Moving from ‘black box’ to ‘glass box’
What is artificial intelligence (AI) and how does it work? For many people, these questions are not easy to answer: this is due to the fact that many machine learning and deep learning algorithms cannot be examined after their execution. The EU-funded XMANAI project will focus on explainable AI, a concept that contradicts the idea of the ‘black box’ in machine learning, where even the designers cannot explain why the AI reaches at a specific decision. XMANAI will carve out a ‘human-centric’, trustful approach that will be tested in real-life manufacturing cases. The aim is to transform the manufacturing value chain with ‘glass box’ models that are explainable to a ‘human in the loop’ and produce value-based explanations.
“Despite the indisputable benefits of AI, humans typically have little visibility and knowledge on how AI systems make any decisions or predictions due to the so-called “black-box effect” in which many of the machine learning/deep learning algorithms are not able to be examined after their execution to understand specifically how and why a decision has been made. The inner workings of machine learning and deep learning are not exactly transparent, and as algorithms become more complicated, fears of undetected bias, mistakes, and miscomprehensions creeping into decision making, naturally grow among manufacturers and practically any stakeholder
In this context, Explainable AI (XAI) is today an emerging field that aims to address how black box decisions of AI systems are made, inspecting and attempting to understand the steps and models involved in decision making to increase human trust.
XMANAI aims at placing the indisputable power of Explainable AI at the service of manufacturing and human progress, carving out a “human-centric”, trustful approach that is respectful of European values and principles, and adopting the mentality that “our AI is only as good as we are”. XMANAI, demonstrated in 4 real-life manufacturing cases, will help the manufacturing value chain to shift towards the amplifying AI era by coupling (hybrid and graph) AI “”glass box”” models that are explainable to a “”human-in-the-loop”” and produce value-based explanations, with complex AI assets (data and models) management-sharing-security technologies to multiply the latent data value in a trusted manner, and targeted manufacturing apps to solve concrete manufacturing problems with high impact.”
Field of science
Natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning
H2020-EU.2.1.1. – INDUSTRIAL LEADERSHIP – Leadership in enabling and industrial technologies – Information and Communication Technologies (ICT)
ICT-38-2020 – Artificial intelligence for manufacturing
Call for proposal
RIA – Research and Innovation action