Algorithms and automation increasingly are connected to many aspects of news production, distribution, and consumption. We invite original, unpublished papers to address such issues at an International Conference on Algorithms, Automation and News to be held at the Center for Advanced Studies at LMU Munich on May 22 – 23, 2018 — shortly before the ICA annual convention in Prague, not far from Munich.
Thanks to the generous support of our sponsors, there will be no conference fee, and free hotel accommodation will be provided for presenters, in addition to opportunities for need-based travel stipends. Select papers from the conference will be published in a special issue of Digital Journalism as well as a proposed edited volume.
- Professor Philip M. Napoli – the James R. Shepley Professor of Public Policy in the Sanford School of Public Policy, Duke University, USA.
- Natali Helberger – Professor of Information Law, with a special focus on the use of information, at the Institute for Information Law (IViR), University of Amsterdam.
- C. W. Anderson – Associate Professor at the College of Staten Island (CUNY) and, as of September 2017, a Professor of Media and Communication at the University of Leeds.
- Nicholas Diakopoulos – Assistant Professor at the University of Maryland, College Park Philip Merrill College of Journalism, with courtesy appointments in the College of Information Studies and Department of Computer Science.
Conference Theme and Topics
We live in a world increasingly influenced by algorithms and automation. The ubiquity of computing in contemporary culture has resulted in human decision-making being augmented, and even partially replaced, by computational processes. Such augmentation and substitution is already common, and even predominates, in some industries. This trend is now spreading rapidly to the fourth estate — our news media.
Algorithms and automation are increasingly implicated in many aspects of news production, distribution, and consumption. For example, algorithms are being used to filter the enormous quantities of content published on social media platforms, picking out what is potentially newsworthy and alerting journalists to its existence (Thurman et al., 2016).
Meanwhile, automated journalism — the transforming of structured data on such things as sports results and financial earnings reports into narrative news texts with little to no human intervention aside from the original programming (Carlson, 2015) — grows apace. What began some years ago as small-scale experiments in machine-written news has, amid the development of big data broadly, become a global phenomenon, involving technology providers from the U.S. to Germany to China developing algorithms to deliver automated news in multiple languages (Dörr, 2016).
And, algorithms are being used in new ways to distribute and package news content, both enabling consumers to request more of what they like and less of what they don’t and also making decisions on consumers’ behalf based on their behavioral traits, social networks, and personal characteristics (Groot Kormelink and Costera Meijer, 2014).
Altogether, these developments raise questions about the social role of journalism as a longstanding facilitator of public knowledge. What are the implications:
- for human labor and journalistic authority?
- for concerns around news quality, transparency, and accountability?
- for notions of who (or what) does journalism?
- for how news moves among various publics (or not)?
Additionally, what happens when editorial functions once performed by journalists are increasingly assumed by new sets of actors situated at the intersection of human and machine?
Ultimately, what do algorithms and automation mean for journalism — its people, purposes, and processes; its norms, ethics, and values; its relationship with audiences and public life; and its obligations toward data management and user privacy?
This three-part call — conference, special issue, and book project — takes up these and other questions by bringing together the latest scholarly research on algorithms, automation, and news. In particular, it seeks to organize research on capabilities, cases, and consequences associated with these technologies:
- explorations of the possibilities and perils,
- of theory and practice,
- and of comparative perspectives according to various sites and levels of analysis.
Ultimately, we aim for research that provides a future orientation while grounded in appropriate historical context, contemporary empirical research, and rigorous conceptual development.
By some accounts, the promise of algorithms and automation is that news may be faster and more personalized, that websites and apps may be more engaging, and even that quality journalism may be better funded, to the benefit of all. However, there are also concerns, including anxieties around:
- the hidden biases built into bots deciding what’s newsworthy,
- the ‘popularism’ that tracking trends inevitably promotes,
- how misplaced trust in algorithmic agency might blunt journalists’ critical faculties, and
- the privacy of data collected on individuals for the purposes of newsgathering and distribution.
Moreover, as more news is templated or data-driven, there is unease about issues such as:
- who and what gets reported,
- the ethics of authorship and accountability,
- the legal issues of libel by algorithm,
- the availability of opportunities for professional development, training, and education, and
- the continuity of fact-checking and analysis, among others.
And, as more news is explicitly or implicitly personalized, there is disquiet about:
- whether we will retreat into our own private information worlds, ‘protected’ from new, challenging and stimulating viewpoints,
- the algorithmically oriented spread of ‘fake news’ within such filter bubbles,
- the boundaries between editorial and advertising content, and
- the transparency and accountability of the decisions made about what we get to read and watch.
Through the conference, and the special issue of Digital Journalism and book to follow, we seek to facilitate conversation around these and related issues across a variety of academic fields, including:
- computer science
- information science
- computational linguistics
- media informatics
- law and public policy
- science and technology studies
- political science
- communication, media and journalism studies.
We welcome original, unpublished articles drawing on a variety of theoretical and methodological approaches, with a preference for empirically driven and/or conceptually rich accounts. These papers might touch on a range of themes, including but not limited to the issues outlined above.
- Neil Thurman – Professor of Communication with an emphasis on Computational Journalism in the Department of Communication Studies and Media Research, LMU Munich.
- Seth C. Lewis, Ph.D. – Shirley Papé Chair in Emerging Media in the School of Journalism and Communication at the University of Oregon, and an affiliated fellow of the Information Society Project at Yale Law School.
- Jessica Kunert – Postdoctoral Research Fellow in the Department of Communication Studies and Media Research at LMU Munich.