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XKDD 2020 : 2nd International Workshop on eXplainable Knowledge Discovery in Data Mining

Ghent, Belgium
Event Date: Sep 14, 2020 - Sep 14, 2020
Submission Deadline: Jun 09, 2020
Notification of Acceptance: Jul 07, 2020
Camera Ready Version Due : Jul 21, 2020

About

The purpose of XKDD, eXaplaining Knowledge Discovery in Data Mining, is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining and machine learning. The workshop will seek top-quality submissions addressing uncovered important issues related to ethical, explainable and transparent data mining and machine learning. Papers should present research results in any of the topics of interest for the workshop as well as application experiences, tools and promising preliminary ideas. XKDD asks for contributions from researchers, academia and industries, working on topics addressing these challenges primarily from a technical point of view, but also from a legal, ethical or sociological perspective. In the past decade, we have witnessed the increasing deployment of powerful automated decision-making systems in settings ranging from control of safety-critical systems to face detection on mobile phone cameras. Albeit remarkably powerful in solving complex tasks, these systems are typically completely obscure, i.e., they do not provide any mechanism to understand and explore their behavior and the reasons underlying the decisions taken.

Call For Paper

Topics of interest include, but are not limited to:

  • Explainable Artificial Intelligence
  • Interpretable Machine Learning
  • Transparent Data Mining
  • Explainability in Clustering Analysis
  • Technical Aspects of Algorithms for Explanation
  • Explaining Black Box Decision Systems
  • Adversarial Attack-based Models
  • Counterfactual and Prototype-based Explanations
  • Causal Discovery for Machine Learning Explanation
  • Fairness Checking
  • Fair Machine Learning
  • Explanation for Privacy Risk
  • Ethics Discovery for Explainable AI
  • Privacy-Preserving Explanations
  • Transparent Classification Approaches
  • Anonymity and Information Hiding Problems in Comprehensible Models
  • Case Study Analysis
  • Experiments on Simulated and Real Decision Systems
  • Monitoring and Understanding System Behavior
  • Privacy Risk Assessment
  • Privacy by Design Approaches for Human Data
  • Statistical Aspects, Bias Detection and Causal Inference
  • Explanation, Accountability and Liability from an Ethical and Legal Perspective
  • Benchmarking and measuring explanation
  • Visualization-based explanations
  • Iterative dialogue explanations

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