Papers by Piyush Joshi
When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech (2026.eacl-short)
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| Challenge: | Existing methods for hate speech detection treat hate speech as a monolithic phenomenon, ignoring the speaker’s motivations and potential societal consequences. |
| Approach: | They propose a dataset with a dual taxonomy that separates Intent (why the speaker produced hate speech) and Impact (what harm it may cause to individuals and communities) they propose to use this data to enable content moderation and user safety. |
| Outcome: | The proposed dataset captures Intent (why the speaker produced hate speech) and Impact (what harm it may cause to individuals and communities) of online hateful posts. |
Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble (2024.emnlp-industry)
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| Challenge: | Increasing use of large language models (LLMs) require performant guardrails to ensure the safety of inputs and outputs . when these guardrail are trained on imbalanced data, they can learn the societal biases resulting from the model's performance. |
| Approach: | They propose a method for mitigating counterfactual fairness in closed-source text safety classifiers by using a debiasing regularizer and a threshold-agnostic metric. |
| Outcome: | The proposed method outperforms classifiers and acts as a debiasing regularizer . it uses threshold-agnostic metrics and Fair Data Reweighting (FDW) to assess the counterfactual fairness of a model . |