![]() Explainability algorithmsĮxplainability or Explainable AI (XAI) is the set of methodologies and algorithms that allow the production of human-understandable explanations for black-box or opaque models. And quite often, you realize that the less you know about it, the less you trust it. ![]() It all boils down to understanding the model in simple terms. In practice, some model types are so highly complex that, even with access to the training data, algorithm, and predictions, an expert wouldn't be able to explain why the model returned a certain prediction. How can you understand the model's predictions? What if the outputs do not correspond to your intuition? For example, what if people with higher incomes have loan applications rejected at higher rates? Or what if a traditionally underserved group has a higher than expected rate of approvals? Using a model with hundreds of parameters, this might go undetected. One example is a simple black-box model that recommends whether a loan application should be approved. In AI/ML, black-box predictive models make inputs and outputs observable, but not their internal functions. This article is an overview of my presentation Explainable AI for business processing models at DevConf.CZ 2022, which you can watch in its entirety below. ![]() Not only is this an ethical concern but also a legal-compliance issue as AI and ML become more regulated. It is increasingly common to find ML predictive models embedded in automation workflows to facilitate automated decision-making, for instance.Īlthough learning from historical data or classifying and predicting scenarios is beneficial, ML techniques have not always been subject to the same level of transparency, audibility, and interpretability as their process-automation counterparts.īeing able to assess, understand, debug, and benchmark AI and ML models is a fundamental issue when used in processes that could directly impact business decisions and people's lives. ![]()
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