In the rapidly evolving landscape of hardware engineering, the integration of artificial intelligence (AI) has emerged as a game-changing paradigm. AI-driven systems empower engineers to tackle complex challenges, optimize performance, and enhance efficiency across a myriad of hardware applications. However, as AI becomes increasingly pervasive, the need for transparency and understanding of AI-driven decision-making processes has never been more pronounced. This is where Explainable AI (XAI) comes into play, serving as a transformative force poised to revolutionize decision-making within the realm of hardware engineering.
Explainable AI, often referred to as the 'XAI,' represents a critical bridge between the complexity of AI algorithms and the practical needs of engineers. It offers the capability to demystify the decision-making black boxes inherent in AI systems, providing engineers with invaluable insights into why and how specific choices are made. In the context of hardware engineering, where precision, reliability, and safety are paramount, XAI emerges as a cornerstone technology, enabling engineers to confidently leverage AI-driven solutions for critical tasks.
This introduction sets the stage for our exploration of the profound impact of Explainable AI on hardware engineering decision-making. We will delve into the ways in which XAI empowers engineers to harness the full potential of AI while ensuring transparency, interpretability, and trust in the decision-making processes that underpin the next generation of hardware innovations.
In tandem with the advancement of artificial intelligence (AI), there is a growing sense of humans being increasingly sidelined in the decision-making process. This has given rise to the emergence of Explainable AI, often abbreviated as XAI.
In essence, Explainable AI embodies a guiding principle, a methodology by which an artificial intelligence system arrives at decisions. Simultaneously, XAI consolidates the outcomes achieved and elucidates the path taken to reach those conclusions. This approach stands in stark contrast to the black-box principle, where even the most brilliant scientists find it impossible to discern any meaning.
To put it differently, eXplainable AI encompasses techniques and procedures that are comprehensible to humans, including the machine learning algorithms employed to yield results. This facet of AI enhances transparency, enabling the tracking of outcomes and the detection of potential biases.
When an organization seeks precise results and aims to establish trust through AI, it turns to such an approach. Additionally, it serves as a safeguard against potential glitches that could impact decision-making or alter the final outcome.
Statistical AI and Explainable AI (XAI) differ significantly in how they operate and the transparency they provide. Let's explore five key differences between the two approaches:
• Statistical AI: Statistical AI models, like deep neural networks, often lack interpretability. They make decisions based on complex mathematical functions, making it challenging to understand why a particular decision was reached.
• Explainable AI: XAI is designed with interpretability in mind. It provides clear explanations of how decisions are made, making it easier for engineers to understand and trust the AI.
Use Case: In hardware product design, suppose you're using a statistical AI model to optimize the thermal performance of a processor. The model suggests a design change, but it's unclear why this change is recommended. Engineers may hesitate to implement it due to the lack of understanding.
• Statistical AI: Statistical AI models are often considered "black boxes" because they offer little insight into their decision-making process.
• Explainable AI: XAI prioritizes transparency, revealing the inner workings of the AI system, allowing engineers to see the factors considered during decision-making.
Use Case: When designing a printed circuit board (PCB), a statistical AI model suggests a layout change to improve signal integrity. Without explanations, engineers may be reluctant to adopt the recommendation, unsure of potential side effects.
• Statistical AI: Identifying and mitigating bias in statistical AI models can be challenging due to their opacity.
• Explainable AI: XAI aids in detecting and addressing bias by providing insights into how and why certain decisions may be biased.
Use Case: In a hardware design project, an XAI tool identifies that a recommendation for a specific component supplier consistently favors one demographic group. This insight allows engineers to rectify the bias.
• Statistical AI: Minimal human interaction is possible because understanding and modifying model decisions can be impractical.
• Explainable AI: XAI fosters collaboration between AI systems and engineers, as the transparency allows engineers to provide feedback and fine-tune models.
Use Case: In the development of a new semiconductor chip, engineers can work closely with an XAI system, iteratively refining design choices based on the AI's explanations and engineers' domain expertise.
• Statistical AI: Risk management is challenging when the decision process is unclear.
• Explainable AI: XAI enables better risk assessment and management by allowing engineers to identify potential pitfalls and uncertainties in the AI's recommendations.
Use Case: When designing a safety-critical hardware component, XAI helps engineers evaluate the risks associated with different design choices and make informed decisions to minimize potential hazards.
In summary, while statistical AI can be powerful for hardware design, Explainable AI offers essential advantages in terms of transparency, interpretability, bias detection, collaboration, and risk management, making it a valuable tool for engineers working on complex hardware products.
The importance of XAI translates into many advantages, which we'll look at in more detail in the next paragraph. In fact, the adoption of explainable AI accesses 3 particular advantages:
For a company, adopting an automated process increases productivity. Full confidence in AI means more production. The rapid introduction of AI models into production is the result of this climate of trust between human and machine.
What's more, when an organization can rely on AI, it can work with complete transparency and peace of mind. With results traceable, there's nothing to fear in the production process.
AI can speed up the production process. Nevertheless, models must be systematically controlled and monitored to ensure there are no errors or biases. A high-performance model delivers satisfactory results. This implies the need for continuous evaluation of AI models.
In an enterprise, it's important to opt for explainable AI models that are transparent. This not only reduces management costs, but also minimizes, if not eliminates, any risk associated with model management. In this case, manual inspection, like errors, will no longer incur additional costs. The same applies to unintentional biases caused by human error.
You can have different strategies in developing an explainable AI application for your business.
Your organisation has the necessary skills and resources to manage a development team and have them build from scratch the application that covers your need.
This is the long, and expensive route. Industrials might prefer it for a variety of reasons, one of them being the ownership that they will have on the product.
Of course, chosing the right language for your development is paramount, and we covered this topic in our article on ‘why can python help you improve engineering processes’.
We don’t hear about that one a lot, yet it is the preferred direction of a lot of companies in the recent years.
Indeed, if you wish to keep ownership and flexibility over your development, while minimizing the complexity and entry cost of such development, the use of AI app development toolboxes is built for you.
Instead of starting from scratch, you can leverage a python framework for engineering such as Dessia Technologies augmented engineering platform.
Leveraging low-code design automation libraries, your team will be able to build in a few days a program that would have taken month to erect with a larger team.
The quickest way, but also probably the most expensive and least flexible.
First, buying means being able to specify. And as a company that undertakes its first Artificial Intelligence developments for engineering efficiency, you might not be able to do so.
As a consequence you will need to go through an expensive consulting exercise, to help you refine your requirements, before engaging through a fixed price engagement to deliver en engineering automation application relying on explainable artificial intelligence.
At Dessia Technologies, we packaged a ‘buy’ engagement model, that not only delivers the above, but also ensures that at the delivery of it, your users get trained and are able to take over the development of future applications.
