@inproceedings{2d1a0be1dcc24a5bb174369f65deefc6,
title = "Responsible Model Selection with Virny and VirnyView",
abstract = "In this demonstration, we present a comprehensive software library for model auditing and responsible model selection, called Virny, along with an interactive tool called VirnyView. Our library is modular and extensible, it implements a rich set of performance and fairness metrics, including novel metrics that quantify and compare model stability and uncertainty, and enables performance analysis based on multiple sensitive attributes, and their intersections. The Virny library and the VirnyView tool are available at https://github.com/DataResponsibly/Virny and https://r-ai.co/VirnyView.",
keywords = "data-centric ai, fairness, model selection, robustness, stability",
author = "Denys Herasymuk and {Arif Khan}, Falaah and Julia Stoyanovich",
note = "Publisher Copyright: {\textcopyright} 2024 Owner/Author.; 2024 International Conferaence on Management of Data, SIGMOD 2024 ; Conference date: 09-06-2024 Through 15-06-2024",
year = "2024",
month = jun,
day = "9",
doi = "10.1145/3626246.3654738",
language = "English (US)",
series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",
publisher = "Association for Computing Machinery",
pages = "488--491",
booktitle = "SIGMOD-Companion 2024 - Companion of the 2024 International Conferaence on Management of Data",
}