Is Explainable Artificial Intelligence a Distant Dream?
Transparency in AI’s working can be headache-inducing for organizations that incorporate the technology in their daily operations.
So, what can they do to put their concerns surrounding explainable artificial intelligence (AI) requirements to rest?
AI’s far-reaching advantages in any industry are pretty well-known by now. We are aware of how artificial intelligence helps thousands of companies around the world by speeding up their operations and allowing them to use their personnel more imaginatively. Additionally, the long-term cost and data security benefits of AI incorporation have also been documented countlessly by several tech columnists and bloggers. AI does have its fair share of problems, though. One such problem is the technology’s sometimes questionable decision-making. More importantly, though, the bigger issue is the slight lack of explainability whenever an AI-powered system makes embarrassing or catastrophic errors. Human beings make mistakes on a daily basis. Nevertheless, we know exactly how a mistake was made. There is a clear sequence of corrective actions that can be taken to avoid making a similar one in the future. However, some of AI’s errors are not explainable because data experts do not know how an algorithm came to a specific conclusion during an operation. Therefore, explainable AI should be a primary priority for organizations planning to implement the technology into their daily work as well as the ones that have incorporated it already.
One of the common fallacies regarding AI is that it is completely immune to making errors. Neural networks, especially in their early stages, can be fallible. At the same time, these networks execute their commands in a non-transparent manner. As mentioned earlier, the route taken for an AI model to arrive at a particular conclusion is not clarified at any point during an operation. Therefore, creating an interpretation of such an error becomes nearly impossible for even accomplished data experts.
AI’s transparency problem stands out in the healthcare industry. Consider this example: A hospital has a neural network or a black box AI model in place to diagnose a brain ailment in a patient. The intelligent system is trained to find data patterns from past records and the patient’s existing medical papers. Using predictive analysis, if the model forecasts that the subject is vulnerable to brain-related diseases in the future, then the reasons behind the prediction may usually not be 100 percent clear. For private and public organizations, here are the four main reasons to make AI’s working more transparent:
As specified earlier, stakeholders need to know the inner workings and reasoning logic behind an AI model’s decision-making process, especially for unexpected recommendations and decisions. An explainable AI system ensures that algorithms can make fair and ethical recommendations and decisions in the future. This can boost the compliance and trust in AI’s neural networks within organizations.
Explainable AI generally prevents system errors from taking place in work operations. The increased knowledge regarding an AI model’s existing weaknesses can be used to eliminate them. As a result, organizations have greater control over the outputs provided by AI systems.
As we know, AI models and systems need to undergo continuous improvements from time to time. Explainable AI’s algorithms will keep getting smarter over the course of regular system updates.
New threads of information will enable humankind to discover solutions for big problems of the current age (such as medicines or treatments to cure HIV AIDS; methods to handle attention deficit disorders). More importantly, these discoveries will be backed by conclusive evidence and justifications for universal verification.
