Papers with out-of-distribution fairness
Features or Spurious Artifacts? Data-centric Baselines for Fair and Robust Hate Speech Detection (2022.naacl-main)
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| Challenge: | lexical biases in hate speech detection are limited when applied to real-world data, exhibiting limited out-of-distribution robustness and perpetuating harmful social biase. |
| Approach: | They propose to disentangle spurious and authentic artifacts and analyze their impact on out-of-distribution fairness and robustness. |
| Outcome: | The proposed models show that spurious artifacts require different treatments to attain robustness and fairness in hate speech detection. |