Human-Interpretable Machine Learning

In the last couple of decades, the increasing disposal of large volumes of data, generated both by humans and machines (i.e., the so-called "Big Data" phenomenon), has opened up unprecedented challenges, which, in turn, have propelled remarkable advancements in the machine learning (ML) and, more generally, artificial intelligence (AI) fields. The application of ML and AI has proven extremely effective to solve business-critical tasks in several domains: e.g., image recognition in healthcare, failure prediction in manufacturing, credit risk assessment in finance, just to name a few. However, ML/AI models are often perceived as "black-boxes": they are given inputs and hopefully produce desired outputs. There are many circumstances, in fact, where human-interpretability is crucial to understand (i) why a model outputs a certain prediction on a given instance (interpretability), (ii) which adjustable features of that instance contribute the most to the given prediction (explainability), and (iii) how to modify the instance so as to change the prediction made by the model (actionability). This need is also formally included in the European Union's General Data Protection Regulation (GDPR), which states that any business using personal data for automated processing must be able to explain how the system makes decisions (see Article 22 of GDPR).