Papers by Chad Atalla

3 papers
FairPrism: Evaluating Fairness-Related Harms in Text Generation (2023.acl-long)

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Challenge: FairPrism dataset provides a framework for measuring and mitigating fairness-related harms caused by AI text generation systems.
Approach: They propose a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering a diverse set of harms relating to gender and sexuality.
Outcome: FairPrism is a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering harms relating to gender and sexuality.
When does text prediction benefit from additional context? An exploration of contextual signals for chat and email messages (2021.naacl-industry)

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Challenge: Prior-message context provides the greatest lift in Teams (chat) scenario.
Approach: They compare prior-message context with email and chat messages from Microsoft Teams and Outlook.
Outcome: The proposed model outperforms existing models on two of the largest commercial communication platforms: Microsoft Teams and Outlook.
Taxonomizing Representational Harms using Speech Act Theory (2025.findings-acl)

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Challenge: a theoretical framework defines representational harms as perlocutionary effects of illocutional acts . the framework provides a granular taxonomy of ils that cause representational damages .
Approach: They propose a framework that defines representational harms as perlocutionary effects of system behaviors . they propose illocutional acts that cause representational damage and a taxonomy that supports measurement instruments .
Outcome: The proposed framework defines representational harms as perlocutionary effects of illocutionaries . it can support the development of valid measurement instruments, the authors show .

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