Papers by Ana Valeria González
Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations (2021.findings-acl)
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| Challenge: | despite interest in explainable AI, there is increasing skepticism as to whether explanations are useful to end-users in downstream applications. |
| Approach: | They conduct user studies to measure whether explanations help users decide when to accept or reject an ODQA system's answer. |
| Outcome: | The proposed study shows that explanations outperform baselines across modalities but the best strategy varies with the modality. |
On the Interaction of Belief Bias and Explanations (2021.findings-acl)
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| Challenge: | Existing methods to evaluate explainability fail to account for belief biases affecting human performance . previous studies have shown that neural models can make confident predictions relying on artifacts . |
| Approach: | They propose to account for belief bias in explainability by using models of varying quality and adversarial examples. |
| Outcome: | The proposed methods show that results change when using models of varying quality and adversarial examples. |
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias (2020.emnlp-main)
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| Challenge: | English challenge datasets highlight gender-ambiguous occurrences of ‘doctor’ as male doctors, but they are not useful for other languages. |
| Approach: | They propose to build multi-task challenge datasets for detecting gender bias that lead to unambiguously wrong model predictions for languages with type B reflexivization. |
| Outcome: | The proposed dataset can detect gender bias in languages with type B reflexivization and spans four languages and four NLP tasks. |