44 citations · 113 across the 13 of their papers we have counts for
13 papers
Information Retrieval for Climate Impact
Maarten de Rijke, Bart van den Hurk, Flora Salim +28
The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and di…
Generalized Open-World Semi-Supervised Object Detection
Garvita Allabadi, Ana Lucic, Siddarth Aananth +3
Traditional semi-supervised object detection methods assume a fixed set of object classes (in-distribution or ID classes) during training and deployment, which limits performance i…
Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy
Ana Lucic
Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users u…
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa +7
When using medical images for diagnosis, either by clinicians or artificial intelligence (AI) systems, it is important that the images are of high quality. When an image is of low…
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language Processing
Michael Neely, Stefan F. Schouten, Maurits Bleeker +1
There has been significant debate in the NLP community about whether or not attention weights can be used as an explanation - a mechanism for interpreting how important each input…
Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence
Ana Lucic, Maurits Bleeker, Sami Jullien +2
In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the…
Order in the Court: Explainable AI Methods Prone to Disagreement
Michael Neely, Stefan F. Schouten, Maurits J. R. Bleeker +1
By computing the rank correlation between attention weights and feature-additive explanation methods, previous analyses either invalidate or support the role of attention-based exp…
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
Kim de Bie, Ana Lucic, Hinda Haned
In hybrid human-AI systems, users need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown. To accommodate such settings,…
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
Ana Lucic, Madhulika Srikumar, Umang Bhatt +4
Given that there are a variety of stakeholders involved in, and affected by, decisions from machine learning (ML) models, it is important to consider that different stakeholders ha…
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei +2
Given the increasing promise of graph neural networks (GNNs) in real-world applications, several methods have been developed for explaining their predictions. Existing methods for…
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
Ana Lucic, Harrie Oosterhuis, Hinda Haned +1
Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans. Counterfactual explanations c…
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
Ana Lucic, Hinda Haned, Maarten de Rijke
In various business settings, there is an interest in using more complex machine learning techniques for sales forecasting. It is difficult to convince analysts, along with their s…
Explaining Predictions from Tree-based Boosting Ensembles
Ana Lucic, Hinda Haned, Maarten de Rijke
Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community. Our work focuses on doing this by g…