Q: What is your organization’s role in XMANAI?

A: The role of Ford Motor Company in the XMANAI project is contributing to defining business requirements and testing algorithms. Defining these requirements involves gathering information from stakeholders, analyzing business processes, and identifying potential problems and opportunities. On the other hand, testing algorithms are an important aspect to ensure the results of XMANAI as we need to validate the results obtained in comparison with our experience.

Q: How does XMANAI relate to your or your team’s background and interests?

A: I am convinced that the use of artificial intelligence is one of the new areas that can enable significant improvements and breakthroughs in our production processes. One of the reasons why artificial intelligence is probably not used for decision-making may be the lack of confidence in its decision as people have more confidence in their own decisions, therefore, the added value of explainability can change this paradigm.

Q: What is the novelty of XMANAI and the main benefits envisaged for your organization?

A: The novelty of XMANAI lies in its ability to provide human-understandable explanations for AI-based decision-making. This is important because it can help build trust in AI systems, improve reliability, and enable humans to understand and act on their outputs. The benefits of XAI for organizations are numerous. By providing transparent explanations for AI-based decisions, XAI can:

• Improve trust and accountability: XAI can help build trust in AI systems by providing understandable explanations for their decisions.
• Enable better decision-making: XAI can help humans understand and act on the outputs of AI systems, which can improve their ability to make informed decisions.
• Facilitate knowledge transfer: XAI can help transfer knowledge from AI systems to humans by explaining the reasoning behind their decisions.

Q: Which target groups can benefit from XMANAI?

A: The use of cases at Ford is focused on helping the engineer to make the best decisions in terms of production optimization. Through this innovative application of explainable artificial intelligence, he will be able to anticipate problems in upcoming shifts.  In addition, the explainability component is critical as we need to have full confidence in the models and results obtained.