International Workshop on eXplainable Knowledge Discovery in Data Mining - Virtual

Virtual - September 13, 2021

Register here.

In the past decade, machine learning based decision systems have been widely used in a wide range of application domains, like for example credit score, insurance risk, and health monitoring, in which accuracy is of the utmost importance. Although the support of these systems has a big potential to improve the decision in different fields, their use may present ethical and legal risks, such as codifying biases, jeopardizing transparency and privacy, reducing accountability. Unfortunately, these risks arise in different applications and they are made even more serious and subtly by the opacity of recent decision support systems, which often are complex and their internal logic is usually inaccessible to humans.

Nowadays most of the Artificial Intelligence (AI) systems are based on Machine Learning algorithms. The relevance and need of ethics in AI is supported and highlighted by various initiatives arising from the researches to provide recommendations and guidelines in the direction of making AI-based decision systems explainable and compliant with legal and ethical issues. These include the EU's GDPR regulation which introduces, to some extent, a right for all individuals to obtain ``meaningful explanations of the logic involved'' when automated decision making takes place, the ``ACM Statement on Algorithmic Transparency and Accountability'', the Informatics Europe's ``European Recommendations on Machine-Learned Automated Decision Making'' and ``The ethics guidelines for trustworthy AI'' provided by the EU High-Level Expert Group on AI.

The challenge to design and develop trustworthy AI-based decision systems is still open and requires a joint effort across technical, legal, sociological and ethical domains.

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. XKDD is an event organized into two moments: a tutorial to introduce audience to the topic, and a workshop to discuss recent advances in the research field. The tutorial will provide a broad overview of the state of the art on the major applications for explainable and transparent approaches and their relationship with fairness and privacy. Moreover, it will present Python/R libraries that practically shows how explainability and fairness tasks can be addressed. The workshop will seek top-quality submissions addressing uncovered important issues related to ethical, fair, 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.

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
  • Explanatory Model Analysis
  • Human-Model Interfaces
  • Human-Centered Artificial Intelligence
  • Human-in-the-Loop Interactions
Monday, September 13, 2021 - 8:45am to 5:00pm