PyData Yerevan 2022

The recent surge of interest in Machine Learning (ML) and Artificial Intelligence (AI) has spurred a wide array of models designed to make decisions in a variety of domains, including healthcare [1, 2, 3], financial systems [4, 5, 6, 7], and criminal justice [8, 9, 10], just to name a few. When evaluating alternative models, it may seem natural to prefer those that are more accurate. However, the obsession with accuracy has led to unintended consequences, as developers often strove to achieve greater accuracy at the expense of interpretability by making their models increasingly complicated and harder to understand [11]. This lack of interpretability becomes a serious concern when the model is entrusted with the power to make critical decisions that affect people’s well-being. These concerns have been manifested by the European Union’s recent General Data Protection Regulation, which guarantees a right to explanation, i.e., a right to understand the rationale behind an algorithmic decision that affects individuals negatively [12]. To address these issues, a number of techniques have been proposed to make the decision-making process of AI more understandable to humans. These “Explainable AI ” techniques (commonly abbreviated as XAI) are the primary focus of this talk. The talk will be divided into three sections, during which the audience will learn (i) the differences between existing XAI techniques, (ii) the practical implementation of some well-known XAI techniques, and (iii) possible uses of XAI as a conventional data analysis tool.