Explainable AI – explainable artificial intelligence – is the ability of an organization to understand how their machine learning models reach conclusions. The goal of explainable AI is to make the predictions made by these models transparent so that organizations have insights into how the underlying decisions are made.
In other words, explainable AI – sometimes called XAI – means that a person can follow the process used by the automated machine learning model to come up with the output, so that they can understand, trust, and manage the model. It allows the user to trace the output through the entire process and arrive back at the input that led it to the final result.
Think back to when you were in school, and your math teacher asked you to show your work when providing an answer. While getting the right answer was important, showing how you arrived at the response was equally as critical – as that demonstrates your decision-making process and confirms that you genuinely understand the concept.
The goal of artificial intelligence is to help businesses make data-driven decisions that will allow them to be more productive. However, if they do not understand how the model arrived at a prediction, they will not be able to make effective decisions or trust in the machine learning model.
It would be nearly impossible for data scientists to convince leaders within an organization to make decisions based on a model if they can’t articulate how the tool reached its conclusion. Explainable AI helps build confidence in the technology being used and can identify if there is human bias influencing the outputs.
This is especially important in healthcare, where a doctor may have to make a life-or-death decision based on information received from AI tools. Consider the doctor’s ability to explain his or her decision to patients or their family members – there is no question that the medical professional must have a complete understanding of how the model arrived at the prediction.
Explainable AI was able to discover human bias in healthcare machine learning models by identifying that the algorithms were more geared towards measuring the cost of healthcare rather than an illness itself. This led to those with lower commercial risk scores received less care – something that could not have been pinpointed without explainable AI.
At LogicPlum, our goal is to not only give you access to some of the most sophisticated machine learning models in the industry but also to make sure that your decision-makers understand how they work. We want you to have confidence in the results of our AI platform, so we provide you with many tools to incorporate explainable AI into your business processes.
A critical report that we provide is feature impact, which shows how much of a model’s outputs are dependent on each feature. Similarly, feature effects allow you to dive deeper and investigate how the features influence the machine learning mode’s decisions on a global level. We also give you access to prediction explanations, which explain the variables that impact every decision outcome and the magnitude of various features for every record.