| Challenge: | Existing research shows that a deep learning model can predict aggression and loss in posts by focusing on stop words such as “a” or “on”. |
| Approach: | They developed an approach to interpret a deep learning model that often bases its predictions on stop words such as "a" or "on" to tackle bias, they annotated the rationales and built models that drastically reduce bias. |
| Outcome: | The proposed model can predict aggression and loss in posts by using stop words such as "a" or "on" the new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. |
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Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, Kathy McKeown
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| Challenge: | Large-scale, pretrained vision-language models are growing in popularity due to impressive performance on downstream tasks with minimal finetuning. |
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