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
Fair Machine Learning
Explanation for Privacy Risk
Ethics Discovery for Explainable AI
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
Iterative Dialogue Explanations
Explanatory Model Analysis
Human-Centered Artificial Intelligence
Submissions with a focus on fairness and with an interdisciplinary orientation are particularly welcome, e.g. works at the boundary between ML, AI, infovis, man-machine interfaces, psychology, etc. Research driven by application cases where interpretability matters are also of our interest, e.g., medical applications, decision systems in law and administrations, industry 4.0, etc.