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The 21st International Conference on Modeling Decisions for Artificial Intelligence 人にやさしい人工知能のための意思決定モデルの 構築に関する第 21 回国際会議 (MDAI 2024) Tokyo, 日本 2024年 8月 27-31日 http://www.mdai.cat/mdai2024 |
Submission deadline:
DEADLINE USB/ISBN: May 15th, 2024 |
| DS Track. | Data science and machine learning track. Data science is the science of data. Its goal is to explain processes and objects through the available data. Statistical and Machine learning provide tools for building data-driven models. They are used to make informed decisions and explanations, using the knowledge extracted from the data. Models need to be fair, transparent, explainable and avoid unnecessary disclosure of sensitive information. Original contributions on methods, models, and tools for data science are sought.
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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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| Sec Track. | Security track. The more data-driven systems are used, the more the security aspects of these systems need to be take into account. Not only that, security in general becomes a key issue in all kind of computer systems. Original contributions in the field of information security, with special emphasis on AI-related systems, 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. |
