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<p class="MsoNormal"><b>AIJ </b><b>Special Issue on Explainable Artificial Intelligence</b><b><o:p></o:p></b></p>
<p class="MsoNormal"><b><span style="font-size:10.0pt;color:black"><o:p> </o:p></span></b></p>
<p class="MsoNormal">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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Topics</b><b><o:p></o:p></b></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><i>Human-centric Explainable AI</i>: 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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><i>Theoretical and Philosophical Foundations</i>: 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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><i>Explainable Black-box Models</i>: 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.
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><i>Knowledge Representation and Machine Learning</i>: 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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><i>Interactive Explanation</i>: 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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><i>Historical Perspectives</i>: 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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><i>Case Study Reports</i>: 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.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Submission Instructions</b><b><o:p></o:p></b></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Submissions will be open from 1 August 2019 until 1 March 2020. and can be made using the EVISE system: http://www.evise.com/evise/faces/pages/navigation/NavController.jspx?JRNL_ACR=ARTINT . When submitting your manuscript please select
the article type "VSI: Explainable AI" Please submit your manuscript before the submission deadline.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular
issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Please see an example here: https://www.sciencedirect.com/journal/science-of-the-total-environment/special-issue/10SWS2W7VVV<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and the link to submit your manuscript is available on the Journal's homepage.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Important dates</b><b><o:p></o:p></b></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Submissions open: 1 August 2019<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Submissions close: 1 March 2020<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Final decisions: 1 August 2020<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">As this is a rolling special issue, papers will be published in the current regular issue as they are accepted and pulled into the online Special Issue<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Guest editors</b><b><o:p></o:p></b></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Tim Miller (tmiller@unimelb.edu.au)<o:p></o:p></p>
<p class="MsoNormal">University of Melbourne, Australia<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Robert Hoffman (rhoffman@ihmc.us)<o:p></o:p></p>
<p class="MsoNormal">Institute for Human and Machine Cognition<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Daniele Magazzeni (daniele.magazzeni@kcl.ac.uk)<o:p></o:p></p>
<p class="MsoNormal">King's College London<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Julie Shah (julie_a_shah@csail.mit.edu)</p>
<p class="MsoNormal">Massachusetts Institute of Technology</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Randy Goebel (goebel@cs.ualberta.ca)</p>
<p class="MsoNormal">University of Alberta</p>
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