Digital Phenotyping and Data Inheritance

Research output: Contribution to journal › Journal article › Research › peer-review

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Digital Phenotyping and Data Inheritance. / Green, Sara; Svendsen, Mette Nordahl.

In: Big Data & Society, Vol. 8, No. 2, 2021, p. 1-5.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Green, S & Svendsen, MN 2021, 'Digital Phenotyping and Data Inheritance', Big Data & Society, vol. 8, no. 2, pp. 1-5. https://doi.org/10.1177/20539517211036799

APA

Green, S., & Svendsen, M. N. (2021). Digital Phenotyping and Data Inheritance. Big Data & Society, 8(2), 1-5. https://doi.org/10.1177/20539517211036799

Vancouver

Green S, Svendsen MN. Digital Phenotyping and Data Inheritance. Big Data & Society. 2021;8(2):1-5. https://doi.org/10.1177/20539517211036799

Author

Green, Sara ; Svendsen, Mette Nordahl. / Digital Phenotyping and Data Inheritance. In: Big Data & Society. 2021 ; Vol. 8, No. 2. pp. 1-5.

Bibtex

@article{8d562fb8cde54eeb907118da11e01974,
title = "Digital Phenotyping and Data Inheritance",
abstract = "Proponents of precision medicine envision that digital phenotyping can enable more individualized strategies to manage current and future health conditions. We problematize the interpretation of digital phenotypes as straightforward representations of individuals through examples of what we call data inheritance. Rather than being a digital copy of a presumed original, digital phenotypes are shaped by larger data collectives that precede and continuously change how the individual is represented. We contend that looking beyond the individual is crucial for understanding the factors that can “bend” digital mirrors in specific directions. Since algorithms used for digital profiling are based on historical data, their predictions often inherit and increase the values and perspectives of past data practices. Moreover, the data legacies we leave behind today may return as so-called “data phantoms” that conflict with the interests of the individual and contest who and what the “original” is.",
author = "Sara Green and Svendsen, {Mette Nordahl}",
year = "2021",
doi = "10.1177/20539517211036799",
language = "English",
volume = "8",
pages = "1--5",
journal = "Big Data & Society",
issn = "2053-9517",
publisher = "SAGE Publications",
number = "2",

}

RIS

TY - JOUR

T1 - Digital Phenotyping and Data Inheritance

AU - Green, Sara

AU - Svendsen, Mette Nordahl

PY - 2021

Y1 - 2021

N2 - Proponents of precision medicine envision that digital phenotyping can enable more individualized strategies to manage current and future health conditions. We problematize the interpretation of digital phenotypes as straightforward representations of individuals through examples of what we call data inheritance. Rather than being a digital copy of a presumed original, digital phenotypes are shaped by larger data collectives that precede and continuously change how the individual is represented. We contend that looking beyond the individual is crucial for understanding the factors that can “bend” digital mirrors in specific directions. Since algorithms used for digital profiling are based on historical data, their predictions often inherit and increase the values and perspectives of past data practices. Moreover, the data legacies we leave behind today may return as so-called “data phantoms” that conflict with the interests of the individual and contest who and what the “original” is.

AB - Proponents of precision medicine envision that digital phenotyping can enable more individualized strategies to manage current and future health conditions. We problematize the interpretation of digital phenotypes as straightforward representations of individuals through examples of what we call data inheritance. Rather than being a digital copy of a presumed original, digital phenotypes are shaped by larger data collectives that precede and continuously change how the individual is represented. We contend that looking beyond the individual is crucial for understanding the factors that can “bend” digital mirrors in specific directions. Since algorithms used for digital profiling are based on historical data, their predictions often inherit and increase the values and perspectives of past data practices. Moreover, the data legacies we leave behind today may return as so-called “data phantoms” that conflict with the interests of the individual and contest who and what the “original” is.

U2 - 10.1177/20539517211036799

DO - 10.1177/20539517211036799

M3 - Journal article

VL - 8

SP - 1

EP - 5

JO - Big Data & Society

JF - Big Data & Society

SN - 2053-9517

IS - 2

ER -

ID: 274169739