<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Digital systems can track every activity. Their logs are the fundamental raw material of intelligent systems in big data approaches. However, big data approaches mainly use predictions and correlations that often fail in the prediction of minorities or invisibilize collectives, causing discriminatory decisions. While this discrimination has been documented regarding, sex, race and sexual orientation, age has received less attention. A critical review of the academic literature confirms that structural ageism also shapes big data approaches. The article identifies some instances in which ageism is in operation either implicitly or explicitly. Concretely, biased samples and biased tools tend to exclude the habits, interests and values of older people from algorithms and studies, which contributes to reinforcing structural ageism.</p>
</abstract>

Digital systems can track every activity. Their logs are the fundamental raw material of intelligent systems in big data approaches. However, big data approaches mainly use predictions and correlations that often fail in the prediction of minorities or invisibilize collectives, causing discriminatory decisions. While this discrimination has been documented regarding, sex, race and sexual orientation, age has received less attention. A critical review of the academic literature confirms that structural ageism also shapes big data approaches. The article identifies some instances in which ageism is in operation either implicitly or explicitly. Concretely, biased samples and biased tools tend to exclude the habits, interests and values of older people from algorithms and studies, which contributes to reinforcing structural ageism.

Structural Ageism in Big Data Approaches

Internet Interdisciplinary Institute (IN3)

Nordicom Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Structural Ageism in Big Data Approaches"}

Digital systems can track every activity. Their logs are the fundamental raw material of intelligent systems in big data approaches. However, big data...

Article	Biased sample	Biased tool
Alvarez-Lozano et al. (2014)
Böhmer et al. (2011)	×
De Montjoye et al. (2013)
Ferdous et al. (2015)	×
Ferreira et al. (2012)	×
Ferreira et al. (2014)
Holz et al. (2015)		×
Ikebe et al. (2012)
Jones et al. (2015)	×	×
Karikoski and Soikkeli (2013)
Kiukkonen et al. (2010)		×
Lee et al. (2014)		×
Rahmati et al. (2012)		×
Rosales and Fernández-Ardèvol (2016b)
Shin et al. (2012)		×
Singh et al. (2013)
Srinivasan et al. (2014)		×
Wagner et al. (2013)	×	×
Xu et al. (2016)		×
Yan et al. (2012)		×

Article	Biased sample	Biased tool
Bayot and Gon (2017)
Bi et al. (2013)	×
Culotta et al. (2016)		×
Kosinski et al. (2013)	×
Liao et al. (2014)		×
Liu and Yang (2012)	×
Nguyen et al. (2013)		×
Nguyen et al. (2014)	×
Oktay et al. (2012)	×
Peersman et al. (2011)		×
Perozzi and Skiena (2015)		×
Rosenthal and McKeown (2011)	×
Schwartz et al. (2013)

Structural Ageism in Big Data Approaches

Online veröffentlicht: 28. Juni 2019

Seitenbereich: 51 - 64

DOI: https://doi.org/10.2478/nor-2019-0013

Schlüsselwörter
ageism, discrimination, big data, intelligent systems, literature review

© 2019 Andrea Rosales et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Classification of the reviewed articles on smartphone usage

Classification of the reviewed articles on age predictions

Structural Ageism in Big Data Approaches

Online veröffentlicht: 28. Juni 2019

Seitenbereich: 51 - 64

DOI: https://doi.org/10.2478/nor-2019-0013

Schlüsselwörterageism, discrimination, big data, intelligent systems, literature review

© 2019 Andrea Rosales et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Classification of the reviewed articles on smartphone usage

Classification of the reviewed articles on age predictions

Schlüsselwörter
ageism, discrimination, big data, intelligent systems, literature review