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We usually understand AI as the technology that is able to replicate human behavior. Meaning that, when cognitive tasks such as problem solving or reading are performed by machines, we define it as AI
Explainable AI is a set of mathematical techniques that make Artificial Intelligence fully understandable by humans. One of the main problems regarding machine learning algorithms is the difficulty to understand its behaviour and reasoning to make predictions. This is way we refer to these models as “black-box“.
Creating transparent processes is the best way to ensure user trust. When analytical models are implemented, their opacity usually generates distrust. This results in slower AI adoption and sub-optimal decisions. Creating explainable artificial intelligence boosts trust and increases efficiency thanks to a better understanding of the decision process.
Although you may think algorithms cannot discriminate since they are not humans, many scandals have shown how AI biases can harm minorities. Understanding how models behave eases bias detection. Explainable AI is essential to deploy inclusive machine learning models.
Artificial Intelligence is usually implemented to automate processes and to make better and faster decisions. However, the lack of understanding can result in sub-optimal decisions. Thanks to Explainable AI (XAI), models will no longer output a value but a whole reasoning which will help you decide whether its predictions are useful or not.
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Request a demo and learn more
In EXPAI, we implement state-of-the-art techniques to explain Artificial Intelligence models. Explanations are represented through plots. Click “Learn More” buttons to discover all the details behind each method.
Measures the impact of each variable to the predictions of the model. It will help you monitor your model and validate whether it follows the right business rules.
It represents how the prediction changes as a function of a variable. Study how specific variables affect your decisions and monitor limit cases.
Get a detailed explanation of a selected prediction. Understand what variables are affecting that specific prediction. Identify possible errors and make optimal decisions.
Take better decisions by validating your business actions based on data and not intuition. Study different scenarios for your results by modifying one or multiple variables from your sample.
