Discover how Explainable AI (XAI) builds trust by making AI predictions transparent and reliable across healthcare, security, autonomous driving, and more.
Have you ever seen a response from an artificial intelligence (AI) tool like ChatGPT and wondered how it came to that conclusion? Then, you’ve met AI’s “black box” concern. It’s a term that refers to the lack of clarity about how AI models process and analyze data. Even AI engineers and scientists who work with cutting-edge AI algorithms regularly can find it challenging to fully understand their inner workings. In fact, only 22% of IT professionals truly understand the capabilities of AI tools.
The uncertainty surrounding how AI models make decisions can be risky, especially in critical areas such as computer vision in healthcare and AI in finance. However, significant progress is being made to tackle these challenges and improve transparency.
In particular, explainable AI (XAI) focuses solely on solving this concern. Simply put, it is a set of processes and methods that helps human users understand and trust the results or outputs given by complex machine learning algorithms.
XAI can help developers ensure that AI systems are working as expected. It can also help AI companies meet regulatory standards. In this article, we’ll explore explainable AI and its wide range of use cases. Let’s get started!
Explainability is key when working with AI. This is especially true when it comes to the subfield of AI, computer vision, which is widely used in applications in industries like healthcare. When using vision models in such sensitive industries, it is important that the model’s workings are transparent and interpretable to everyone.
Interpretability in computer vision models helps the users have a better understanding of how a prediction was made and the logic behind it. Transparency adds to this by making the model’s workings clear for everyone by clearly outlining the model's limitations and ensuring data is used ethically. For instance, computer vision can help radiologists efficiently identify health complications in X-ray images.
However, a vision system that is just accurate isn’t enough. The system also needs to be able to explain its decisions. Let’s say the system could show which parts of the image led to its conclusions - then, any outputs would be clearer. Such a level of transparency would help medical professionals double-check their findings and make sure that patient care meets medical standards.
Another reason why explainability is essential is that it makes AI companies accountable and builds trust in users. Trustworthy AI leads to users feeling confident that AI innovations work reliably, make fair decisions, and handle data responsibly.
Now that we’ve discussed why explainability matters in computer vision, let’s take a look at the key XAI techniques used in Vision AI.
Neural networks are models inspired by the human brain, designed to recognize patterns and make decisions by processing data through interconnected layers of nodes (neurons). They can be used to solve complex computer vision problems with high accuracy. Even with this accuracy, they are still black boxes by design.
Saliency maps are an XAI technique that can be used to help make sense of what neural networks are seeing when they analyze images. It can also be used to troubleshoot models in case they’re not performing as expected.
Saliency maps work by focusing on which parts of an image (pixels) define a model’s predictions. This process is very similar to backpropagation, where the model traces back from predictions to the input. But instead of updating the model's weights based on errors, we're just looking at how much each pixel "matters" for the prediction. Saliency maps are very useful for computer vision tasks like image classification.
For example, if an image classification model predicts that an image is of a dog, we can look at its saliency map to understand why the model thinks it’s a dog. This helps us identify which pixels affect the output the most. The saliency map would highlight all the pixels that contributed to the final prediction of the image being a dog.
Class Activation Mapping is another XAI technique used to understand what parts of an image a neural network focuses on when making image classification predictions. It works similarly to saliency maps but focuses on identifying important features in the image instead of specific pixels. Features are patterns or details, like shapes or textures, that the model detects in the image.
Methods like Gradient Weighted Class Activation Mapping (Grad-CAM) and Grad-CAM++ build on the same idea, with some improvements.
Here’s how CAM works:
Grad-CAM improves on this by using gradients, which are like signals showing how much each feature map influences the final prediction. This method avoids the need for GAP and makes it easier to see what the model focuses on without retraining. Grad-CAM++ takes this a step further by focusing only on positive influences, which makes the results even clearer.
Counterfactual explanations are an important element of explainable AI. A counterfactual explanation involves describing a situation or outcome by considering alternative scenarios or events that did not happen but could have happened. It can demonstrate how changes in specific input variables lead to different outcomes, such as: “If X hadn’t happened, Y wouldn’t have occurred.”
When it comes to AI and computer vision, a counterfactual explanation identifies the smallest change required in an input (such as an image or data) to cause an AI model to produce a different, specific result. For instance, altering the color of an object in an image could change an image classification model’s prediction from "cat" to "dog."
Another good example would be changing the angle or lighting in a facial recognition system. This could cause the model to identify a different individual, showing how small changes in input can influence the model’s predictions.
The simplest way to create these explanations is by trial and error: you can randomly change parts of the input (like features of the image or data) until the AI model gives you the desired result. Other methods include model-agnostic approaches, which use optimization and search techniques to find changes, and model-specific approaches, which rely on internal settings or calculations to identify the changes needed.
Now that we’ve explored what XAI is and its key techniques, we can walk through how it is used in real life. XAI has diverse applications across many fields. Let’s dive into some use cases that highlight its potential:
Explainable AI makes it easier to understand how AI systems work and why they make certain decisions. Transparency about AI models builds trust and accountability. Knowledge is power and helps AI innovations be used more responsibly. In critical areas like healthcare, security, autonomous driving, and legal systems, XAI can be used to help developers and users understand AI predictions, identify errors, and ensure fair and ethical use. By making AI more transparent, XAI bridges the gap between technology and human trust, making it safer and more reliable for real-world applications.
To learn more, visit our GitHub repository, and engage with our community. Explore AI applications in self-driving cars and agriculture on our solutions pages. 🚀
