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07/28/2023
Semi-Supervised Object Detection in the Open World
Existing approaches for semi-supervised object detection assume a fixed ...
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09/12/2022
Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy
Model explainability has become an important problem in machine learning...
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07/06/2022
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
When using medical images for diagnosis, either by clinicians or artific...
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11/01/2021
Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence through the Lens of Reproducibility
In this work we explain the setup for a technical, graduate-level course...
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05/07/2021
Order in the Court: Explainable AI Methods Prone to Disagreement
In Natural Language Processing, feature-additive explanation methods qua...
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04/14/2021
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
In hybrid human-AI systems, users need to decide whether or not to trust...
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03/27/2021
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
Given that there are a variety of stakeholders involved in, and affected...
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02/05/2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
Graph neural networks (GNNs) have shown increasing promise in real-world...
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11/27/2019
Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles
Counterfactual explanations help users understand why machine learned mo...
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07/17/2019
Contrastive Explanations for Large Errors in Retail Forecasting Predictions through Monte Carlo Simulations
At Ahold Delhaize, there is an interest in using more complex machine le...
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07/04/2019