Papers by Alexandra Chouldechova
Taxonomizing Representational Harms using Speech Act Theory (2025.findings-acl)
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Emily Corvi, Hannah Washington, Stefanie Reed, Chad Atalla, Alexandra Chouldechova, P. Alex Dow, Jean Garcia-Gathright, Nicholas J Pangakis, Emily Sheng, Dan Vann, Matthew Vogel, Hanna Wallach
| 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 . |
What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (N19-1)
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Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai
| Challenge: | Existing methods for mitigating bias in machine learning systems rely on access to protected attributes such as race, gender, or age. |
| Approach: | They propose a method for discouraging correlation between predicted probability of an individual’s true occupation and a word embedding of their name. |
| Outcome: | The proposed method reduces race and gender biases, with almost no reduction in the classifier’s overall true positive rate. |
Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems (2025.findings-acl)
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Emma Harvey, Emily Sheng, Su Lin Blodgett, Alexandra Chouldechova, Jean Garcia-Gathright, Alexandra Olteanu, Hanna Wallach
| Challenge: | Existing tools for measuring representational harms caused by large language model systems are not useful for practitioners. |
| Approach: | They examine the extent to which public instruments are used to measure representational harms caused by large language model-based systems. |
| Outcome: | The proposed instruments do not meet the needs of practitioners evaluating large language model-based systems. |