This book constitutes the refereed proceedings of the 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022, held in Vienna, Austria during August 2022.The 23 full papers presented were carefully reviewed and selected from 45 submissions.

Explain to Not Forget: Defending Catastrophic Forgetting with XAI.- Approximation of SHAP values for Randomized Tree Ensembles.- Color shadows (part I): exploratory usability evaluation of activation maps in radiological machine learning.- Effects of Fairness and Explanation on Trust in Ethical AI.- Towards Refined Classifications driven by SHAP explanations.- Global Intepretable Calibration Index, a New Metric to Estimate Machine Learning Models' Calibration.- The ROC Diagonal is not Layperson’s Chance: a New Baseline Shows the Useful Area.- Debiasing MDI Feature Importance and SHAP values in Tree Ensembles.- The Influence of User Diversity on Motives and Barriers when Using Health Apps - A Conjoint Investigation of the Intention-Behavior Gap.- Identifying Fraud Rings Using Domain Aware Weighted Community Detection.- Capabilities, limitations and challenges of style transfer with CycleGANs: a study on automatic ring design generation.- Semantic Causal Abstraction for Event Prediction.- An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments.- Towards Generating Financial Reports From Tabular Data Using Transformers.- Evaluating the performance of SOBEK text mining keyword extraction algorithm.- Classification of Screenshot Image Captured in Online Meeting System.- A survey on the application of virtual reality in event-related potential research.- Visualizing Large Collections of URLs Using the Hilbert Curve.- How to Reduce the Time Necessary for Evaluation of Tree-based Models.- An Empirical Analysis of and Guidelines for Synthetic-Data-based Anomaly Detection.- SECI Model in Data-Based Procedure for the Assessment of the Frailty State in Diabetic Patients.- Comparing machine learning correlations to domain experts’ causal knowledge: Employee turnover use case.- Machine learning and knowledge extraction to support work safety for smart forest operations.