Papers by Thomas Fisher
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models (2021.eacl-main)
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| Challenge: | Existing benchmarks do not probe professional bias as pronoun resolution may be obfuscated by cross-correlations from other manifestations of gender prejudice. |
| Approach: | They propose to use a skew and stereotype metrics to quantify and analyse the gender bias present in contextual language models when tackling the WinoBias pronoun resolution task. |
| Outcome: | The proposed methods reduce skew and stereotype relative to the unaugmented fine-tuned BERT model. |
Biased LLMs can Influence Political Decision-Making (2025.acl-long)
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Jillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson, Yejin Choi, Daniel W Fisher, Jennifer Pan, Yulia Tsvetkov, Katharina Reinecke
| Challenge: | Recent studies have found that biased LLMs can influence decisions in areas such as medical classifications and educational hiring. |
| Approach: | They conducted two interactive experiments on partisan bias in large language models while completing tasks with either a biased liberal, biased conservative, or unbiased control model. |
| Outcome: | The results show that prior knowledge of AI is weakly correlated with a reduction of the bias, suggesting that AI education can be crucial for mitigating bias effects. |
Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models (2020.emnlp-main)
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| Challenge: | a new method for resume classification reduces the time and labor needed to screen applications . the current method of screening applications involves reviewing individual resumes via string/regex matching . |
| Approach: | They propose to use transformer-based resume classification to reduce time and labor needed to screen applications. |
| Outcome: | The proposed models reduce time and labor needed to screen applications while improving the selection of suitable candidates. |