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Accountability in algorithmic decision making

Accountability in algorithmic decision making practice DOI:10.1145/ 2844110 Article development led by queue.acm.org A view from computational journalism. BY NICHOLAS DIAKOPOULOS Accountability in Algorithmic Decision Making automated writing algorithms churn out thousands of corporate earnings articles for the Associated Press based on little more than structured data. Companies such as Automated Insights, which produces the articles for the AP, and Narrative Science can now write straight news articles in almost any domain that has clean and well-structured data: finance, sure, but also sports, weather, and education, among others. The articles are not cardboard either; they have variability, tone, and style, and in some cases readers even have difficulty distinguishing the machine-produced articles from human-written ones.4 It is difficult to argue with the scale, speed, and laborsaving cost advantage that such systems afford. But the trade-off for media organizations appears to be nuance and accuracy. A quick search on Google for "`generated by Automated Insights' correction'" yields results for thousands of articles that were automatically written, published, and then had to have corrections issued. EVERY F ISC AL QUART E R, COMM UNICATIO NS O F THE ACM | F EBR UA RY 201 6 | VO L . 5 9 | NO. 2 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Communications of the ACM Association for Computing Machinery

Accountability in algorithmic decision making

Communications of the ACM , Volume 59 (2) – Jan 25, 2016

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References (24)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
0001-0782
DOI
10.1145/2844110
Publisher site
See Article on Publisher Site

Abstract

practice DOI:10.1145/ 2844110 Article development led by queue.acm.org A view from computational journalism. BY NICHOLAS DIAKOPOULOS Accountability in Algorithmic Decision Making automated writing algorithms churn out thousands of corporate earnings articles for the Associated Press based on little more than structured data. Companies such as Automated Insights, which produces the articles for the AP, and Narrative Science can now write straight news articles in almost any domain that has clean and well-structured data: finance, sure, but also sports, weather, and education, among others. The articles are not cardboard either; they have variability, tone, and style, and in some cases readers even have difficulty distinguishing the machine-produced articles from human-written ones.4 It is difficult to argue with the scale, speed, and laborsaving cost advantage that such systems afford. But the trade-off for media organizations appears to be nuance and accuracy. A quick search on Google for "`generated by Automated Insights' correction'" yields results for thousands of articles that were automatically written, published, and then had to have corrections issued. EVERY F ISC AL QUART E R, COMM UNICATIO NS O F THE ACM | F EBR UA RY 201 6 | VO L . 5 9 | NO. 2

Journal

Communications of the ACMAssociation for Computing Machinery

Published: Jan 25, 2016

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