A 2020 survey found that over 50% of companies have integrated artificial intelligence (AI)/ machine learning (ML) in at least one aspect of their business. Making predictions through the use of AI/ML is beneficial for any company that wants to optimize its data. Over 50% of executives believe AI/ML increases productivity in their workplace.
However, understanding how AI/ML predictions are made can seem complicated, overwhelming, and frustrating. That is where explainable artificial intelligence (XAI) comes in.
Why Should We Understand the Data?
There are various reasons that an organization may want AI/ML explainability. These include ensuring the system is working as it should, meeting regulatory standards, or changing or challenging an outcome. Additionally, it allows users to truly understand deep learning, machine learning, and neural networks.
Understanding AI/ML gives employees and companies a leading advantage. Not only are they able to explain and understand the AI/ML behind predictions, but when errors arise, they can understand where to go back and make improvements.
A deeper understanding of AI/ML allows businesses to know whether their AI/ML is making valuable predictions or whether they should be improved upon. This can ensure that incorrect data is spotted early on and stopped before decisions are made.
Essentially, explainable AI/ML allows businesses to optimize their data truly.
The Advantages of Explainable AI/ML
Explainable artificial intelligence and machine learning offer various benefits; however, they can also face a few challenges that businesses may face.
Inherited bias
Computers and AI/ML are not foolproof. They are defined by rules and linear logic, which can sometimes limit their decision-making capabilities. In some cases, AI/ML will replicate discrimination through data sets.
This essentially means that it uses predefined parameters biased towards one particular solution. This can be difficult to spot at times, so it is essential to understand the processes going on behind the scenes.
Improved Readability
If higher-ups in a company need to understand why the AI/ML is predicting what it does, employees need to be ready to explain.
However, even some of the savviest programmers can struggle to comprehend how an AI/ML “thinks” and works. On the other hand, explainable AI/ML can demonstrate its process. It provides insight into the decision-making process and allows for a more transparent environment.
Developing Improvements
A solution or prediction is only half the battle. While artificial intelligence and machine learning might offer the correct solution, it typically does not explain how it came up with it. While this surface-level information is valuable, the why behind the solution is equally, if not more important.
For example, AI/ML might predict that people may be more prone to buying a specific product during a particular season. However, users would not have the information to ascertain how the AI/ML came to that conclusion in a black-box setting.
Key Takeaways
Artificial intelligence and Machine Learning can be a considerable asset to many businesses. However, it is crucial to understand what is going on behind the scenes. Explainable AI/ML is the solution to ensuring organizations are fully aware of the processes behind predictions made by AI/ML.
When building models in PetroVisor, we include many common visualizations such as PDP, ICE, and Feature Importance and interactive simulations and “playgrounds” to help end-users understand what features influence the models. To learn more about the technical details behind model explainability, we love the online e-book “Interpretable Machine Learning” by Christoph Molnar.
XAI allows for more confidence in the decision-making processes and better transparency in understanding the why and how behind the solution.
June 7, 2022