The role of ontologies and knowledge in Explainable AI

DC JURIX, 2019

This research aims to explore explainable artificial intelligence , a new sub field of artificial intelligence that is gaining importance in academic and business literature due to increased use of intelligent systems in our daily lives. As part of the research project, first of all, the necessity of the explainability in AI systems will be explained in terms of accountability, transparency, liability, and fundamental rights & freedoms. The latest explainable AI algorithms introduced by the AI researchers will be examined firstly from technical and then, from legal perspectives. Their statistical and legal competencies will be analyzed. After detecting the deficiencies of the current solutions, a comprehensive and technical AI system design will be proposed which satisfies not only the statistical requisites; but also the legal, ethical, and logical requisites.

Lecture Notes in Computer Science, 2020

We discuss a perspective aimed at making black box models more eXplainable, within the eXplainable AI (XAI) strand of research. We argue that the traditional end-to-end learning approach used to train Deep Learning (DL) models does not fit the tenets and aims of XAI. Going back to the idea of hand-crafted feature engineering, we suggest a hybrid DL approach to XAI: instead of employing end-to-end learning, we suggest to use DL for the automatic detection of meaningful, handcrafted high-level symbolic features, which are then to be used by a standard and more interpretable learning model. We exemplify this hybrid learning model in a proof of concept, based on the recently proposed Kandinsky Patterns benchmark, that focuses on the symbolic learning part of the pipeline by using both Logic Tensor Networks and interpretable rule ensembles. After showing that the proposed methodology is able to deliver highly accurate and explainable models, we then discuss potential implementation issues and future directions that can be explored.

Doctor of PhilosophyDepartment of Computer ScienceMajor Professor Not ListedSymbolic knowledge representation and reasoning and deep learning are fundamentally different approaches to artificial intelligence with complementary capabilities. The former are transparent and data-efficient, but they are sensitive to noise and cannot be applied to non-symbolic domains where the data is ambiguous. The latter can learn complex tasks from examples, are robust to noise, but are black boxes; require large amounts of --not necessarily easily obtained-- data, and are slow to learn and prone to adversarial examples. Either paradigm excels at certain types of problems where the other paradigm performs poorly. In order to develop stronger AI systems, integrated neuro-symbolic systems that combine artificial neural networks and symbolic reasoning are being sought. In this context, one of the fundamental open problems is how to perform logic-based deductive reasoning over knowledge bases by means of...

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.

Information Fusion, 106, 2024

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.

Proceedings of the Technology, Mind, and Society, 2018

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence (AI) technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of "many things" is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which draws attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or "patients" of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency.

Zenodo, 2024

Picture yourself in front of a master of chess who always gets it right but cannot tell you why. This scenario mirrors one of the most pressing challenges in modern artificial intelligence: the black box problem. Given that we would never trust a judge who cannot justify their verdicts, we ought to be wary of the same hold our AI systems in important decisions.

AI and Transparency, 2023

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are "weighted" differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a-highly needed-standard for the communication among interdisciplinary areas of AI.

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.