Sylvain Parasie

Data-driven journalism and the public good: “Computer-assisted-reporters” and “programmer-journalists” in Chicago

Since the mid-2000s, some US and British news organizations have hired programmers to design data-driven news projects within the newsroom. But how does the rise of these “programmer-journalists,” armed with their skills and technical artifacts, really affect the way journalism can contribute to the public good? Based on an empirical study in Chicago, we show in this article that although they have built on previous historical developments, these programmer-journalists have also partly challenged the epistemology conveyed by the computer-assisted reporting tradition in the US, grounded in the assumption that data can help journalists to set the political agenda through the disclosure of public issues. Involved in open source communities and open government advocacy, these programmers and their technical artifacts have conveyed challenging epistemological propositions that have been highly controversial in the journalism community.

Data-Driven Revelation?

As an increasing number of reporters see databases and algorithms as appropriate means of doing investigation, journalism has been challenged in recent years by the following question: to what extent would the processing of huge datasets allow journalists to produce new types of revelations that rely less on normative assumptions? Drawing on the analysis of a particular investigation by the San Francisco-based Center for Investigative Reporting, this article points out the existence of epistemological tensions in the making of journalistic revelations that involve the processing of vast amounts of data. First, I show that the design of data-processing artifacts can match the traditional epistemology of journalistic investigation, but only with great efforts and resources from the organization. Second, I point out that the use of these artifacts by journalists follows two opposite paths to produce the revelation: a “hypothesis-driven” path and a “data-driven” path. Such findings contribute to a better understanding of how news organizations produce justified beliefs, as data-processing artifacts become major components of the newsroom’s environment.