@inproceedings{f9c3ae914e07405599a6bfea62d5a8fc,
title = "Mithralabel: Flexible dataset nutritional labels for responsible data science",
abstract = "Using inappropriate datasets for data science tasks can be harmful, especially for applications that impact humans. Targeting data ethics, we demonstrate MithraLabel, a system for generating task-specific information about a dataset, in the form of a set of visual widgets, as a flexible {"}nutritional label{"} that provides a user with information to determine the fitness of the dataset for the task at hand.",
keywords = "Accountability, Data Ethics, Fairness, Machine Bias, Transparency",
author = "Chenkai Sun and Abolfazl Asudeh and Jagadish, {H. V.} and Bill Howe and Julia Stoyanovich",
note = "Funding Information: This work was supported in part by NSF Grants No. 1926250, 1741022, 1740996, and Microsoft. Funding Information: ∗This work was supported in part by NSF Grants No. 1926250, 1741022, 1740996, and Microsoft. Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 28th ACM International Conference on Information and Knowledge Management, CIKM 2019 ; Conference date: 03-11-2019 Through 07-11-2019",
year = "2019",
month = nov,
day = "3",
doi = "10.1145/3357384.3357853",
language = "English (US)",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "2893--2896",
booktitle = "CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management",
}