5^th International Workshop on

EXplainable and TRAnsparent AI and Multi-Agent Systems

(EXTRAAMAS 2023)

London, UK, 29 May 2023

The 5^th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems (EXTRAAMAS) runs since 2019, and is a well-established workshop and forum. It aims to discuss and disseminate research on explainable artificial intelligence, with a particular focus on intra/inter-agent explainability and cross-disciplinary perspectives. In its 5th edition, EXTRAAMAS 2023 identifies four particular focus topics with the ultimate goal of strengthening cutting-edge foundational and applied research.

1. XAI Fundamentals.
EXTRAAMAS encourages the submission of seminal and visionary research papers.

2. XAI in Action: Applied perspectives.
EXTRAAMAS explicitly encourages the submission of applied research and demo papers.

3. XAI and Law Cross-disciplinary Perspectives.

EXTRAAMAS will feature the keynote titled "Untrustworthy AI" given by Jeremy Pitt, Professor at Imperial College London.

This of course comes in addition to the main theme of the workshop, focused as usual on XAI fundamentals. Below the four tracks for this year. Workshop CfP

All accepted papers are eligible for publication in the Springer Lecture Notes of Artificial Intelligence conference proceedings (after revisions have been applied).

Tracks of EXTRAAMAS

Track 1: XAI in symbolic and subsymbolic AI: the “AI dichotomy” separating symbolic AKA classical AI from connectionism AI has been persistent for more than seven decades. Nevertheless, the advent of explainable AI has accelerated and intensified the efforts to bridge this gap, since providing faithful explanations of black-box machine learning techniques would necessarily mean combining symbolic and subsymbolic AI. This track aims at discussing the recent works in this hot-topic of AI.
Track 1 chair: Dr. Giovanni Ciatto.

XAI for Machine learning

Explainable neural networks

Symbolic knowledge injection or extraction

Neuro-symbolic computation

Computational logic for XAI

Multi-agent architectures for XAI

Surrogate models for sub-symbolic predictors

Explainable planning (XAIP)

XAI evaluation

Track 2: XAI in negotiation and conflict resolution: Conflict resolution (e.g., agent-based negotiation, voting, argumentation, etc.) has been a prosperous domain within the MAS community since its foundation. However, as agents and the problems they are tackling become more complex, incorporating explainability becomes vital to assess the usefulness of the supposedly conflict-free solution. This is the main topic of this track, with a special focus on MAS negotiation and explainability.
Track 2 chair: Dr. Reyhan Aydogan.

Explainable conflict resolution techniques/frameworks

Explainable negotiation protocols and strategies

Explainable recommendation systems

Trustworthy voting mechanisms

Argumentation for explaining the process itself

Argumentation for explaining and supporting the potential outcomes

Explainable user/agent profiling (e.g., learning user's preferences or strategies)

User studies and assessment of the aforementioned approaches

Applications (virtual coaches, robots, IoT)

Track 3: Explainable Robots and Practical Applications: Explainable robots have been one of the main topics of XAI for several years. The main interest of this track is to publish the latest works whose focus is notably on (i) the impact of embodiment on explanation, (ii) explainability for remote robots, (iii) how humans receive and perceive explanations by robots, and (iv) practical XAI applications & simulations.
Track 3 chair: Dr. Yazan Mualla.

Explainable remote robots

Explainability and embodiment

Practical XAI applications

Emotions in XAI

Perception in XAI

Human-Computer Interaction (HCI) studies

Explanation of communication and reception

Agent simulations and XAI

Track 4: (X)AI in Law and Ethics: complying with regulation (e.g., GDPR) is among the main objectives for XAI. The right to explanation is key to ensuring transparency of ever more complex AI systems dealing with a multitude of sensitive AI applications. This track discusses works related to explainability in AI ethics, machine ethics, and AI & Law.
Track 4 chair: Rachele Carli.

XAI in AI & Law

Fair (X)AI

XAI & Machine Ethics

Bias reduction

Deception and XAI

Nudging and XAI

Legal issues of XAI

Liability and XAI

XAI, Transparency, and the Law

Enforceability and XAI

Culture-aware systems and XAI