Papers by Eddie Ungless

2 papers
Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models (2023.findings-acl)

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Challenge: Initial studies have pointed to the potential for harm due to predictive bias, reflecting and potentially reinforcing cultural stereotypes.
Approach: They conduct a survey among non-cisgender individuals and interviews to establish which harms affected individuals anticipate, and how they would like to be represented.
Outcome: The results show that certain non-cisgender identities are consistently (mis)represented as less human, more stereotyped and more sexualised.
This prompt is measuring <mask>: evaluating bias evaluation in language models (2023.findings-acl)

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Challenge: a growing body of work uses prompts and templates to assess bias in language models . authors examine the scope of possible bias types and identify those under-researched .
Approach: They draw on a measurement modelling framework to create a bias taxonomy . they show that bias tests are often unstated or ambiguous, carry implicit assumptions .
Outcome: The proposed taxonomy shows that bias tests are often unstated or ambiguous . the analysis illuminates the scope of possible bias types the field can measure .

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