The Grey Area: How Regulations Impact Autonomy in Computational Journalism

Abstract

Computational journalists who use new technological methods in news production face an uncertain legal and policy landscape. Through data collected from eighteen in-depth interviews with journalists and editors, this article analyzes the legal issues surrounding computational journalism and provides insight into how journalists who use such methods negotiate their autonomy and independence. By utilizing a theoretical framework based in practice theory, this article illustrates how computational journalists perceive their autonomy as both constrained and enabled by legal regulation, organizational policy, and professional journalism norms and values.

Keywords:

• Autonomy
• computational journalism
• policy
• practice theory
• emergent routines
• press freedom

Acknowledgements

The author wishes to thank the anonymous reviewers and Matthew S. Weber whose critical feedback helped this article, as well as the interviewees for their time and thought-provoking discussions. The author is also thankful for a number of conversations with Jane E. Kirtley, which eventually led to this work.

Disclosure Statement

No potential conflict of interest was reported by the author.

Funding

The author received no funding for this article.

Notes

1 Such concerns are in line with recent reports that requests under the United States federal Freedom of Information Act (FOIA) have been met with prolonged delays, immense redaction, and outright denials. For more information about FOIA issues see the Transactional Records Access Clearinghouse, a research center at Syracuse University - https://trac.syr.edu/ and the FOIA Project - http://foiaproject.org/. Recent research presented at the National Freedom of Information Coalition’s annual conference also highlighted that access to government databases poses unique legal issues under FOI laws (see e.g., Anderson and Wiley 2020). Meanwhile, it is important to note that data access is often strategic and offered by institutions with a specific outcome in mind (Splendore 2016).

2 A number of recent decisions have found that the CFAA’s “unauthorized access” provision does not apply to public website data that is presumptively open to all (see e.g. hiQ Labs, Inc. v. LinkedIn Corp., 2019; Sandvig v. Barr, 2020) However, the question is not yet completely settled (Kerr 2020; The Markup 2020; see also Van Buren v. United States, 2020).

3 It is also worth noting that juries may have a different interpretation than judges on what should be “newsworthy.” See e.g., Bollea v. Gawker Media, LLC, . -(M.D. Fla. 2012); Gawker Media, LLC v. Bollea, - (Fla. Dist. Ct. App. 2014); Gawker Media, LLC v. Bollea, (Fla. Dist. Ct. App. 2015).

4 Clearview AI is a facial recognition company that provides tools to law enforcement agencies in the United States. What makes the tools so valuable is the massive database of more than three billion images that Clearview claims to have scraped from public profiles including Facebook, Youtube, and Venmo (Hill 2020), similar to the web scraping practices used by computational journalists.