Explainable AI (XAI) to Dominate the Future Market of Industries
With the advent of Machine learning, Artificial Intelligence has achieved previously unimaginable heights. This is because machine learning algorithms help in learning the behavior of an entity using patterns detection and interpretation methods. Although this helps in making informed decisions more efficiently, we still are not able to understand how machines arrived at a particular insight in the first place. What are the processes they adopted, and at what speed? How did they make such autonomous decision? These are a few of the fundamental questions that come across our minds. Even if it might seem unnecessary in the first place, for many critical applications in defense, medicine, finance, and law, explanations are essential for users to understand, trust, and effectively manage these new, artificially intelligent partners.
Enter explainable AI (XAI). This subset of Artificial intelligence is programmed to define its purpose, justification, and decision-making process in a way that can be understood by the average person. This is important since we want computer systems to work as expected and produce transparent explanations and reasons for the decisions they make. By inspecting and trying to understand the steps and models involved in making decisions, XAI essentially addresses how black box decisions of AI systems are made. This can help to create more human-understandable AI systems that answer questions like what it has done, what it is doing now, what will happen next, and how. Besides, it can propel the adoption of best practices around the areas of compliance, accountability, and ethics and reduce the impact of biased algorithms.
Models build on XAI can identify relevant stakeholders and the information they require about how the model arrives at decisions. As it can also identify any form of bias that has crept in, it shall aid data scientists to eradicate them out at an early stage. Thus XAI can foster trust for all users due to enhanced transparency. Additionally, it can make the process of decision-making more systematic and accountable. This is why XAI often converses with deep learning and its essential role in the FAT ML model (fairness, accountability, and transparency in machine learning).
In terms of industrial application, XAI can be influential by allowing the healthcare industry to manage, organize, and analyze its colossal datasets, and eventually guide medical professionals into understanding how these AI-based conclusions are made. In manufacturing, when there arises an instance of decision making, explainable AI offers a set of consistent and debatable solutions for fixing and maintaining equipment. Besides, it can also explain why a specific task or technique was prioritized, and allow the user to choose other possible, better choices which can enable the machine to improve its performance. This also applies in logistics, automobile, and other industries where XAI will be a critical part of mechanism development, particularly regarding safety.
Although it is still in its nascent stages, further research may lead to the social roles of XAI. These can range from coordinating with other agents to connect the knowledge, developing cross-disciplinary insights to explaining the reason, and logic to individuals. With AI gaining popularity XAI shall soon become even more important.
“Explainable AI is important to a business because it gives us new ways to solve problems, appropriately scale processes, and minimize the opportunity for human error. That improved visibility helps increase understanding and improves the customer experience,” says Collins, the SAS CIO.