MDAI 2023 : 20th International Conference on Modeling Decisions for Artificial Intelligence
Conference Series : Modeling Decisions for Artificial Intelligence
Call For Papers
The 20th International Conference on Modeling Decisions for Artificial Intelligence will be held in Umeo, Sweden on 19-22 June 2023.
In MDAI we are particularly interested in the different facets of decision processes in a broad sense. This includes model building and all kind of mathematical tools for data aggregation, information fusion, and decision making; tools to help decision in data science problems (including e.g., statistical and machine learning algorithms as well as data visualization tools); and algorithms for data privacy and transparency-aware methods so that data processing processes and decisions made from them are fair, transparent, explainable and avoid unnecessary disclosure of sensitive information.
The MDAI conference includes tracks on the topics of (i) data science, (ii) machine learning, (iii) data privacy, (iv) aggregation funcions, (v) human decision making, and (vi) graphs and (social) networks. The conference has been since 2004 a forum for researchers to discuss last results into these areas of research.
MDAI 2023 is the 20th MDAI conference. Previous conferences were celebrated in Barcelona (2004), Tsukuba (2005), Tarragona (2006), Kitakyushu (2007), Sabadell (2008), Awaji Island (2009), Perpinyà (2010), Changsha (2011), Girona (2012), Barcelona (2013), Tokyo (2014), Skövde (2015), Sant Julià de Lòria (2016), Kitakyushu (2017), Mallorca (2018), Milan (2019), cancelled due to COVID (2020), Umeå (2021), Sant Cugat (2022).
MDAI is rated as a CORE B conference by the Computing Research and Education Association of Australasia - CORE.
Proceedings with accepted papers are to be published in the LNAI/LNCS series (Springer-Verlag) and distributed at the conference, as done in previous conferences.
Besides, papers, that according to the evaluation of the referees, are not suitable for the LNAI but that have some merits will be published in a USB proceedings and scheduled in the MDAI program. Direct submission for the USB proceedings is also possible. We have a later deadline for direct submission for the USB proceedings.
LNAI Submission deadline: December 15th, 2022
LNAI Acceptance notification: March 1st, 2023
USB-only Submission deadline: April 31st, 2023
Final version of LNAI accepted papers: March 17th, 2023
USB Acceptance notification: May 20th, 2023
Early registration: March 15th, 2023
Conference: 19-22 June, 2023
Data science track. Data science is the science of data. Its goal is to explain processes and objects through the available data. The explanation is expected to be objective and suitable to make predictions. The ultimate goal of the explanations is to make informed decisions based on the knowledge extracted from the data. Original contributions on methods, models, and tools for data science are sought.
Machine learning track. Algorithms and methods building models that are fair, transparent, explainable and that avoid unnecessary disclosure of sensitive information.
Data privacy track. Privacy-preserving data mining, privacy enhancing technologies, abd statistical disclosure control provide tools to avoid disclosure, and/or have a good balance between disclosure risk and data utility and security. Original contributions on aspects related to data privacy are sought.
Aggregation functions. Functions to aggregate data appear in several contexts. They are used for decision making and information fusion. Data science and artificial intelligence systems need these functions to summarize information, improve data quality and help in decision processes. Original contributions on aggregation functions and their applications are sought.
Human decision making. Decision making is a pervasive problem in intelligent systems, and decisions are to be made in scenarios where uncertainty is common. Most mathematical models for decision making under risk and uncertainty provide optimal decisions under certain constraints. Experience and studies show that these rational decision making models diverge from the typical approach human use to make decisions.
Graphs and (social) networks track. Graphs are often a convenient way to represent data. Social networks is a paradigmatic case. Algorithms and functions to process graphs and to extract information and knowledge from them are of high relevance in data science. Original contributions on graph analysis are sought.