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praxai:start [2022/03/10 09:05] – [Practical applications of explainable artificial intelligence methods (PRAXAI)] sbk | praxai:start [2023/04/21 09:18] – [Program Committee (tentative)] sbk | ||
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**PRAXAI webpage address is [[http:// | **PRAXAI webpage address is [[http:// | ||
- | PRAXAI | + | PRAXAI |
Conference on Data Science and Advanced Analytics]] focuses on bringing the research on Explainable | Conference on Data Science and Advanced Analytics]] focuses on bringing the research on Explainable | ||
Artificial Intelligence (XAI) to actual applications and tools that help | Artificial Intelligence (XAI) to actual applications and tools that help | ||
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daily work. | daily work. | ||
- | The PRAXAI | + | The PRAXAI |
===== Important Dates ===== | ===== Important Dates ===== | ||
- | * **Submission Deadline**: | + | * **Submission Deadline**: |
- | * **Notification**: | + | * **Notification**: |
- | * **Camera Ready Due**: | + | * **Camera Ready Due**: August |
+ | * **Conference date**: October 9-13 | ||
===== Call for papers ===== | ===== Call for papers ===== | ||
- | * TBA | + | * {{ : |
+ | |||
===== Submission Instructions ===== | ===== Submission Instructions ===== | ||
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Authors are also encouraged to submit supplementary materials, i.e., providing the source code and data through a GitHub-like public repository to support the reproducibility of their research results. | Authors are also encouraged to submit supplementary materials, i.e., providing the source code and data through a GitHub-like public repository to support the reproducibility of their research results. | ||
- | Electronic submission site: https://cmt3.research.microsoft.com/DSAA2022 | + | Electronic submission site: [[https://easychair.org/my/conference? |
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Explainable Artificial Intelligence (XAI) has become an inherent component of data mining (DM) and machine learning (ML) pipelines in the areas where the insight into the decision process of an automated system is important. | Explainable Artificial Intelligence (XAI) has become an inherent component of data mining (DM) and machine learning (ML) pipelines in the areas where the insight into the decision process of an automated system is important. | ||
+ | |||
Although explainability (or intelligibility) is not a new concept in AI, it has been most extensively developed over the last decade focusing mostly on explaining black-box models. Many successful frameworks were developed such as LIME, SHAP, LORE, Anchor, GradCam, DeepLift and others that aim at providing explanations and transparency to decisions made by machine learning models. | Although explainability (or intelligibility) is not a new concept in AI, it has been most extensively developed over the last decade focusing mostly on explaining black-box models. Many successful frameworks were developed such as LIME, SHAP, LORE, Anchor, GradCam, DeepLift and others that aim at providing explanations and transparency to decisions made by machine learning models. | ||
+ | |||
However, artificial intelligence systems in real-life applications are rarely composed of a single machine learning model, but rather are formed by a number of components orchestrated to work together for solving selected goals. Similarly, explainability | However, artificial intelligence systems in real-life applications are rarely composed of a single machine learning model, but rather are formed by a number of components orchestrated to work together for solving selected goals. Similarly, explainability | ||
Thus, the goal of the XAI methods is not simply to provide an explanation of a decision made by a ML model, but use this explanation to achieve goals that are related to the primary goal of a system as a whole by improving its transparency, | Thus, the goal of the XAI methods is not simply to provide an explanation of a decision made by a ML model, but use this explanation to achieve goals that are related to the primary goal of a system as a whole by improving its transparency, | ||
+ | |||
Therefore, in this special session we focus on works that apply different paradigms of XAI as a means of solving particular problems in many different | Therefore, in this special session we focus on works that apply different paradigms of XAI as a means of solving particular problems in many different | ||
+ | |||
We also focus on application of XAI methods in the machine learning/ | We also focus on application of XAI methods in the machine learning/ | ||
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===== Topics of interest ===== | ===== Topics of interest ===== | ||
+ | * Industry 4.0/5.0 and XAI | ||
* Model explanations verbalized in human-comprehensible natural language | * Model explanations verbalized in human-comprehensible natural language | ||
* Explainable Reinforcement learning | * Explainable Reinforcement learning | ||
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* Visualization of model explanations for different types of data apart from language and images (tabular data, time series, graphs, etc.) | * Visualization of model explanations for different types of data apart from language and images (tabular data, time series, graphs, etc.) | ||
* XAI software development and its integration into popular ML/DL libraries | * XAI software development and its integration into popular ML/DL libraries | ||
+ | * Fairness and XAI | ||
+ | * Ethics and XAI | ||
+ | * Trust in XAI systems | ||
+ | * XAI in real-world applications: | ||
+ | |||
===== Program Committee (tentative) ===== | ===== Program Committee (tentative) ===== | ||
+ | * Javier del Ser, Tecnalia | ||
+ | * Eneko Osaba, Tecnalia | ||
+ | * Ricardo Aler, Universidad Carlos III de Madrid | ||
+ | * Felix José Fuentes Hurtado, | ||
+ | * Alejandro Martin, Universidad Politécnica de Madrid, Spain | ||
+ | * Angel Panizo, Universidad Politécnica de Madrid, Spain | ||
+ | * Javier Huertas, Universidad Politécnica de Madrid, Spain | ||
+ | * Juan Pavón, Universidad Complutense de Madrid | ||
+ | * Francesco Piccialli, | ||
+ | * Salvatore Cuomo, | ||
+ | * Edoardo Prezioso, | ||
+ | * Federico Gatta, | ||
+ | * Fabio Giampaolo, | ||
+ | * Stefano Izzo, | ||
+ | * Martin Atzmueller, Universitat Osnabruck | ||
+ | * Kacper Sokół, University of Bristol | ||
+ | * Sławomir Nowaczyk, Halmstad University | ||
+ | * Michal Choras, UTP University of Science and Technology | ||
+ | * Bogusław Cyganek, AGH University of Science and Technology in Krakow | ||
+ | * Timos Kipouros, University of Cambridge | ||
+ | * Jerzy Stefanowski, | ||
+ | * Hubert Baniecki, Warsaw University, Poland | ||
+ | * Holzinger Andreas, Vienna University, Austria | ||
+ | * Bastian Pfeifer, Medical University of Graz, Austria | ||
+ | * Mustafa Cavuş, Eskisehir Technical University, Turkey | ||
+ | * Giuseppe Casalicchio, | ||
+ | * Dawid Rymarczyk, Jagiellonian University, Poland | ||
+ | * Jacek Tabor, Jagiellonian University, Poland | ||
+ | * Bartosz Zieliński, Jagiellonian University, Poland | ||
+ | * Abraham Duarte, Universidad Rey Juan Carlos I de Madrid, Spain | ||
+ | * Sancho Salcedo Sanz, Universidad de Alcalá de Henares, Madrid, Spain | ||
+ | * Benslimane Djamal, Lyon 1 University, France | ||
+ | * Hujun Yin, University of Manchester, UK | ||
+ | * Boyan Xu, Guangdong University of Technology, China | ||
+ | * Cesar Analide, Universidad do Minho, Portugal | ||
+ | * Maria Alcina Alpoim Sousa Pereira, Universidad do Minho, Portugal | ||
+ | * Valery Naranjo, Universidad Politécnica de Valencia, Spain | ||
+ | * Adrián Colomer, Universidad Politécnica de Valencia, Spain | ||
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+ | |||
+ | ===== Past events ===== | ||
+ | * [[praxai: | ||
+ | * [[praxai: | ||