Artificial intelligence (AI) has been proving its use for humanity as it helps provide solutions to complex problems in such sectors as energy, transportation, education, and more. However, the technology is often limited in terms of giving straightforward answers. Most AI systems, such as superhuman AI, remain very goal-oriented. They can only be programmed for one goal and sometimes go to the extreme, putting users in harm’s way. For example, in order to achieve a certain objective, they may work without thinking about the health and safety of users. 

So it isn’t surprising that we see a clamor to make AI systems more transparent in terms of how these come up with resolutions to problems. That is the foundation of explainable AI (XAI).

What is Explainable Artificial Intelligence (XAI)?

XAI is a field within machine learning (ML) that helps address issues concerning how AI systems make conclusions. Often, developers don’t explain how the machines they built come up with choices to achieve predetermined tasks. XAI provides users an insight into the data an AI system uses, the variables it applies, and the decision-making process it carries out to derive recommendations. As such, users can expect answers to questions like:

  • How did the AI system come up with a particular prediction?
  • Why did it choose one course of action over another?
  • Did it succeed or fail?

While the responses to the questions above might shed more light and ease users’ minds, XAI has a few hurdles left to overcome before it can indeed be considered a suitable answer to humankind’s remaining doubts about AI.

Current Challenge for XAI

Most of the fear arising from using AI comes from not knowing how machines come up with decisions. In AI systems, explanations can be obtained only by deciphering the ML algorithms these use.

But while basic ML concepts such as Bayesian classifiers and decision trees provide some form of transparency that enables users to trace the decision-making process without compromising its accuracy, more sophisticated algorithms like artificial neural networks can provide explanations without sacrificing efficiency and performance. Therein lies the rub. It will take some time to resolve these issues because, in most cases, it is difficult to achieve fast response without sacrificing quality. But when they are resolved, experts believe that XAI can alleviate concerns, allowing AI systems to improve lives further.

Can Explainable Artificial Intelligence Impact Lives?

While XAI is still in the development stages, experts believe it will prove beneficial in strictly regulated industries such as the healthcare and financial services sectors where human biases and ethics play a role.

Explainable Artificial Intelligence in Healthcare

XAI would prove useful in the healthcare industry, mainly because of growing concerns about adhering to the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and maintaining patient-doctor confidentiality. Doctors and other healthcare providers often find it hard to trust AI systems because these lack transparency. 

XAI can ease healthcare practitioners’ qualms so patients can benefit from AI systems’ ability to perform accurate medical diagnoses and predict drug efficacy. With patients’ medical history and genetic information, doctors may soon find ways to help them avoid suffering from the same illnesses they may be predisposed to contract aided by preventive medication.

Explainable Artificial Intelligence in Financial Services

Regulators often demand that financial service providers explain their processes to ensure that providers consider the best interests of customers and investors in decision-making. It is also a fact that AI remains a novel concept for most regulators. But if XAI can dissect processes enough to satisfy the regulators’ queries, they may be more open to AI use. XAI can help with that.

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Granted, XAI still has a long way to go, especially since most AI algorithms are hard to explain in a format that any person can understand. That is precisely the reason why XAI algorithms remain limited. But should it truly take off, then AI system use might become a norm.