Explaining Predictions from Tree-based Boosting Ensembles. - Ana Lucic; Hinda Haned; Maarten de Rijke (CoRR, 2019)

Global Aggregations of Local Explanations for Black Box models. - Ilse van der Linden; Hinda Haned; Evangelos Kanoulas (CoRR, 2019)

Contrastive Explanations for Large Errors in Retail Forecasting Predictions through Monte Carlo Simulations. - Ana Lucic; Hinda Haned; Maarten de Rijke (CoRR, 2019)

Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles. - Ana Lucic; Harrie Oosterhuis; Hinda Haned; Maarten de Rijke (CoRR, 2019)

FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. - Alexandra Olteanu; Jean Garcia-Gathright; Maarten de Rijke; Michael D. Ekstrand; Adam Roegiest; Aldo Lipani; Alex Beutel; Ana Lucic; Ana-Andreea Stoica; Anubrata Das 001; Asia Biega; Bart Voorn; et al. (SIGIR Forum, 2019)

Why does my model fail?: contrastive local explanations for retail forecasting. - Ana Lucic; Hinda Haned; Maarten de Rijke (FAT*, 2020)

Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing. - Phillip Lippe; Pengjie Ren; Hinda Haned; Bart Voorn; Maarten de Rijke (CoRR, 2020)

To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions. - Kim de Bie; Ana Lucic; Hinda Haned (CoRR, 2021)

FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles. - Ana Lucic; Harrie Oosterhuis; Hinda Haned; Maarten de Rijke (AAAI, 2022)

Evaluating the Practicality of Counterfactual Explanations. - Nina Spreitzer; Hinda Haned; Ilse van der Linden (XAI.it@AI*IA, 2022)

PEBAM: A Profile-Based Evaluation Method for Bias Assessment on Mixed Datasets. - Mieke Wilms; Giovanni Sileno; Hinda Haned (KI, 2022)