You Shouldn't Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods. - Botty Dimanov; Umang Bhatt; Mateja Jamnik; Adrian Weller (SafeAI@AAAI, 2020)

Now You See Me (CME): Concept-based Model Extraction. - Dmitry Kazhdan; Botty Dimanov; Mateja Jamnik; Pietro Liò; Adrian Weller (CIKM (Workshops), 2020)

MEME: Generating RNN Model Explanations via Model Extraction. - Dmitry Kazhdan; Botty Dimanov; Mateja Jamnik; Pietro Liò (CoRR, 2020)

Interpretable Deep Learning: beyond feature-importance with concept-based explanations. - Botty Dimanov (2020)

Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches. - Dmitry Kazhdan; Botty Dimanov; Helena Andrés-Terré; Mateja Jamnik; Pietro Liò; Adrian Weller (CoRR, 2021)

Failing Conceptually: Concept-Based Explanations of Dataset Shift. - Maleakhi A. Wijaya; Dmitry Kazhdan; Botty Dimanov; Mateja Jamnik (CoRR, 2021)

Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations. - Shea Cardozo; Gabriel Islas Montero; Dmitry Kazhdan; Botty Dimanov; Maleakhi A. Wijaya; Mateja Jamnik; Pietro Liò (CIKM Workshops, 2022)

GCI: A (G)raph (C)oncept (I)nterpretation Framework. - Dmitry Kazhdan; Botty Dimanov; Lucie Charlotte Magister; Pietro Barbiero; Mateja Jamnik; Pietro Liò (CoRR, 2023)