Special Issue on Explainable Artificial Intelligence
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
As intelligent systems become more widely applied (robots, automobiles, medical & legal decision-making), users and the general public are becoming increasingly concerned with issues of understandability and trust. The current generation of Intelligent systems based on machine learning seem to be inscrutable. Consequently, explainability has become an important research topic, in both research literature and the popular press. These considerations in the public discourse are partly responsible for the establishment of projects like DARPA's Explainable AI Project, European response to the General Data Protection Regulation, and the recent series of XAI Workshops at major AI conferences such as IJCAI. In addition, because "Explainability" is inherently about helping humans understand intelligent systems, XAI is also gaining interest in the human computer interaction (HCI) community.
The creation of explainable intelligent systems requires at least two major components. First, explainability is an issue of human-AI interaction; and second, it requires the construction of representations that support the articulation of explanations. The achievement of Explainable AI requires interdisciplinary research that encompasses Artificial Intelligence, social science, and human-computer interaction.
A recent survey published in AIJ (https://doi.org/10.1016/j.artint.2018.07.007) shows that there is a rich understanding in philosophy and cognitive & social psychology of how humans explain concepts to themselves and to others. This work addresses the development of a framework for the first issue noted above: what counts as an explanation to support the HCI aspects of XAI. The second challenge of building models to support explanation (especially in intelligent systems based on aspects of machine learning) is more scattered, ranging from from recursive application of deep learning all the way to the induction of logical causal models.
This special issue seeks contributions on foundational studies in Explainable Artificial Intelligence. In particular, we seek research articles that address the fact that explainable AI is both a technical problem and a human problem, and scientific work on explainable AI must consider that it is ultimately humans that need to understand technology.
The importance of the topic of Explainable AI is manifested by the number of conferences and conference sessions that on the topic that have been announced in recent months, along with calls for reports on explainability in specialized areas, such as robotics, planning, machine learning, optimisation, and multi-agent systems.
Human-centric Explainable AI:Submissions with the flavor of both an AI research report and a report on a human-behavioural experiment are of particular interest. Such submissions must convey details of the research methods (experimental designs, control conditions, etc.). There should be a presentation of results that adduce convincing empirical evidence that the XAI processes achieve genuine success at explaining to its intended users.
Theoretical and Philosophical Foundations:We invite submissions on the philosophical, theoretical or methodological issues in Explainable AI (XAI). In particular, we encourage submissions that go beyond standard issues of interpretability and casual attribution, and into foundations of how to provide meaningful insights from AI models that are useful for people other than computer scientists.
Explainable Black-box Models:We invite submissions that investigate how to provide meaningful and actionable insights on black-box models, especially machine learning approaches using opaque models such as deep neural networks. In particular, we encourage submissions that go beyond the extraction of interpretable features; for example, considering explanation as a process, building user mental models, contrastive explanation, etc.
Knowledge Representation and Machine Learning:Submissions that investigate the use of knowledge representation techniques, including user modelling, abductive reasoning, diagnosis, etc., are of interest. In particular, we encourage submissions that capitalize on the strengths of knowledge representation and explainable machine learning.
Interactive Explanation:Submissions are of interest if they report on research in which human users or learners interact with intelligent systems in an explanation modality, which leads to improvement in the performance of the human-machine work system. Submissions that regard explanation as an exploratory, interactive process are of particular interest. This is in contrast with the model that considers explanation as a one-way paradigm.
Historical Perspectives:One additional topic of particular interest is Expert Systems, since many of the current issues of interpretability, explainability, and explanation as a process first emerged in the era of Expert Systems. Brief historical retrospections on the fundamental problems are encouraged.
Case Study Reports:We invite short reports outlining case studies illustrating the consequences of a lack of explainability in intelligence systems, with the aim of providing motivating examples/benchmarks and challenge problems for the community.