Explainable artificial intelligence can help bridge the gap between human understanding and the way artificial intelligence models function
The very first industrial revolution historically kicked off with the introduction of steam- and water-powered technology. We have come a long way since then, with the current fourth industrial revolution, or Industry 4.0, being focused on utilising new technology to boost industrial efficiency. Some of these technologies include the internet of things (IoT), cloud computing, cyber-physical systems and, clearly, AI. AI is the key driver in 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 “explainable” to experts. In this regard, explainable AI, or XAI, focuses on developing algorithms that produce human-understandable results made by AI-based systems.
XAI
Recently, a group of researchers surveyed the existing AI- and XAI-based methods used in building efficient smart factories, healthcare, cities and human-computer interactions.
XAI-based methods are classified according to specific AI tasks, like the feature explanations, decision making or visualisation of the model. The researchers 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 that explains a customer’s decision to purchase or not 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, there are obstacles, too. Most significant is the power-hungry nature of AI-based systems, the exponentially-increasing requirement for a large number of processor cores and GPUs, and 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.
Hence, 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.
