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arxiv:2409.05990

FairHome: A Fair Housing and Fair Lending Dataset

Published on Sep 9, 2024
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Abstract

A dataset labeled for compliance risk in the housing ___domain is used to train a classifier that outperforms state-of-the-art LLMs in detecting potential violations in real-estate transactions.

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.

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