(Foto von mir)
Für alle, die sich für Algorithmen und Journalismus interessieren, ist die Konferenz „Algorithms, Automation, and News“ an der Ludwig-Maximilians-Universität München der Ort, an dem man vom 22. bis 23. Mai 2018 sein muss! Nicht nur, weil ich zum Organisationsteam gehöre (zusammen mit Prof. Neil Thurman und Prof. Seth Lewis), sondern weil wir eine Menge zu bieten haben – so werden z.B. die besten eingereichten Paper in einer Special Issue von „Digital Journalism“ veröffentlicht. Außerdem werden die Übernachtungen für die Vortragenden gesponsert, d.h. das leibliche Wohl wird auch nicht außer Acht gelassen.
Was wir darüber hinaus noch alles bieten, das steht unten im englischen Call – die gesamte Konferenz wird auf Englisch stattfinden – und mit noch ausführlicheren Infos auf http://algorithmic.news.
Abstracts (500 bis 1000 Wörter) können bis zum 15. Juli 2017 an die Adresse email@example.com eingereicht werden.
Obwohl es sich um eine Konferenz für die Wissenschaftscommunity handelt, können auch Außenstehende daran teilhaben. Wir haben eine tolle Keynote von Prof. Philip M. Napoli (Duke University), die bestimmt sehr interessant wird.
Ich freue mich auf viele spannende Einreichungen!
Hier der Call – bequem zum Mitnehmen – als pdf:
ALGORITHMS, AUTOMATION, AND NEWS:
Capabilities, cases, and consequences
CALL FOR PAPERS: Conference, special issue & edited book
* Conference in Munich, Germany — May 22–23, 2018
* Select papers published in special issue of Digital Journalism & proposed edited volume
* 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
* Dr Jessica Kunert, Ludwig-Maximilians-University Munich
* Philip M. Napoli, Duke University
* 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 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.
Inquiries about this call are encouraged and should be directed to firstname.lastname@example.org.
* July 15, 2017: abstract submission deadline. Abstracts should be 500-1,000 words (not including references) and sent to email@example.com. 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
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.