The International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) is an annual international conference dedicated to emerging and challenging topics in intelligent data analytics and associated learning systems and paradigms. Following the recent successful events: IDEAL 2024 in Valencia, IDEAL2023 in Évora, IDEAL2022 in Manchester (hybrid), IDEAL2021 (virtual), IDEAL2020 in Guimarães (virtual), IDEAL2019 in Manchester, and IDEAL 2018 in Madrid, the 26th edition, IDEAL2025 will be taking place in olive capital and historic city of Jaén, Andalusia, Spain.

Based on the success of the special session on Explainability and Fairness in Decision Support on IDEAL 2024, this year we aim to start with a satellite workshop series on Intelligent Decision and Recommender Systems, associated to IDEAL conference, and focused on our topics of our former special session. This year our running topic will be: “Toward Fair, Transparent, and Human-Centered Decision Support, under the umbrella of Intelligent Data Engineering and Automated Learning”.

Explainability in decision support systems is essential for fostering trust and transparency in algorithmic/AI-driven decision processes. By making complex models understandable, users can see the rationale behind decisions, ensuring accountability and facilitating better-informed choices. This is particularly crucial in high-stakes areas like healthcare, finance, and criminal justice, where the consequences of decisions can significantly impact individuals and society. Explainability also aids in identifying and mitigating biases within models, promoting fairness and ethical AI use. Overall, enhancing explainability helps bridge the gap between advanced technology and human users, ensuring that decision support systems are both effective and reliable.

In a different direction, fairness in decision support systems ensures that algorithmic/AI-driven decisions do not perpetuate or amplify biases, promoting equality and justice. It involves developing and implementing algorithms that treat all individuals and groups equitably, especially those from historically marginalized communities. Ensuring fairness is crucial in sectors like hiring, lending, and law enforcement, where biased decisions can have severe social and economic consequences. Addressing fairness involves continuous monitoring, bias detection, and corrective measures, creating systems that not only perform well but also uphold ethical standards and societal values, fostering trust and legitimacy in automated decision-making processes.

The aim of the current session is to promote the exchange between different researchers working on these topics, over the view of the intelligent data engineering framework. Core themes or topics include, but are not limited to:

-Novel approaches and methodologies in recommender systems and intelligent decision making.
-Innovative algorithms and techniques for boosting transparency and explainability in decision support systems.
-New approaches for characterizing and improving fairness in decision support.
-Human-in-the-loop approaches for explainability and fairness.
-Explainability and fairness in recommender systems.
-Human-centered explainable artificial intelligence: approaches, methodologies, and evaluation.
-Real-world applications.

The workshop is supported by the Spanish Tematic Network of Recommender Systems Research (ELIGE-IA) (https://www.esi.uclm.es/elige-ia/)


Submission

See submission instructions for the conference at the general call for papers (https://ideal2025.ujaen.es/1668-2/). At the beginning of the submission, please choose the track "Workshop on Intelligent Decision and Recommender Systems (WIDRS)".

Submission link: https://easychair.org/conferences/?conf=ideal2025

The papers accepted in the workshop will be published by Springer in the LNCS/LNAI Proceedings series, as part of IDEAL 2025 proceedings.

Contact:
Luis Martínez
University of Jaén
martin@ujaen.es

Bapi Dutta
University of Jaén
bdutta@ujaen.es

Raciel Yera
University of Jaén
ryera@ujaen.es