*A Primer on Powerful Numbers: Selected Readings in the Social Study of
Public Data and Official Numbers**
*
/https://datasociety.net/library/a-primer-on-powerful-numbers-selected-readings-in-the-social-study-of-public-data-and-official-numbers/
/
Scholars Dan Bouk, Kevin Ackermann, and danah boyd join forces on a
thought-provoking resource that begins to explore the power of official
numbers and public data. In /*A Primer on Powerful Numbers: Selected
Readings in the Social Study of Public Data and Official Numbers*/, the
authors touch on the power and authority that public numbers have in
societies.
Written with the end user in mind, this publication is intended to be a
non-exhaustive syllabus organized around a series of teachable or
debatable claims concerning the influence institutions of authority
have on how data and numbers are created, as well as how that
information is used by the datafied state to make fundamental decisions
about democratic policy and process.
Official numbers are the foundation upon which modern societies trust
data. An official number is different from any other number because it’s
given with authority and always there for the taking. Official data sets
come out of bureaucratic and corporate offices and are imbued with the
authority of those in power.
Provided in an easy-to-digest format, *this primer is organized around
six key arguments that center on the authority of data:*
1. Modern societies are built to trust in official numbers (they even
let official numbers make key decisions);
2. Official numbers are made, not found;
3. We forget that official numbers have to be made even when things are
going well;
4. Institutions make public data and they make data public;
5. Official numbers are political; and
6. Consensus on official numbers requires work.
The compilation of readings in this acute collection by Bouk, Ackermann,
and boyd encourages deep exploration into this topic by introducing
readers to 103 seminal works by a wide range of scholars, including but
not limited to Ruha Benjamin, Madeleine Claire Elish, Khalil Gibran
Muhammad, Ranjit Singh, Kadijah Ferryman, Jacqueline Wernimont, and Kate
Brown.
For more information on official numbers in practice, read the House
Arrest report <https://datasociety.net/library/house-arrest/>by Dan Bouk
on how an automated algorithm constrained Congress for a century. And
for more information on the challenge of producing detailed, useful, and
confidential public data in the 2020 census, read the Differential
Privacy report
<https://datasociety.net/library/balancing-data-utility-and-confidentiality-in-the-2020-us-census/>by
danah boyd.
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