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Algorithmic Accountability Reporting: On the Investigation of Black Boxes

Nicholas Diakopoulos

Title:
Algorithmic Accountability Reporting: On the Investigation of Black Boxes
Author(s):
Diakopoulos, Nicholas
Date:
Type:
Reports
Department(s):
Journalism
Tow Center for Digital Journalism
Persistent URL:
Series:
Tow Center for Digital Journalism White Papers
Publisher:
Tow Center for Digital Journalism, Columbia University
Publisher Location:
New York
Abstract:
How can we characterize the power that various algorithms may exert on us? And how can we better understand when algorithms might be wronging us? What should be the role of journalists in holding that power to account? In this report I discuss what algorithms are and how they encode power. I then describe the idea of algorithmic accountability, first examining how algorithms problematize and sometimes stand in tension with transparency. Next, I describe how reverse engineering can provide an alternative way to characterize algorithmic power by delineating a conceptual model that captures different investigative scenarios based on reverse engineering algorithms’ input-output relationships. I then provide a number of illustrative cases and methodological details on how algorithmic accountability reporting might be realized in practice. I conclude with a discussion about broader issues of human resources, legality, ethics, and transparency.
Subject(s):
Journalism
Journalism--Methodology
Journalism--Data processing
Item views
587
Metadata:
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Suggested Citation:
Nicholas Diakopoulos, , Algorithmic Accountability Reporting: On the Investigation of Black Boxes, Columbia University Academic Commons, .

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