ashudeep/ranking-fairness-uncertainty
Code for the NeurIPS 2021 paper: Fairness in Ranking under Uncertainty
This project helps optimize ranked lists of items, like movie recommendations or search results, when there's uncertainty about user preferences. It takes in item data with potential demographic attributes and outputs a ranking that balances overall utility with fairness across different groups. This is for data scientists or machine learning engineers who build and evaluate ranking systems and want to ensure equitable outcomes.
No commits in the last 6 months.
Use this if you are developing recommender systems or search engines and need to ensure fairness in rankings, especially when user preferences are not perfectly known.
Not ideal if you are looking for a plug-and-play solution for general data analysis or if your ranking problem does not involve uncertainty or fairness considerations.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Nov 09, 2021
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