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The 20th International Conference on Modeling Decisions for Artificial Intelligence Modelització de Decisions per a la Intel·ligència Artificial Umeå, Suècia Juny 19-22, 2023 http://www.mdai.cat/mdai2023 |
Termini de submissió:
ISBN DEADLINE: April 30th, 2023 |
| DS Track. | 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.
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| ML Track. | Machine learning track. Algorithms and methods building models that are fair, transparent, explainable and that avoid unnecessary disclosure of sensitive information.
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| DP Track. | Data privacy track. Privacy-preserving data mining, privacy enhancing technologies, and 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.
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| AGOP Track. | 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.
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| DM Track. | 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.
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| GSN Track. | 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.
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| Publication: |
Proceedings will be published in the LNAI/LNCS series (Springer-Verlag), and distributed at the conference. 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 accompanying proceedings and scheduled in the MDAI program. Papers can be directly submitted for the accompanying proceedings.
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| Submission: | Information here. |
