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02/08/2023
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten
The Right to Explanation and the Right to be Forgotten are two important...
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07/08/2022
TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues
Machine Learning (ML) models are increasingly used to make critical deci...
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06/22/2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations
While several types of post hoc explanation methods (e.g., feature attri...
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03/16/2022
Measuring Fairness of Text Classifiers via Prediction Sensitivity
With the rapid growth in language processing applications, fairness has ...
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03/14/2022
Rethinking Stability for Attribution-based Explanations
As attribution-based explanation methods are increasingly used to establ...
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02/03/2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
As various post hoc explanation methods are increasingly being leveraged...
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10/27/2021
Towards Realistic Single-Task Continuous Learning Research for NER
There is an increasing interest in continuous learning (CL), as data pri...
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06/21/2021
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
Existing bias mitigation methods to reduce disparities in model outcomes...
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06/03/2021
Grounding Complex Navigational Instructions Using Scene Graphs
Training a reinforcement learning agent to carry out natural language in...
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01/29/2021
ADePT: Auto-encoder based Differentially Private Text Transformation
Privacy is an important concern when building statistical models on data...
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01/27/2021
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation
Recent advances in deep learning techniques have enabled machines to gen...
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05/16/2020
Towards classification parity across cohorts
Recently, there has been a lot of interest in ensuring algorithmic fairn...
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10/25/2019
