In the first part of this series (Notes on Robot Churnalism, Part I – Robot Writers), I reviewed some of the ways in which robot writers are able to contribute to the authoring of news content.
In this part, I will consider some of the impacts that might arise from robots entering the workplace.
Robot Journalism in the Workplace
“Robot journalists” have some competitive advantages which are hard for human journalists to compete with. The strengths of automated content generation are the low marginal costs, the speed with which articles can be written and the broad spectrum of sport events which can be covered.
Arjen van Dalen, The Algorithms Behind the Headlines, Journalism Practice, 6:5-6, 648-658, 2012, p652
One thing machines do better is create value from large amounts of data at high speed. Automation of process and content is the most under-explored territory for reducing costs of journalism and improving editorial output. Within five to 10 years, we will see cheaply produced information monitored on networks of wireless devices.
Post Industrial Journalism: Adapting to the Present, Chris Anderson, Emily Bell, Clay Shirky, Tow Center for Digital Journalism Report, December 3, 2014
Year on year, it seems, the headlines report how the robots are coming to take over a wide range of professional jobs and automate away the need to employ people to fill a wide range of currently recognised roles (see, for example, this book: The Second Machine Age [review], this Observer article: Robots are leaving the factory floor and heading for your desk – and your job, this report: The Future of Employment: How susceptible are jobs to computerisation? [PDF], this other report: AI, Robotics, and the Future of Jobs [review], and this business case: Rethink Robotics: Finding a Market).
Stories also abound fearful of a possible robotic takeover of the newsroom: ‘Robot Journalist’ writes a better story than human sports reporter (2011), The robot journalist: an apocalypse for the news industry? (2012), Can an Algorithm Write a Better News Story Than a Human Reporter? (2012), Robot Writers and the Digital Age (2013), The New Statesman could eventually be written by a computer – would you care? (2013), The journalists who never sleep (2014), Rise of the Robot Journalist (2014), Journalists, here’s how robots are going to steal your job (2014), Robot Journalist Finds New Work on Wall Street (2015).
It has to be said, though, that many of these latter “inside baseball” stories add nothing new, perhaps reflecting the contributions of another sort of robot to the journalistic process: web search engines like Google…
Looking to the academic literature, in his 2015 case study around Narrative Science, Matt Carlson describes how “public statements made by its management reported in news about the company reveal two commonly expressed beliefs about how its technology will improve journalism: automation will augment— rather than displace — human journalists, and it will greatly expand journalistic output” p420 (Matt Carlson (2015), The Robotic Reporter, Digital Journalism, 3:3, 416-431).
As with the impact of many other technological innovations within the workplace, “[a]utomated journalism’s ability to generate news accounts without intervention from humans raises questions about the future of journalistic labor” (Carlson, 2015, p422). In contrast to the pessimistic view that “jobs will lost”, there are at least two possible positive outcomes for jobs that may result from the introduction of a new technology: firstly, that the technology helps transform the original job and in so doing help make it more rewarding, or allows the original worker to “do more”; secondly, that the introduction of the new technology creates new roles and new job opportunities.
On the pessimistic side, Carlson describes how:
many journalists … question Narrative Science’s prediction that its service would free up or augment journalists, including Mathew Ingram (GigaOm, April 25, 2012): “That’s a powerful argument, but it presumes that the journalists who are ‘freed up’ because of Narrative Science … can actually find somewhere else that will pay them to do the really valuable work that machines can’t do. If they can’t, then they will simply be unemployed journalists.” This view challenges the virtuous circle suggested above to instead argue that some degree of displacement is inevitable.(Carlson, 2015, p423)
On the other hand:
[a]ccording to the more positive scenario, machine-written news could be complementary to human journalists. The automation of routine tasks offers a variety of possibilities to improve journalistic quality. Stories which cannot be covered now due to lack of funding could be automated. Human journalists could be liberated from routine tasks, giving them more time to spend on quality, in-depth reporting, investigative reporting. (van Dalen, p653)
This view thus represents the idea of algorithms working alongside the human journalists, freeing them up from the mundane tasks and allow them to add more value to a story… If a journalist has 20 minutes to spend on a story, if that time is spent searching a database and pulling out a set of numbers that may not even be very newsworthy, how much more journalistically productive could that journalist be if a machine gave them the data and a canned summary of it for free, then allowing the journalist to use the few minutes allocated to that story to take the next step – adding in some context, perhaps, or contacting a second source for comment?
A good example of the time-saving potential of automated copy production can be seen in the publication of earnings reports by AP, as reported by trade blog journalism.co.uk, who quoted vice president and managing editor Lou Ferrara’s announcement of a tenfold increase in stories from 300 per quarter produced by human journalists, to 3,700 with machine support (AP uses automation to increase story output tenfold, June, 2015).
The process AP went through during testing appears to be one that I’m currently exploring with my hyperlocal, OnTheWight, for producing monthly JobSeekers Allowance reports (here’s an example of the human produced version, which in this case was corrected after a mistake was spotted when checking that an in-testing machine generated version of the report was working correctly..! As journalism.co.uk reported about AP, “journalists were doing all their own manual calculations to produce the reports, which Ferrara said had ‘potential for error’.” Exactly the same could have been said of the OnTheWight process…)
In the AP case, “during testing, the earnings reports were produced via automation and journalists compared them to the relevant press release and figured out bugs before publishing them. A team of five reporters worked on the project, and Ferrara said they still had to check for everything a journalist would normally check for, from spelling mistakes to whether the calculations were correct.” (I wonder if they check the commas, too?!) The process I hope to explore with OnTheWight builds in the human checking route, taking the view that the machine should generate press-release style copy that does the grunt work in getting the journalist started on the story, rather than producing the complete story for them. At AP, it seems that automation “freed up staff time by one fifth”. The process I’m hoping to persuade OnTheWight to adopt is that to begin with, the same amount of time should be spent on the story each month, but month on month we automate a bit more and the journalistic time is then spent working up what the next paragraph might be, and then in turn automate the production of that…
Extending the Promise?
In addition to time-saving, there is the hope that the wider introduction of robot journalists will create new journalistic roles:
Beyond questions of augmentation or elimination, Narrative Science’s vision of automated journalism requires the transformation of journalistic labor to include such new positions as “meta-writer” or “metajournalist” to facilitate automated stories. For example, Narrative Science’s technology can only automate sports stories after journalists preprogram it with possible frames for sports stories (e.g., comeback, blowout, nail-biter, etc.) as well as appropriate descriptive language. After this initial programming, automated journalism requires ongoing data management. Beyond the newsroom, automated journalism also redefines roles for non-journalists who participate in generating data. (Carlson, 2015, p423)
In the first post of these series, I characterised the process used by Narrative Science which included the application of rules for detecting signals and angles, and the linkage of detected “facts” to story points within an a particular angle that could then be used to generate a narrative told through automatically generated natural language. Constructing angles, identifying logical processes that can identify signals and map them on to story elements, and generating turns of phrase that can help explicate narratives in a natural way are all creative acts that are likely to require human input for the near future at least, albeit tasking the human creative with the role of supporting the machine. This is not necessarily that far removed from the some of the skills already employed by journalists, however. As Carlson suggests, “Scholars have long documented the formulaic nature underlying compositional forms of news exposed by the arrival of automated news. … much journalistic writing is standardized to exclude individual voice. This characteristic makes at least a portion of journalistic output susceptible to automation” (p425). What’s changing, perhaps, is that now the journalists mush learn to capture those standardised forms and map them onto structures that act as programme fodder for their robot helpers.
Audience Development
Narrative Science also see potential in increasing the size of the total potential audience by accommodating the very specific needs of a large number of niche audiences.
“While Narrative Science flaunts the transformative potential of automated journalism to alter both the landscape of available news and the work practices of journalists, its goal when it comes to compositional form is conformity with existing modes of human writing. The relationship here is telling: the more the non-human origin of its stories is undetectable, the more it promises to disrupt news production. But even in emulating human writing, the application of Narrative Science’s automation technology to news prompts reconsiderations of the core qualities underpinning news composition. The attention to the quality and character of Narrative Science’s automated news stories reflects deep concern both with existing news narratives and with how automated journalistic writing commoditizes news stories.” Carlson, 2015, p424
In the midst of this mass of stories, it’s possible that there will be some “outliers” that are of more general interest which can, with some additional contextualisation and human reporting, be made relevant to a wider audience.
There is also the possible of searching for “meta-stories” that tell not the specifics of particular cases, but identify trends across the mass of stories as whole. (Indeed, it is by looking for such trends and patterns that outliers may be detected). In addition, patterns that only become relevant when looking across all the individual stories might in turn lead to additional stories. (For example, a failing school operated by a particular provider is perhaps of only local interest, but if it turns out that the majority of schools operated by a particular provider we turned round from excellent to failing by that provider, questions might, perhaps, be worth asking…?!)
When it comes to the case for expanding the range of content that is available, Narrative Science’s hope appears to be that:
[t]he narrativization of data through sophisticated artificial intelligence programs vastly expands the terrain of news. Automated journalism becomes a normalized component of the news experience. Moreover, Narrative Science has tailored its promotional discourse to reflect the economic uncertainty of online journalism business models by suggesting that its technology will create a virtuous circle in which increased news revenue supports more journalists (Carlson, 2015, p 421).
The alternative, fearful view, of course, is that revenues will be protected by reducing the human wage bill, using robot content creators operating at a near zero marginal cost on particular story types to replace human content creation.
Whether news organisations will use automation to extend the range of producers in the newsroom, or contribute to the reduction of human creative input to the journalistic process, is perhaps still to be seen. As Anderson, Bell & Shirky noted, “the reality is that most journalists at most newspapers do not spend most of their time conducting anything like empirically robust forms of evidence gathering.” Perhaps now is the time for them to stop churning the press releases and statistics announcements – after all, the machines can do that faster and better – and concentrate more on contextualising and explaining the machine generated stories, as well as spending more time out hunting for stories and pursuing their own investigative leads?
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