Excavating awareness and power in data science: A manifesto for trustworthy pervasive data research

Big Data and Society 8 (2) (2021)
  Copy   BIBTEX

Abstract

Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 91,881

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

A Realistic Analysis of Possibility.Richard L. Barber - 1952 - Review of Metaphysics 5 (3):341 - 360.
Levels of awareness and “awareness without awareness”: From data to theory.G. E. Schwartz - 1996 - In S. Hamreoff, Alfred W. Kaszniak & A. C. Scott (eds.), Toward a Science of Consciousness. MIT Press. pp. 279--298.
Data politics.Didier Bigo, Engin Isin & Evelyn Ruppert - 2017 - Big Data and Society 4 (2).
Trustworthy research—an editorial introduction.Caroline Whitbeck - 1995 - Science and Engineering Ethics 1 (4):322-328.
Good Data.Angela Daly, Monique Mann & S. Kate Devitt - 2019 - Amsterdam, Netherlands: Institute of Network Cultures.
Radical empiricism and machine learning research.Judea Pearl - 2021 - Journal of Causal Inference 9 (1):78-82.

Analytics

Added to PP
2022-02-10

Downloads
12 (#1,085,484)

6 months
8 (#361,431)

Historical graph of downloads
How can I increase my downloads?

Author's Profile

Jacob Metcalf
University of California, Santa Cruz

References found in this work

Data feminism.Catherine D'Ignazio - 2020 - Cambridge, Massachusetts: The MIT Press. Edited by Lauren F. Klein.
Trust and Power.Niklas Luhmann - 1982 - Studies in Soviet Thought 23 (3):266-270.

View all 12 references / Add more references