What Is Explainable AI?
While the capabilities of artificial intelligence are impressive (to say the least), it’s not always apparent how a model reached a particular prediction or decision. What do the inner workings of the model look like? How did it get from point A to point B?
Enter: Explainable AI (XAI), the set of processes and methods that enable humans to understand the output of machine learning (ML) and AI algorithms, increasing trust in the results they produce while maintaining high prediction accuracy.
There are two major types of ML models: white box and black box. White box models refer to models whose inner workings are easy to understand, transparent, and interpretable. Black box models, on the other hand, are incredibly difficult to comprehend. In fact, even experts who can see their structure and weights are unable to completely understand and explain how these models work.
XAI techniques are used to explain how black box models work and clarify questions about their behavior. Its primary goal is to help end users understand, trust, and manage their AI algorithms.
Why Is Explainable AI Important?
As AI becomes more widely adopted across industries such as healthcare, manufacturing, finance, education, and even law enforcement, it’s essential that the models these organizations rely upon are trustworthy and accurate. This is especially important in situations that can have critical consequences, such as models impacting self-driving cars, medical diagnosis and treatments, and military drones.
Explainable AI decreases risks that come about when businesses don’t understand what their models are doing, such as ethical concerns or sensitive information leaks.
Understanding why a model makes the decisions that it does can also positively affect a number of critical business factors, including:
- Trust: The predictions made by an AI model are often used to alter or completely change the processes and operations of a company. By having a better understanding of how the model reached a particular prediction, it’s easier to trust a model to have a greater impact on the business.
- Transparency: XAI helps us understand how AI systems work by making the inner workings of the model comprehensible. This can greatly help to not only understand the results better, but to improve the system and rectify any pre-existing issues or errors.
- Accountability: By understanding how the model reached a particular prediction, it’s clear who is responsible for the results and executing upon them.
- Bias detection support: Ethics and morals have always been a point of concern when it comes to AI. In such a scenario, XAI helps detect any bias in the predictions and identify any skewed decisions.
- Regulatory compliance: XAI methods can ensure that your AI models aren’t violating any government mandates, further protecting your organization.
Top Explainable AI Use Cases
Where there’s a black box model, there’s a use case for explainable AI to increase transparency and trust in the model. Here are a few cross-industry examples of XAI:
Healthcare
When it comes to potentially life-threatening diagnoses, doctors don’t want to leave anything to chance. By using XAI methods, doctors can understand why a model made a particular conclusion, creating a more accurate treatment plan for their patients. This level of communication and accountability between patients and doctors helps create trust and mitigates the risk of critical errors.
Manufacturing
When an assembly line stops functioning properly, a black box model might just tell you that it will need maintenance in a certain number of days. Explainable AI can improve machine-to-machine communication in manufacturing while helping to create a better awareness between humans and machines, so that workers can understand why a system stopped working and how to better maintain it going forward.
Defense
Being able to trust AI applications is extremely important when it comes to defense as making a wrong decision can have deadly consequences, posing a very serious ethical threat—from misidentifying an object to misfiring on a target. XAI can explain the reasoning behind a decision made by autonomous vehicles during a war to mitigate risks.
Banking/FinTech
AI enables financial institutes to create better customer experiences while also improving loyalty, increasing profitability, and automating procedures. But, in such a highly regulated space, being able to trust and hold models accountable is essential—explainable AI can help.
The Case for Interpretability: Challenges With Explainable AI
For users to fully embrace and implement predictions offered by their AI models, they have to be able to trust those predictions. However, there are significant challenges with explaining complex AI and ML models, rather than creating interpretable models in the first place.
Here’s a few problems you might run into with XAI:
- Accuracy: When you have to create a solution to explain a model, it’s inevitable that something will get lost in translation, especially when your explainer is tasked with being significantly simpler than the original model. Which brings us to…
- Complexity: Another challenge is the vast amount of data you’re working with. How can you create a simple but meaningful explanation from a black box model? Different levels of knowledge will be required for different purposes, hence why determining the right information for the user can be difficult.
- Contradiction: Most XAI methods focus on providing an explanation of the AI processes, which might be contradictory to the application’s original context. This could create unrealistic explanations and defeat the original purpose of the solution.
Some data scientists are arguing against explainable AI in favor of interpretable machine learning. Their conviction lies around creating inherently interpretable, simpler models from the beginning, without compromising on accuracy. For now, XAI is necessary to interpret the black box models that businesses rely on every day.
Get Started With Responsible & Explainable AI
If you’re working in an industry where explainability is crucial to making business decisions, XAI should be a key aspect of your AI strategy. With explainable AI, you can get insight into the decisions your AI and ML models are making, increasing your trust in their predictions and minimizing potential risk.
Curious to learn more about the importance of model explainability? Check out our whitepaper, Model Explainability Explained, for insights on how to get executive buy-in on your data science projects and maximize the positive impact on your models’ ROI.
