Accountability in Algorithmic Decision Making

被引:288
作者
Diakopoulos, Nicholas [1 ,2 ,3 ,4 ]
机构
[1] Univ Maryland, Philip Merrill Coll Journalism, College Pk, MD 20742 USA
[2] Univ Maryland, Coll Informat Studies, College Pk, MD 20742 USA
[3] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA
[4] Columbia Univ, Ctr Digital Journalism, New York, NY 10027 USA
关键词
Automation - Decision making;
D O I
10.1145/2844110
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
EVERY FISCAL QUARTER, 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 labor-saving 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.
引用
收藏
页码:56 / 62
页数:7
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