Usebias

Usebias is an open-source software library designed for analyzing and mitigating biases in machine learning models and datasets. It aims to promote fairness and transparency in artificial intelligence applications by providing tools to evaluate, detect, and reduce biases related to sensitive attributes such as gender, race, age, and socioeconomic status.

The library offers a collection of algorithms and metrics that enable researchers and practitioners to assess bias levels in datasets and model predictions. It supports various fairness measures, including demographic parity, equalized odds, and disparate impact, among others. Usebias also provides techniques for bias mitigation, such as pre-processing data balancing, in-processing algorithms to adjust model training, and post-processing methods to correct outputs.

Usebias is built to be compatible with commonly used machine learning frameworks like scikit-learn, facilitating integration into existing workflows. Its user-friendly interface allows users to perform bias assessment easily and implement mitigation strategies without extensive expertise in fairness auditing.

The project is actively maintained by a community of contributors committed to advancing ethical AI practices. Documentation and tutorials are available to assist users in applying the library’s functionalities effectively. By promoting rigorous bias analysis and mitigation, Usebias contributes to the development of more equitable AI systems, fostering trust and accountability in artificial intelligence deployments.

While primarily aimed at researchers and data scientists, Usebias also serves policymakers and educators interested in the societal impacts of AI and methods to ensure fair treatment across diverse populations.