Call for papers

Important dates (all times are 23:59 Anywhere on Earth, UTC-12)

Paper submission: extended to 8 May, 2024

Late submission HCAI Track: 18 May, 2024 (only for papers rejected from the IJCAI Human-Centred AI Track)

Notification: 4 June, 2024

Camera-ready submission: 30 June, 2024

In-person workshop: 4-5 August, 2024

Virtual event: August, 2024 [Dates TBC]

Targeted Participants and Topic Areas

XAI may interest researchers studying the topics listed below (among others). We are particularly interested in papers that draw out cross-disciplinary problems and solutions to explainability. The call for papers is divided into a general track and three special tracks: human-centered XAI; explainable sequential decision-making; and interpretable machine learning. We also have a special late-submission track for papers rejected from the IJCAI Human-centered AI track in the main conference.

Late submission track: Only for papers rejected from the IJCAI main conference Human-Centered AI track

Due to the author notification deadline of the IJCAI main conference human-centered AI track being on 16 May, which is after the workshop deadline, we have opened a special late submission for papers rejected from this IJCAI track. The deadline for this track is 18 May, 2024. To submit a paper to this track, please submit: (a) your paper; (b) the IJCAI reviews for the paper; and (c) optionally, a short description of any changes made in response to the reviewer comments.

Special track: Human-Centered XAI

XAI is particular in that its role is primarily to reveal the underlying workings of a system or elaborate on its reasoning. It deals with two core problems: (a) how to extract/uncover that reasoning; and (b) how to communicate that reasoning to the user. The intended focus of this track is on (b) whereby we are interested in how users interact with explanations, trust, automation bias, deception and performance. We encourage papers with emphasis on human-centered empirical evaluation and consideration for different stakeholders. Human-Centered XAI therefore, encompasses topics such as:

• Trust, reliance, and XAI

• Ethical XAI

• Reports on human behavioural experiments/studies

• Impact on users

• Effect on automation bias

• User preferences

• Communication approaches

• Metrics of explainability evaluation

• HCI for explainability 

• Psychological and philosophical foundations of explainability and interpretability

• Social aspects of XAI

• Interactive XAI

• XAI and social-, behavioural- and psychological-oriented disciplines

• Actionable recourse

• Contestability of (semi-)automated decisions

• Commonsense reasoning

• Decision making and sensemaking

Special track: Explainable Sequential Decision-making

The intended focus of the track is on explainable autonomous agents - systems that operate in the context of an environment, typically through a goal-driven sequence of decisions. This stands in contrast to the substantial existing work on interpretable machine learning, which generally focuses on the single input-output mappings of "black box" models such as neural networks. While such ML models are an important tool, intelligent behavior extends over time and needs to be explained and understood as such. Explainable SDM for example encompasses topics such as:

• Explainable/interpretable/intelligible reinforcement learning

• Explainable (classical) planning

• Explainable search

• Explainability in Multi-Agent Systems

• Sequential decision-making approaches as models of  explanatory dialogue with users

• Sequential decision-making approaches for and through negotiations or argumentation

• Explanation-aware sequential decision-making

• Integration of explainable agents and explainable deep learning, e.g. when DL models are guiding agent behaviors

• User interfaces/visualizations for explaining agent behavior, learning or planning

• Evaluation methods for explainable agents

• Explainability for embodied systems/robotics

• Other practical applications for explainability in sequential or goal-oriented tasks, e.g. in planning/scheduling, in pathfinding, etc.

• Agent policy summarization

• Formal foundations of explainable agency

• Cognitive, social, and philosophical theories of explainable agency

Special track: Interpretable Machine Learning

This track focuses on enhancing the transparency and explainability of the inner workings of machine learning models. With the increasing complexity of machine learning models and their widespread use in various domains, the need for interpretable machine learning has become more crucial than ever. This track welcomes submissions on intrinsically interpretable models (e.g., neuro-symbolic approaches), sparse interpretable models, as well as post-hoc explainability methods for machine learning. A particular focus of this track is the evaluation of the fidelity of explainability methods for machine learning models to ensure that they provide accurate and reliable insights into the model's decision-making processes. As such, the track encompasses topics such as:

• Transparent and interpretable machine learning models

• Sparse models

• Approaches for simplifying and approximating complex models while preserving fidelity

• Evaluation of post-hoc model fidelity

• Post-hoc methods for visualizing and understanding deep neural networks

• Theoretical foundations and metrics for interpretability in machine learning

• Benchmarking interpretability methods

• Causal inference in machine learning interpretability

• Interpretable machine learning in natural language processing

• Interpretable machine learning in computer vision

• Human-in-the-loop systems for improving interpretability

• Interpretability in the context of unsupervised and semi-supervised learning.

General track

The general track will focus on other areas of explainability. Topics in this track include:

• Technical approaches to explainability

• Approaches for computational evaluation

• Applied case studies

• New benchmarks

• Industry applications of XAI

• Industry insights on XAI

• Historical perspectives of XAI and mature surveys – please note that surveys should provide new and interesting insights into the field or focus on specific sub-areas, rather than 'just another set of descriptions about existing XAI techniques'.

Submission instructions

Submission site: https://cmt3.research.microsoft.com/XAIIJCAI2024

Page limit: Authors may submit long papers (7 pages plus unlimited pages of references) or short papers (4 pages plus unlimited page of references). 

Format: A PDF file, using the LaTeX styles or Word template for IJCAI.

Author details: The review process is double blind, so author details should be omitted from the submitted PDF file. Author information must be entered into the Microsoft CMT submission site.

Reviewing: All authors are expected to review 1-2 papers if called upon.

Supplementary material: Authors may submit supplementary material (e.g. appendices, data, source code) as an additional PDF file. Reviewers will not be expected to review this, so please make sure the main paper is self contained.

Copyright: The workshop proceedings will be non-archived, so authors keep the copyright and are free to submit the work to other venues after the review process (please do not submit to another venue simultaneously) or upload onto pre-print servers such as arXiv.