A SURVEY ON EXPLAINABLE AI: TECHNIQUES AND CHALLENGES

Lecture Notes in Computer Science, 2020

The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasing the transparency of automatically learned prediction models, as for instance by deep or reinforcement learning. The second is aimed at anticipating the negative impact of opaque models with the desire to regulate or control impactful consequences of incorrect predictions, especially in sensitive areas like medicine and law. The formulation of methods to augment the construction of predictive models with domain knowledge can provide support for producing human understandable explanations for predictions. This runs in parallel with AI regulatory concerns, like the European Union General Data Protection Regulation, which sets standards for the production of explanations from automated or semi-automated decision making. Despite the fact that all this research activity is the growing acknowledgement that the topic of explainability is essential, it is important to recall that it is also among the oldest fields of computer science. In fact, early AI was re-traceable, interpretable, thus understandable by and explainable to humans. The goal of this research is to articulate the big picture ideas and their role in advancing the development of XAI systems, to acknowledge their historical roots, and to emphasise the biggest challenges to moving forward.

@KidOnWallSt, 2025

Theoretical foundations and definitions  Methodological approaches (inherently interpretable models, post-hoc methods, attention-based techniques)  Technical challenges and limitations  Domain-specific applications (healthcare, finance, criminal justice, education)  Regulatory and ethical frameworks  Current research trends and future directions  Implementation challenges and barriers  Case studies and practical applications  Policy and societal implications  Comprehensive conclusion  Academic references

IEEE Access

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories. INDEX TERMS Explainable artificial intelligence, interpretable machine learning, black-box models. I. INTRODUCTION A. CONTEXT

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

The integration of Artificial Intelligence (AI) into various domains has witnessed remarkable advancements, yet the opacity of complex AI models poses challenges for widespread acceptance and application. This research paper delves into the field of Explainable AI (XAI) and explores innovative strategies aimed at bridging the gap between the intricacies of advanced AI algorithms and the imperative for human comprehension. We investigate key developments, including interpretable model architectures, local and visual explanation techniques, natural language explanations, and model-agnostic approaches. Emphasis is placed on ethical considerations to ensure transparency and fairness in algorithmic decision-making. By surveying and analyzing these innovations, this research contributes to the ongoing discourse on making AI systems more accessible, accountable, and trustworthy, ultimately fostering a harmonious collaboration between humans and intelligent machines in an increasingly AI-driven world.

2020

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested. This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system with four main clusters: review articles, theories and notions, methods and their evaluation. It also summarises the state-of-the-art in XAI and recommends future research directions.

Applied Sciences

Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. The literature shows evidence from numerous studies on the philosophy and methodologies of XAI. Nonetheless, there is an evident scarcity of secondary studies in connection with the application domains and tasks, let alone review studies following prescribed guidelines, that can enable researchers’ understanding of the current trends in XAI, which could lead to future research for domain- and application-specific method development. Therefore, this paper presents a systematic literature review (SLR) on the recent de...

ArXiv, 2021

Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and prediction skills, their functioning does not allow obtaining detailed explanations of their behaviour. As opaque machine learning models are increasingly being employed to make important predictions in critical environments, the danger is to create and use decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing machine learning models with explainability. The reason is that EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This tutorial is meant to be the go-to handbook for any audience with a computer science background aiming at getting intuitive insights of machine learning models, accompa...

Proceedings of the Annual Hawaii International Conference on System Sciences, 2021

The diffusion of artificial intelligence (AI) applications in organizations and society has fueled research on explaining AI decisions. The explainable AI (xAI) field is rapidly expanding with numerous ways of extracting information and visualizing the output of AI technologies (e.g. deep neural networks). Yet, we have a limited understanding of how xAI research addresses the need for explainable AI. We conduct a systematic review of xAI literature on the topic and identify four thematic debates central to how xAI addresses the black-box problem. Based on this critical analysis of the xAI scholarship we synthesize the findings into a future research agenda to further the xAI body of knowledge.

Information Fusion, 2020

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

ArXiv, 2021

The field of explainable AI (XAI) has quickly become a thriving and prolific community. However, a silent, recurrent and acknowledged issue in this area is the lack of consensus regarding its terminology. In particular, each new contribution seems to rely on its own (and often intuitive) version of terms like “explanation” and “interpretation”. Such disarray encumbers the consolidation of advances in the field towards the fulfillment of scientific and regulatory demands e.g., when comparing methods or establishing their compliance w.r.t. biases and fairness constraints. We propose a theoretical framework that not only provides concrete definitions for these terms, but it also outlines all steps necessary to produce explanations and interpretations. The framework also allows for existing contributions to be recontextualized such that their scope can be measured, thus making them comparable to other methods. We show that this framework is compliant with desiderata on explanations, on ...