Summary of Fairhome: a Fair Housing and Fair Lending Dataset, by Anusha Bagalkotkar (1) et al.
FairHome: A Fair Housing and Fair Lending Dataset
by Anusha Bagalkotkar, Aveek Karmakar, Gabriel Arnson, Ondrej Linda
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce a unique dataset called FairHome, which contains approximately 75,000 examples across nine protected categories related to fair housing and lending practices. To date, FairHome is the first publicly available dataset with binary labels indicating compliance risk in the housing domain. The team demonstrates the value of such a dataset by training a classifier and utilizing it to identify potential violations when applying large language models (LLMs) in real-estate transactions. The trained classifier is benchmarked against state-of-the-art LLMs, including GPT-3.5, GPT-4, LLaMA-3, and Mistral Large, both in zero-shot and few-shot contexts. Notably, the developed classifier outperforms these models with an F1-score of 0.91, highlighting the effectiveness of the proposed dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a special dataset called FairHome that helps ensure fair housing and lending practices. It’s like a big collection of examples showing what’s right or wrong in this area. The team also shows how they can use this data to train a computer program to detect when someone might be breaking these rules. They tested their program against other strong programs and it did really well, which means the dataset is helping make sure things are fair. |
Keywords
» Artificial intelligence » F1 score » Few shot » Gpt » Llama » Zero shot
