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The QUEST project is designing new AI tools that will help science journalists contextualise their stories and tailor them to different types of news audiences.
The Ubiquity of AI
Artificial intelligence – or AI for short – is now ubiquitous.
When I write ‘ubiquitous’, I don’t mean that AI software is present in most if not all aspects of our lives. Even if this presence might happen more soon than we think.
No, what I mean that is AI is ubiquitous in today’s ether – our conversations, our news and our politics. Everyone is talking about AI, what forms it will take, and what it will mean for our lives.
AI in Science Journalism
In QUEST, we are also talking about what forms of AI will take, and how it can support science journalism. These conversations are needed if we are to build the right forms of AI support for journalists.
Now, clearly we are not the first to think about developing and deploying AI to report science journalism.
But we are amongst the first to think more expansively about what AI might be able to do for journalists. Sure, AI tools that automatically make sense of and summarise complex academic papers for journalists are useful.
But is this all that AI can do for journalists?
We don’t think so.
New AI Features
So, in QUEST, we are imagining and designing new interactive AI tools to support journalists writing about science in other ways, and working with journalists to co-design the new features of these new tools.
One new feature will be creative search – innovative algorithms that will search for information from different sources and synthesise related information to present it to journalists to support rather than inhibit their work tasks. In QUEST, we recognise that news about science is not all about the science. Science shapes and is shaped by politics. It is undertaken by and impacts on people. And often, it has wider implications for our society and how we live.
So, journalists writing about science also need to know about the science’s human stories, the political context and social implications of the science. Information about these stories, contexts and implications can be found in diverse sources – in general news stories, scientific magazine articles and on social media sites, as well as in the scientific publications. In QUEST, we are developing new algorithms that, at the press of a button, will retrieve and collate science-related information from these diverse sources.
Another new feature will be smart guidance about target science news audiences, and in particular audiences more resistant to current forms of science journalism. Recently published research (e.g. [Schafer et al. 2018]) has revealed different audiences for science news, from the science enthusiasts and the critically interested to those who are passive supporters and disengaged from science.
Therefore, we drew on design practices and developed multiple audience personas – descriptions of fictitious people prototypical of each audience. Journalists will be able to choose personas to focus their stories and invoke specialised searches to retrieve information tailored to each of the audiences.
A third feature will be AI-based interactive guides for storytelling, to encourage the journalist to think creatively about the different roles that people play in science-based stories. New algorithms will encourage the journalist to think about different perspectives on established roles – e.g. protagonists, antagonists, and mentors in science – in the story, to develop new, more engaging angles on science stories
Co-designing AI features
At the moment we’re working with journalists across Europe to co-design these and other new features. Co-design is important, to ensure that each feature meets journalists’ needs, and works with established tools and work practices.
So, if you want to get involved over the autumn, contact me at N.A.M.Maiden@city.ac.uk.
Reference
Schafer M.S., Fuchslin T., Metag J., Kristiansen S. & Rauchfleisch A., 2018, ‘The different audiences of science communication: A segmentation analysis of the Swiss population’s perceptions of science and their information and media use patterns’, Public Understanding of Science 27(7), 836-856.
(Image by Gerd Altmann from Pixabay)
