# XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
## 1 Introduction  
Machine Learning (ML) systems have significantly grown in popularity in the last decade. They are currently being used in several fields with increasing task-solving capabilities, from a human's everyday life (e.g., language translation) to decision-making in high-stake domains (e.g., clinical decision support). However, most ML systems are labeled as \"black-box\" models because their underlying structures are complex, nonlinear, and difficult to explain. These characteristics prevent domain experts from understanding the reasoning behind specific decisions, a vital requirement on domains such as medical diagnosis, criminal justice, or financial decision-making. ML algorithms' opacity brought up the need for interpretable algorithms creation, the main focus of the eXplainable Artificial Intelligence (XAI) field. According to [PERSON] and [PERSON] [1], there are three motivating factors: (i) the demand to produce more transparent models; (ii) the need for techniques that enable humans to interact with them; and (iii) the requirement of trustworthiness of their inferences. Moreover, the recently approved General Data Protection Regulation (GDPR) document [2] introduced the right of explanation. These guidelines aim to give individuals the right to obtain an explanation of the inference(s) automatically produced by a model, confront, and challenge an associated recommendation, particularly when it might negatively affect an individual legally, financially, mentally, or physically. Since explainability is an inherently human-centric property, XAI research has received increasing attention from scholars of several different domains in the research community. As stated in [3], \"currently, there is a broad array of definitions and expectations for XAI, which require multidisciplinary research efforts, as existing communities have different requirements and often have drastically different priorities and areas of specialization\". For instance, ML engineers aim to create either interpretable models or explain black-box models with post-hoc techniques. Meanwhile, Human-Computer Interaction (HCI) researchers are mainly focused on building solutions that satisfy end-user needs, independently of the technical approach adopted. There are also relevant discussions on the fields of Philosophy, Psychology, and Cognitive Science, particularly in merging the existing research regarding how people generate or evaluate explanations into current XAI research [4]. This evident multidisciplinary nature makes the design and evaluation of XAI systems an intrinsically challenging task. In fact, the effectiveness of explainability is founded on the \"perception and reception of the person receiving the explanation\", which means that the user's explainability needs greatly influence technical choices, both in terms of design and evaluation. As a result, designing and evaluating a XAI system can be considered as much of a design challenge as an algorithmic one [5]. The contributions of this work are threefold. First, we aim to provide an overview of the most relevant evaluation methods already proposed in the literature to evaluate XAI systems. Our extensive literature review identified an urgent need to standardize the categorization and terminologies used for the different explanations' proprieties and respective evaluation methods. Thus, our second contribution focuses on providing a new taxonomy to organize the already available XAI evaluation methods clearly and intuitively. The multidisciplinary nature of XAI research was considered an essential requirement during the design of this taxonomy, which resulted in a clear separation between Human-centred and Computer-centred methods. Third, we discuss possible ways to use the proposed taxonomy in the design and evaluation of XAI systems. The conducted literature review examined relevant papers from five prominent academic databases and bibliographic search engines, namely ScienceDirect, Engineering Village, ACM Digital Library, Arxiv and Google Scholar.