@article{jeyasothy2023,

title = {A general framework for personalising post hoc explanations through user knowledge integration},

author = {Adulam Jeyasothy and Thibault Laugel and Marie-Jeanne Lesot and Christophe Marsala and Marcin Detyniecki},

doi = {https://doi.org/10.1016/j.ijar.2023.108944},

year  = {2023},

date = {2023-09-01},

urldate = {2023-09-01},

journal = {International Journal of Approximate Reasoning},

volume = {160},

keywords = {Explainability & Interpretability},

pubstate = {published},

tppubtype = {article}

}

@article{rychener2020sentence,

title = {On the Granularity of Explanations in Model Agnostic NLP Interpretability},

author = {Yves Rychener and Xavier Renard and Djamé Seddah and Pascal Frossard and Marcin Detyniecki},

url = {https://arxiv.org/abs/2012.13189},

year  = {2022},

date = {2022-09-19},

urldate = {2020-01-01},

journal = {2022 ECML-PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD)},

abstract = {Current methods for Black-Box NLP interpretability, like LIME or SHAP, are based on altering the text to interpret by removing words and modeling the Black-Box response. In this paper, we outline limitations of this approach when using complex BERT-based classifiers: The word-based sampling produces texts that are out-of-distribution for the classifier and further gives rise to a high-dimensional search space, which can't be sufficiently explored when time or computation power is limited. Both of these challenges can be addressed by using segments as elementary building blocks for NLP interpretability. As illustration, we show that the simple choice of sentences greatly improves on both of these challenges. As a consequence, the resulting explainer attains much better fidelity on a benchmark classification task.},

keywords = {Explainability & Interpretability},

pubstate = {published},

tppubtype = {article}

}

@article{grari:hal-03923289,

title = {Adversarial learning for counterfactual fairness},

author = {Vincent Grari and Sylvain Lamprier and Marcin Detyniecki},

url = {https://hal.sorbonne-universite.fr/hal-03923289},

doi = {10.1007/s10994-022-06206-8},

year  = {2022},

date = {2022-05-01},

urldate = {2022-05-01},

journal = {Machine Learning},

publisher = {Springer Verlag},

keywords = {Fairness},

pubstate = {published},

tppubtype = {article}

}

@article{laugel2022,

title = {Explaining Local Discrepancies between Image Classification Models},

author = {Thibault Laugel and Xavier Renard and Marcin Detyniecki},

year  = {2022},

date = {2022-01-01},

journal = {CVPR Explainable AI for Computer Vision Workshop (XAI4CV 2022)},

keywords = {Explainability & Interpretability},

pubstate = {published},

tppubtype = {article}

}

@article{bove2022,

title = {Contextualising local explanations for 

non-expert users: an XAI pricing interface for 

insurance},

author = {Clara Bove and Jonathan Aigrain and Marie-Jeanne Lesot and Charles Tijus and Marcin Detyniecki},

year  = {2022},

date = {2022-01-01},

journal = {Proceedings of the International Conference of Intelligent User Interface, IUI 2022},

keywords = {Human-Computer Interface & HCXAI},

pubstate = {published},

tppubtype = {article}

}

@article{vincent2022fair,

title = {A fair pricing model via adversarial learning},

author = {Grari Vincent and Charpentier Arthur and Lamprier Sylvain and Detyniecki Marcin},

year  = {2022},

date = {2022-01-01},

journal = {arXiv preprint arXiv:2202.12008},

keywords = {Fairness},

pubstate = {published},

tppubtype = {article}

}

@article{bove2021contextualization,

title = {Contextualization and Exploration of Local Feature Importance Explanations to Improve Understanding and Satisfaction of Non-Expert Users},

author = {Clara Bove and Jonathan Aigrain and Marie-Jeanne Lesot and Charles Tijus and Marcin Detyniecki},

year  = {2021},

date = {2021-01-01},

journal = {IUI 2021 Workshop Transparent and Explainable Smart System (TExSS)},

keywords = {Human-Computer Interface & HCXAI},

pubstate = {published},

tppubtype = {article}

}

@article{bove2021trust,

title = {Building Trust in Artificial Conversational 

Agents},

author = {Clara Bove and Jonathan Aigrain and Marcin Detyniecki},

year  = {2021},

date = {2021-01-01},

journal = {IUI 2021 Workshop on Conversational User Interface (CUI)},

keywords = {Human-Computer Interface & HCXAI},

pubstate = {published},

tppubtype = {article}

}

@article{ruf2021implementing,

title = {Implementing Fair Regression In The Real World},

author = {Boris Ruf and Marcin Detyniecki},

year  = {2021},

date = {2021-01-01},

journal = {arXiv preprint arXiv:2104.04353},

keywords = {Fairness},

pubstate = {published},

tppubtype = {article}

}

@article{ruf2021towards,

title = {Towards the right kind of fairness in AI},

author = {Boris Ruf and Marcin Detyniecki},

year  = {2021},

date = {2021-01-01},

journal = {arXiv preprint arXiv:2102.08453},

keywords = {Fairness},

pubstate = {published},

tppubtype = {article}

}

@article{ruf2021explaining,

title = {Explaining how your AI system is fair},

author = {Boris Ruf and Marcin Detyniecki},

year  = {2021},

date = {2021-01-01},

journal = {arXiv preprint arXiv:2105.00667},

keywords = {Fairness},

pubstate = {published},

tppubtype = {article}

}