Dark Patterns of Explainability, Transparency, and User Control for Intelligent Systems. - Michael Chromik; Malin Eiband; Sarah Theres Völkel; Daniel Buschek (IUI Workshops, 2019)

reSHAPe: A Framework for Interactive Explanations in XAI Based on SHAP. - Michael Chromik (ECSCW, 2020)

Mind the (persuasion) gap: contrasting predictions of intelligent DSS with user beliefs to improve interpretability. - Michael Chromik; Florian Fincke; Andreas Butz (EICS, 2020)

A Taxonomy for Human Subject Evaluation of Black-Box Explanations in XAI. - Michael Chromik; Martin Schuessler (ExSS-ATEC@IUI, 2020)

ML for UX? - An Inventory and Predictions on the Use of Machine Learning Techniques for UX Research. - Michael Chromik; Florian Lachner; Andreas Butz (NordiCHI, 2020)

Making SHAP Rap: Bridging Local and Global Insights Through Interaction and Narratives. - Michael Chromik (INTERACT (2), 2021)

Human-XAI Interaction: A Review and Design Principles for Explanation User Interfaces. - Michael Chromik; Andreas Butz (INTERACT (2), 2021)

I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI. - Michael Chromik; Malin Eiband; Felicitas Buchner; Adrian Krüger; Andreas Butz (IUI, 2021)

Human-centric explanation facilities: explainable AI for the pragmatic understanding of non-expert end users. - Michael Chromik (2021)