Directions for Explainable Knowledge-Enabled Systems (original) (raw)
Related papers
Foundations of Explainable Knowledge-Enabled Systems
2020
Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the capabilities of the artificial intelligence systems at the time. With the proliferation of AI-enabled systems in sometimes critical settings, there is a need for them to be explainable to end-users and decision-makers. We present a historical overview of explainable artificial intelligence systems, with a focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.
Innovations in Explainable AI : Bridging the Gap Between Complexity and Understanding
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.
The Role of Human Knowledge in Explainable AI
Data
As the performance and complexity of machine learning models have grown significantly over the last years, there has been an increasing need to develop methodologies to describe their behaviour. Such a need has mainly arisen due to the widespread use of black-box models, i.e., high-performing models whose internal logic is challenging to describe and understand. Therefore, the machine learning and AI field is facing a new challenge: making models more explainable through appropriate techniques. The final goal of an explainability method is to faithfully describe the behaviour of a (black-box) model to users who can get a better understanding of its logic, thus increasing the trust and acceptance of the system. Unfortunately, state-of-the-art explainability approaches may not be enough to guarantee the full understandability of explanations from a human perspective. For this reason, human-in-the-loop methods have been widely employed to enhance and/or evaluate explanations of machine...
Notions of explainability and evaluation approaches for explainable artificial intelligence
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 that lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested, coupled with several studies attempting to define the concept of explainability and its evaluation. This systematic review contributes to the body of knowledge by clustering all the scientific studies via a hierarchical system that classifies theories and notions related to the concept of explainability and the evaluation approaches for XAI methods. The structure of this hierarchy builds on top of an exhaustive analysis of existing taxonomies and peer-reviewed scientific material. Findings suggest that scholars have identified numerous notions and requirements that an explanation should meet in order to be easily understandable by end-users and to provide actionable information that can inform decision making. They have also suggested various approaches to assess to what degree machine-generated explanations meet these demands. Overall, these approaches can be clustered into human-centred evaluations and evaluations with more objective metrics. However, despite the vast body of knowledge developed around the concept of explainability, there is not a general consensus among scholars on how an explanation should be defined, and how its validity and reliability assessed. Eventually, this review concludes by critically discussing these gaps and limitations, and it defines future research directions with explainability as the starting component of any artificial intelligent system.
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach
SSRN Electronic Journal, 2020
The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits their potential use in safety-critical applications. To achieve trust and accountability, designers and operators of machine learning algorithms must be able to explain the inner workings, the results and the causes of failures of algorithms to users, regulators, and citizens. The originality of this paper is to combine technical, legal and economic aspects of explainability to develop a framework for defining the "right" level of explainability in a given context. We propose three logical steps: First, define the main contextual factors, such as who the audience of the explanation is, the operational context, the level of harm that the system could cause, and the legal/regulatory framework. This step will help characterize the operational and legal needs for explanation, and the corresponding social benefits. Second, examine the technical tools available, including post hoc approaches (input perturbation, saliency maps...) and hybrid AI approaches. Third, as function of the first two steps, choose the right levels of global and local explanation outputs, taking into the account the costs involved. We identify seven kinds of costs and emphasize that explanations are socially useful only when total social benefits exceed costs.
Explainable Artificial Intelligence in Machine Learning
Capstone Project Report, 2024
Explainable Artificial Intelligence aims to assist in opening the blackbox of opaque algorithms, especially present in machine learning systems, so they can earn due trustworthiness. This work employs a systematic literature review in order to understand how the concepts of explainability and interpretability, among others, are defined in this context. It offers a structured vocabulary to better comprehend and classify artificial intelligence systems and models, as well as the techniques used to make them more intelligible. Inspired by Human-Centered Computing notions, it becomes crucial to consider the audience to which explanations are owed, emphasizing that it is composed of a community of heterogeneous users.
2023
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.