[ecrea] cfp: Algorithms, automation, and news

ALGORITHMS, AUTOMATION, AND NEWS:
CfP: Conference, special issue & edited book

http://algorithmic.news

* Conference in Munich, Germany — May 22–23, 2018
* Select papers published in special issue of Digital Journalism & 
proposed edited volume

CONFERENCE BENEFITS:

* Free hotel accommodation for presenters
* Travel stipends available for presenters
* No conference fee
* Precedes the 2018 ICA convention in nearby Prague

ORGANIZERS & EDITORS:

* Neil Thurman, Ludwig-Maximilians-University Munich
* Seth C. Lewis, University of Oregon
* With the assistance of Dr Jessica Kunert, 
Ludwig-Maximilians-University Munich

KEYNOTE SPEAKER:

* Philip M. Napoli, Duke University

CONFIRMED SPEAKERS:

* C.W. Anderson, College of Staten Island & University of Leeds
* Natali Helberger, University of Amsterdam
* Nicholas Diakopoulos, University of Maryland

CALL FOR PAPERS:

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)? Ultimately, 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 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, philosophy, sociology, political 
science, and design, in addition to communication, media and journalism 
studies. We welcome 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.

Inquiries about this call are encouraged and should be directed to 
(conference /at/ algorithmic.news).

TIMELINE:

* July 15, 2017: abstract submission deadline. Abstracts should be 
500-1,000 words (not including references) and sent to 
(conference /at/ algorithmic.news). Also include a 100-word biography of each 
author and 6-8 keywords
* Mid-August 2017: decisions on abstracts
* February 15, 2018: full 7,000-word papers due for initial round of 
feedback by conference peers
* May 22–23, 2018: conference in Munich
* Post-conference: peer-review and feedback process leading toward 
publication in either the special issue or edited volume

http://algorithmic.news

ORGANISERS & SPONSORS:

Conference organised by the Center for Advanced Studies at 
Ludwig-Maximilians-University Munich and sponsored by The Volkswagen 
Foundation (VolkswagenStiftung) and The Shirley Papé Chair in the School 
of Journalism and Communication at the University of Oregon.


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