We are pleased to announce our keynote speakers for the workshop:
AI & Automated News: Implications on Trust, Bias, and Credibility
Edgar Meij, BloombergEdgar Meij is a senior scientist at Bloomberg. Before this, he was a research scientist at Yahoo Labs and a postdoc at the University of Amsterdam, where he also obtained his Ph.D. His research focuses on all applications and aspects of knowledge graphs, entity linking, and semantic search.
While the technology is far from mature, artificial intelligence in the form of autonomous production of journalistic content is becoming increasingly prominent in newsrooms – and it’s here to stay. The promise of automatically generating news at a faster pace, a larger scale, in multiple languages, and with potentially fewer errors, has scholars and practitioners championing this technology. As always, this development fuels fears that journalists will soon be out of work. Yet, today’s algorithms cannot ask questions, explain phenomena, or establish causality, giving human journalists the opportunity to write stories that address the ‘why’ something happens – as opposed to the ‘what’ that machines tell us. When established news organizations start publishing partly or fully automated news stories, they lend credibility to them. Little is known yet about potential societal implications of this on dimensions of trust and potential bias, as the algorithms themselves cannot be held accountable. In this talk, I will discuss these developments and also place them in the context of news search and recommendations, automatic media monitoring, polarity detection and sentiment analysis.
"Every tool is better than nothing"?: The use of dashboards in journalistic work
Peter Tolmie, Universität SiegenPeter Tolmie is a Principal Research Scientist at the Universität Siegen in Germany. He has conducted ethnographic studies across numerous settings (e.g. retail banking, music production, the TV and film industries, journalism, etc...). Most recently, he has collaborated on the Pheme project, which is focused on establishing the veracity of claims in the internet.
In this paper I shall be drawing upon a series of ethnographic studies of journalists and technology evaluations undertaken during the European project PHEME, to examine some of the assumptions that get made about how dashboard-type systems might support journalists and the problems that can arise. In particular I shall be discussing the ‘clustering problem’, its ramifications, and how it constitutes one of the main challenges for future technology development in this space. The clustering problem refers to the thorny issue of how to assemble appropriate materials for journalists to work with that are meaningful and intelligible at the point of use. Whilst this resonates strongly with a number of long-standing issues in computer science, I shall focus here upon how it illuminates a gap between expectation and provision that is leading to a magpie-like proclivity for journalists to download the latest tool, try it once, then set it aside.
The workshop will also feature a discussion panel, combining insights from journalism and information retrieval. In addition to the keynote speakers, the panel will also feature:
David Corney spent 15 years in academia, applying machine learning to a variety of real-world problems. These included information extraction from biomedical research papers in collaboration with GSK, and tracking breaking news stories on Twitter as part of the SocialSensor FP7 project. He then joined Signal Media as a data scientist, where he continued to apply machine learning, NLP and IR methods to large-scale media monitoring, via document clustering, topic classification and entity recognition. In January, he joined Factmata as Lead ML/NLP engineer. Factmata is a start-up using machine learning and NLP approaches to identify misinformation and other forms of malicious digital content.
Barbara Poblete is an Assistant Professor at the Computer Science Department of the University of Chile in Santiago. She was a researcher at Yahoo! Labs for 5 years (in Barcelona and Santiago). Currently, her research areas are Web Data Mining, Social Network Analysis and Web IR. She is an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering Journal and Senior PC member for the conferences SIGIR and WWW (and PC member of several other top-tier conferences in her areas). Here work on time-sensitive credibility in microblogging platforms, published in WWW 2011 and in the Internet Research journal (2013), was the first on this particular topic (with +1800 citations), according to Google Scholar and has been featured in mainstream media such as Scientific American Magazine, The Wall Street Journal, Slate Magazine and BBC News, The Huffington Post, BBC News and NPR.