Patrick Altmeyer, Mojtaba Farmanbar, Arie van Deursen, Cynthia C.S. Liem (2024), Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals, In Proceedings of the AAAI Conference on Artificial Intelligence p.10829-10837.

Stevan Rudinac, Alan Hanjalic, Cynthia Liem, Marcel Worring, Björn Þór Jónsson, Bei Liu, Yoko Yamakata (2024), Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 14555 LNCS p.v.

Stevan Rudinac, Alan Hanjalic, Cynthia Liem, Marcel Worring, Björn Þór Jónsson, Bei Liu, Yoko Yamakata (2024), Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 14554 LNCS.

Stevan Rudinac, Alan Hanjalic, Cynthia Liem, Marcel Worring, Björn Þór Jónsson, Bei Liu, Yoko Yamakata (2024), Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 14556 LNCS.

Priya Sarkar, Cynthia C.S. Liem (2024), “It's the most fair thing to do, but it doesn't make any sense”: Perceptions of Mathematical Fairness Notions by Hiring Professionals, In Proceedings of the ACM on Human-Computer Interaction Volume 8.

Andra Georgiana Sav, Andrew M. Demetriou, Cynthia C.S. Liem (2023), Annotation Practices in Societally Impactful Machine Learning Applications: What are Popular Recommender Systems Models Actually Trained On?, In CEUR Workshop Proceedings.

P. Altmeyer, G.J.A. Angela, A.J. Buszydlik, K.T. Dobiczek, A. van Deursen, C.C.S. Liem (2023), Endogenous Macrodynamics in Algorithic Recourse [VIDEO].

Patrick Altmeyer, Angela Giovan, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, Cynthia C. S. Liem (2023), Endogenous Macrodynamics in Algorithmic Recourse, Cristina Ceballos (Eds.), In Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) p.418-431, IEEE.

P. Altmeyer, C.C.S. Liem, A. van Deursen (2023), Explaining Black-Box Models through Counterfactuals, In The Proceedings of the JuliaCon Conferences (JCON).

Antony Bartlett, Cynthia C.S. Liem, Annibale Panichella (2023), On the Strengths of Pure Evolutionary Algorithms in Generating Adversarial Examples, In Proceedings of the 2023 IEEE/ACM International Workshop on Search-Based and Fuzz Testing (SBFT) p.1-8, IEEE.