Ethical and socially-aware data labels

E. Beretta; A. Vetrò; B. Lepri; J.C. De Martin

doi:10.1007/978-3-030-11680-4_30

Ethical and socially-aware data labels

E. Beretta
, A. Vetrò
, B. Lepri
, J.C. De Martin

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

© 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.

Original language	English
Title of host publication	Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings
Editors	J.A. Lossio-Ventura, D. Muñante, H. Alatrista-Salas
Publisher	Springer Verlag
Pages	320-327
ISBN (Print)	9783030116798
DOIs	https://doi.org/10.1007/978-3-030-11680-4_30
Publication status	Published - 2019
Externally published	Yes
Event	5th International Conference on Information Management and Big Data, SIMBig 2018 - Lima, Peru Duration: 3 Sept 2018 → 5 Sept 2018

Publication series

Name	Communications in Computer and Information Science
ISSN (Print)	1865-0929

Conference

Conference	5th International Conference on Information Management and Big Data, SIMBig 2018
Country/Territory	Peru
City	Lima
Period	3/09/18 → 5/09/18

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10.1007/978-3-030-11680-4_30

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Beretta, E., Vetrò, A., Lepri, B., & De Martin, J. C. (2019). Ethical and socially-aware data labels. In J. A. Lossio-Ventura, D. Muñante, & H. Alatrista-Salas (Eds.), Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings (pp. 320-327). (Communications in Computer and Information Science). Springer Verlag. https://doi.org/10.1007/978-3-030-11680-4_30

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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",

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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",

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Beretta, E, Vetrò, A, Lepri, B & De Martin, JC 2019, Ethical and socially-aware data labels. in JA Lossio-Ventura, D Muñante & H Alatrista-Salas (eds), Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings. Communications in Computer and Information Science, Springer Verlag, pp. 320-327, 5th International Conference on Information Management and Big Data, SIMBig 2018, Lima, Peru, 3/09/18. https://doi.org/10.1007/978-3-030-11680-4_30

Ethical and socially-aware data labels. / Beretta, E.; Vetrò, A.; Lepri, B. et al.
Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings. ed. / J.A. Lossio-Ventura; D. Muñante; H. Alatrista-Salas. Springer Verlag, 2019. p. 320-327 (Communications in Computer and Information Science).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Vetrò, A.

AU - Lepri, B.

AU - De Martin, J.C.

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N2 - © 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.

AB - © 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.

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ER -

Ethical and socially-aware data labels

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