FairHome: A Fair Housing and Fair Lending Dataset
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909310593794048 |
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| author | Bagalkotkar, Anusha Karmakar, Aveek Arnson, Gabriel Linda, Ondrej |
| author_facet | Bagalkotkar, Anusha Karmakar, Aveek Arnson, Gabriel Linda, Ondrej |
| contents | We present a Fair Housing and Fair Lending dataset (FairHome): A dataset with around 75,000 examples across 9 protected categories. To the best of our knowledge, FairHome is the first publicly available dataset labeled with binary labels for compliance risk in the housing domain. We demonstrate the usefulness and effectiveness of such a dataset by training a classifier and using it to detect potential violations when using a large language model (LLM) in the context of real-estate transactions. We benchmark the trained classifier against state-of-the-art LLMs including GPT-3.5, GPT-4, LLaMA-3, and Mistral Large in both zero-shot and few-shot contexts. Our classifier outperformed with an F1-score of 0.91, underscoring the effectiveness of our dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FairHome: A Fair Housing and Fair Lending Dataset Bagalkotkar, Anusha Karmakar, Aveek Arnson, Gabriel Linda, Ondrej Machine Learning Computers and Society We present a Fair Housing and Fair Lending dataset (FairHome): A dataset with around 75,000 examples across 9 protected categories. To the best of our knowledge, FairHome is the first publicly available dataset labeled with binary labels for compliance risk in the housing domain. We demonstrate the usefulness and effectiveness of such a dataset by training a classifier and using it to detect potential violations when using a large language model (LLM) in the context of real-estate transactions. We benchmark the trained classifier against state-of-the-art LLMs including GPT-3.5, GPT-4, LLaMA-3, and Mistral Large in both zero-shot and few-shot contexts. Our classifier outperformed with an F1-score of 0.91, underscoring the effectiveness of our dataset. |
| title | FairHome: A Fair Housing and Fair Lending Dataset |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2409.05990 |