2nd Italian Workshop on Explainable Artificiale Intelligence XAI.it

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2021/12/01 01:00 Europe/Rome
2021/12/03 01:00 Europe/Rome
co-located with AI*IA 2021, Milan, Italy
2nd Italian Workshop on Explainable Artificiale Intelligence XAI.it

Artificial Intelligence systems are increasingly playing an increasingly important role in our daily lives. As their importance in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the recent General Data Protection Regu-lation (GDPR) emphasized the users' right to explanation when people face artificial intelligence-based technologies.
Unfortunately, the current research tends to go in the opposite direction, since most of the approaches try to maximize the effectiveness of the models (e.g., recommendation accuracy) at the expense of the explainability and the transparency.
The main research questions which arise from this scenario is straightforward: how can we deal with such a dichotomy between the need for effective adaptive systems and the right to transparency and interpretability?
Several research lines are triggered by this question: building transparent intelligent systems, analyzing the impact of opaque algorithms on final users, studying the role of explanation strategies, investigating how to provide users with more control in the behavior of intelligent systems. The workshop tries to address these research lines and aims to provide a forum for the Italian community to discuss problems, challenges and innovative approaches in the various sub-fields of AI.

Topics of interests include but are not limited to:

Explainable Artificial Intelligence
Interpretable and Transparent Machine Learning Models
Strategies to Explain Black Box Decision Systems
Designing new Explanation Styles
Evaluating Transparency and Interpretability of AI Systems
Technical Aspects of Algorithms for Explanation
Theoretical Aspects of Explanation and Interpretability
Ethics in Explainable AI
Argumentation Theory for Explainable AI
Natural Language Generation for Explainable AI
Human-Machine Interaction for Explainable AI
Fairness and Bias Auditing
Privacy-Preserving Explanations
Privacy by Design Approaches for Human Data
Monitoring and Understanding System Behavior
Successful Applications of Interpretable AI Systems