With the advent of industry 4.0, advancements in artificial intelligence (AI) have become vital to helping with industry efficiency and performance.
Explainable artificial intelligence (XAI) is helping companies and developers analyse algorithms transparently, assessing exactly how they work out solutions and improve them.
The need for XAI-based methods to help build efficient smart cities, factories, healthcare, and human-computer interactions has now been highlighted by a group of researchers who surveyed the existing AI and explainable AI (XAI) based methods used in Industry 4.0.
Necessary to develop XAI to improve efficiency of models
XAI generally takes one of two approaches: black-box analysis or interpretable models. Black-box analysis is the traditional method since it simply opens up an algorithm’s preexisting box and examines the data inside.
These models are intended to be analysed, like a computer in a glass case. They can be highly complex to create, but demand for user-friendly XAI is encouraging developers to continue researching and innovating interpretable technology.
Recently, a group of researchers, including Assistant Professor Gwanggil Jeon from Incheon National University, South Korea, surveyed existing AI and XAI technologies and their applications in Industry 4.0. Their review, published in IEEE Transactions on Industrial Informatics, was published in the journal last month.
“Though AI technologies like DL can solve many social problems due to their excellent performance and resolution, it is difficult to explain how and why such good performance is obtained,” said Professor Jeon. “Therefore, there is a necessity to develop XAI, so that DL, like the current black box, can be modelled more efficiently. It will also be easier to make applications.”
AI is the key driver of Industry 4.0, automating intelligent machines to self-monitor, interpret, diagnose, and analyse all by themselves. AI methods, such as machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision (CV), help industries forecast their maintenance needs and cut down on downtime.
However, to ensure the smooth, stable deployment and integration of AI-based systems, the actions and results of these systems must be made comprehensible, or, in other words, “explainable” to experts. In this regard, explainable AI (XAI) focuses on developing algorithms that produce human-understandable results made by AI-based systems.
XAI-based methods are classified according to specific AI tasks, like the feature explanations, decision-making, or visualisation of the model. The authors note that the combination of cutting-edge AI and XAI-based methods with Industry 4.0 technologies results in various successful, accurate, and high-quality applications. One such application is an XAI model made using visualisation and ML which explains a customer’s decision to purchase or not purchase non-life insurance. With the help of XAI, humans can recognise, comprehend, interpret, and communicate how an AI model draws conclusions and takes action.
There are clearly many notable advantages of using AI in Industry 4.0; however, it also has many obstacles, the study adds. Most significant is the power-hungry nature of AI-based systems, the exponentially increasing requirement for a large number of cores and GPUs, as well as the need for fine-tuning and hyperparameter optimisation. At the heart of this is data collected and generated from millions of sources, devices, and users, thereby introducing bias that affects AI performance. This can be managed using XAI methods to explain the bias introduced.
“AI is the principal component of industrial transformation that empowers smart machines to execute tasks autonomously, while XAI develops a set of mechanisms that can produce human-understandable explanations,” concludes Professor Jeon.