@inproceedings{73c67aa23d8343c98db239aad4325b09,
title = "Ethical and socially-aware data labels",
abstract = "{\textcopyright} 2019, Springer Nature Switzerland AG.Many software systems today make use of large amount of personal data to make recommendations or decisions that affect our daily lives. These software systems generally operate without guarantees of non-discriminatory practices, as instead often required to human decision-makers, and therefore are attracting increasing scrutiny. Our research is focused on the specific problem of biased software-based decisions caused from biased input data. In this regard, we propose a data labeling framework based on the identification of measurable data characteristics that could lead to downstream discriminating effects. We test the proposed framework on a real dataset, which allowed us to detect risks of discrimination for the case of population groups.",
author = "E. Beretta and A. Vetr{\`o} and B. Lepri and {De Martin}, J.C.",
year = "2019",
doi = "10.1007/978-3-030-11680-4_30",
language = "English",
isbn = "9783030116798",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "320--327",
editor = "J.A. Lossio-Ventura and D. Mu{\~n}ante and H. Alatrista-Salas",
booktitle = "Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings",
note = "5th International Conference on Information Management and Big Data, SIMBig 2018 ; Conference date: 03-09-2018 Through 05-09-2018",
}