![[2411.09847] Towards a Fairer Non-negative Matrix Factorization](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2411.09847] Towards a Fairer Non-negative Matrix Factorization
Summary
This article presents a novel approach to Non-negative Matrix Factorization (NMF) aimed at improving fairness in machine learning algorithms by modifying the objective function with a min-max formulation.
Why It Matters
As fairness in machine learning becomes increasingly critical, this research addresses the need for practical bias mitigation strategies. By exploring NMF, a widely used method in topic modeling and feature extraction, the authors contribute to the ongoing discourse on balancing fairness and accuracy in algorithmic decision-making.
Key Takeaways
- Introduces a min-max formulation to enhance fairness in NMF.
- Demonstrates that fairness improvements may come at the cost of increased error for some individuals.
- Emphasizes the importance of context in defining fairness and selecting methods.
Computer Science > Machine Learning arXiv:2411.09847 (cs) [Submitted on 14 Nov 2024 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Towards a Fairer Non-negative Matrix Factorization Authors:Lara Kassab, Erin George, Deanna Needell, Haowen Geng, Nika Jafar Nia, Aoxi Li View a PDF of the paper titled Towards a Fairer Non-negative Matrix Factorization, by Lara Kassab and 5 other authors View PDF HTML (experimental) Abstract:There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical and approachable to the practitioner. Motivated by recent work on ``fair" PCA, here we consider the more challenging method of non-negative matrix factorization (NMF) as both a showcasing example and a method that is important in its own right for both topic modeling tasks and feature extraction for other ML tasks. We demonstrate that a modification of the objective function, by using a min-max formulation, may \textit{sometimes} be able to offer an improvement in fairness for groups in the population. We derive two methods for the objective minimization, a multiplicative update rule as well as an alternating minimization scheme, and discuss implementation practicalities. We include a suite of synthetic and real experiments that show how the method may improve fairness while also highlighting the...
