The benefits of self-explainable AI over interpretable AI

Would you trust an artificial intelligence algorithm that works eerily well, making accurate decisions 99.9% of the time, but is a mysterious black box? Every system fails every now and then, and when it does, we want explanations, especially when human lives are at stake. And a system that can’t be explained can’t be trusted. That is one of the problems the AI community faces as their creations become smarter and more capable of tackling complicated and critical tasks.

In the past few years, explainable artificial intelligence has become a growing field of interest. Scientists and developers are deploying deep learning algorithms in sensitive fields such as medical imaging analysis and self-driving cars. There is concern, however, about how these AI operate. Investigating the inner-workings of deep neural networks is very difficult, and their engineers often can’t determine what are the key factors that contribute to their output.

For instance, suppose a neural network has labeled the image of a skin mole as cancerous. Is it because it found malignant patterns in the mole or is it because of irrelevant elements such as image lighting, camera type, or the presence of some other artifact in the image, such as pen markings or rulers?

Researchers have developed various interpretability techniques that help investigate decisions made by various machine learning algorithms. But these methods are not enough to address AI’s explainability problem and create trust in deep learning models, argues Daniel Elton, a scientist who researches the applications of artificial intelligence in medical imaging.

[Read: Everything you need to know about recurrent neural networks]

Elton discusses why we need to shift from techniques that interpret AI decisions to AI models that can explain their decisions by themselves as humans do. His paper, “Self-explaining AI as an alternative to interpretable AI,” recently published in the arXiv preprint server, expands on this idea.

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What’s wrong with current explainable AI methods?

Classic symbolic AI systems are based on manual rules created by developers. No matter how large and complex they grow, their developers can follow their behavior line by line and investigate errors down to the machine instruction where they occurred. In contrast, machine learning algorithms develop their behavior by comparing training examples and creating statistical models. As a result, their decision-making logic is often ambiguous even to their developers.

Machine learning’s interpretability problem is both well-known and well-researched. In the past few years, it has drawn interest from esteemed academic institutions and DARPA, the research arm of the Department of Defense.

Efforts in the field split into two categories in general: global explanations and local explanations. Global explanation techniques are focused on finding general interpretations of how a machine learning model works, such as which features of its input data it deems more relevant to its decisions. Local explanation techniques are focused on determining which parts of a particular input are relevant to the decision the AI model makes. For instance, they might produce saliency maps of the parts of an image that have contributed to a specific decision.

RISE explainable AI example saliency map

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