Improving users' mental model with attention‐directed counterfactual edits
نویسندگان
چکیده
In the domain of visual question answering (VQA), studies have shown improvement in users' mental model VQA system when they are exposed to examples how these systems answer certain image-question (IQ) pairs. this work, we show that showing controlled counterfactual IQ more effective at improving users as compared simply random examples. We compare a generative approach and retrieval-based use recent advances adversarial networks generate images by deleting inpainting regions interest image. then expose changes system's on those altered images. To select region for inpainting, experiment with using both human-annotated attention maps fully automatic method uses values. Finally, test user's asking them predict model's performance note an overall accuracy change explanations. While realistic retrieved counterfactuals obviously most model, can also be equally effective.
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ژورنال
عنوان ژورنال: Applied AI letters
سال: 2021
ISSN: ['2689-5595']
DOI: https://doi.org/10.1002/ail2.47