project has introduced a ML model card including built-in bias mitigation techniques and protocols for privacy protection and accountability for developers working on AI in the context of Cooperative, Connected, and Automated Mobility (CCAM). These measures ensure that AI decision-making is transparent, providing a clearer explanation of why a system (such as an automated vehicle) makes one decision over another.

By applying these principles, the AIthena project aims to create AI systems that prioritise explainability and trustworthiness, giving users greater insight into how and why AI systems make their decisions. This is important in the context of CCAM, where trust in automated vehicles (AI-driven) decisions is critical.

Source: More information about AIthena’s ML model card and their full related report are published here