In a wide range of industries and academic fields, artificial intelligence is becoming increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow in popularity and performance. Although AI models, particularly machine learning models, are successful in research, they have numerous limitations and drawbacks in practice. Furthermore, due to the lack of transparency behind their behavior, users need more understanding of how these models make specific decisions, especially in complex state-of-the-art machine learning algorithms. Complex machine learning systems utilize less transparent algorithms, thereby exacerbating the problem. This survey analyzes the significance and evolution of explainable AI (XAI) research across various domains and applications. Throughout this study, a rich repository of explainability classifications and summaries has been developed, along with their applications and practical use cases. We believe this study will make it easier for researchers to understand all explainability methods and access their applications simultaneously.

The use of AI and machine learning models in the industry is rapidly increasing. Because of this growth and the noticeable performance of these models, more mission-critical decision-making intelligent systems have been developed. Despite their success, when used for decision-making, AI solutions have a significant drawback: transparency. The lack of transparency behind their behaviors, particularly in complex state-of-the-art machine learning algorithms, leaves users with little understanding of how these models make specific decisions. To address this issue, algorithms such as LIME and SHAP (Kernel SHAP) have been introduced. These algorithms aim to explain AI models by generating data samples around an intended test instance by perturbing the various features. This process has the drawback of potentially generating invalid data points outside of the data domain. In this paper, we aim to improve LIME and SHAP by using a pre-trained Variational AutoEncoder (VAE) on the training dataset to generate realistic data around the test instance. We also employ a sensitivity feature importance with Boltzmann distribution to aid in explaining the behavior of the black-box model surrounding the intended test instance.

An N-dimensional game theory-based model for multi-actor predictive analytics is presented in this article. The proposed model expands our previous work on two-dimensional group decision model for predictive analytics. The one-dimensional models are used for the problems where actors are interacting in a single issue space only. This is less than an ideal assumption since; in most cases, players' strategies may depend on the dynamics of multiple issues when dealing with other players. In this work, the one-dimensional model is expanded to N-dimensional model by considering different positions, and separate salience values, across different axes for the players. The model predicts an outcome for a given problem by taking into account stakeholder's positions in different dimensions and their conflicting perspectives. To illustrate the capability of the proposed model, three case studies have been presented.