The economic fallout from the COVID-19 pandemic has caused an unprecedented crisis in journalism that could decimate media organizations around the world.

The future of journalism — and its survival — could lie in artificial intelligence (AI). AI refers “to intelligent machines that learn from experience and perform tasks like humans,” according to Francesco Marconi, a professor of journalism at Columbia University in New York, who has just published a book on the subject: Newsmakers, Artificial Intelligence and the Future of Journalism.

Marconi was head of the media lab at the Wall Street Journal and the Associated Press, one of the largest news organizations in the world. His thesis is clear and incontrovertible: the journalism world is not keeping pace with the evolution of new technologies. So, newsrooms need to take advantage of what AI can offer and come up with new a business model.

For Marconi, journalists and media owners are missing out and AI needs to be at the heart of journalism’s business model in the future. As a professor of journalism at the Université du Québec à Montréal, I have been closely following the evolution of this profession since 1990, and I am mostly in agreement with him.

In Canada, The Canadian Press news agency is, for example, one of the rare media outlets to use AI in its newsrooms. It has developed a system to speed up translations based on AI. The _Agence France-Presse_ news agency (AFP) also uses AI to detect doctored photos.

AI does not replace journalists

Artificial intelligence is not there to replace journalists or eliminate jobs. Marconi believes that only eight to 12 per cent of reporters’ current tasks will be taken over by machines, which will in fact reorient editors and journalists towards value-added content: long-form journalism, feature interviews, analysis, data-driven journalism and investigative journalism.

At the moment, AI robots perform basic tasks like writing two to six paragraphs on sports scores and quarterly earnings reports at the Associated Press, election results in Switzerland and Olympic results at the Washington Post. The outcomes are convincing, but they also show the limits of AI.

AI robots analyzing large databases can send journalists at Bloomberg News an alert as soon as a trend or anomaly emerges from big data.

AI can also save reporters a lot of time by transcribing audio and video interviews. AFP has a tool for that. The same is true for major reports on pollution or violence, which rely on vast databases. The machines can analyze complex data in no time at all.

Afterwards, the journalist does his or her essential work of fact-checking, analyzing, contextualizing and gathering information. AI can hardly replace this. In this sense, humans must remain central to the entire journalistic process.

A broken business model

Marconi is quite right when he explains that the media must develop a paid subscription model, get closer to their communities with even more relevant content, develop new products (newsletters, events, podcasts, videos) and new content. AI can facilitate some of this by generating personalized news: recommendations for readers, for example.

In this sense, AI is part of a new business model based on breaking down media silos. There needs to be a symbiosis in the sense of establishing a “close collaboration” between the editorial staff and other media teams such as engineers, computer scientists, statisticians, sales or marketing staff.

In a newsroom, more than ever before, databases must be used to find stories that are relevant to readers, listeners, viewers and internet users.

And there are already various AI tools available to detect trends or hot topics on the internet and social media. These tools can also help newsrooms distribute content.

Beware of bias

Of course, newsroom size must be taken into account. A small weekly or a hyper-local media organization may not have the means to act quickly in adopting AI. But for the others, it’s important to start taking action right away. Journalists need to be better trained and begin to work with start-ups and universities to get the best out of this. AI is not a fad. It is here to stay.

Take the current example of COVID-19. This is an opportunity to analyze public health data to make connections, analyze and dig into the data neighbourhood by neighbourhood and street by street. AI can help with that. But it takes well-trained data reporters to do this work.

One of the dangers of AI, on the other hand, is algorithm bias. Because algorithms are designed by humans, there will always be biases that can alter data analysis and lead to serious consequences. And human verification of content before publication will always remain a safeguard against errors.

AI has also helped developing systems for detecting fake videos (deepfakes) and fake news, which are of course supported by experienced journalists from Reuters and AFP, for example.

In this sense, the transformation of newsrooms is only just beginning and Marconi’s essay is a must-read for identifying survival scenarios for media organizations and journalists. Because that’s what it’s all about. We need to better equip our newsrooms and completely rethink the workflow to achieve better collaboration and better content that will attract new and paying subscribers.

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Much as robots have transformed entire swaths of the manufacturing economy, artificial intelligence and automation are now changing information work, letting humans offload cognitive labor to computers. In journalism, for instance, data mining systems alert reporters to potential news stories, while newsbots offer new ways for audiences to explore information. Automated writing systems generate financial, sports and elections coverage.

A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who – or what – will do journalism in this AI-enhanced and automated world, and how will they do it?

The evidence I’ve assembled in my new book “Automating the New: How Algorithms are Rewriting the Media” suggests that the future of AI-enabled journalism will still have plenty of people around. However, the jobs, roles and tasks of those people will evolve and look a bit different. Human work will be hybridized – blended together with algorithms – to suit AI’s capabilities and accommodate its limitations.

Augmenting, not substituting

Some estimates suggest that current levels of AI technology could automate only about 15% of a reporter’s job and 9% of an editor’s job. Humans still have an edge over non-Hollywood AI in several key areas that are essential to journalism, including complex communication, expert thinking, adaptability and creativity.

Reporting, listening, responding and pushing back, negotiating with sources, and then having the creativity to put it together – AI can do none of these indispensable journalistic tasks. It can often augment human work, though, to help people work faster or with improved quality. And it can create new opportunities for deepening news coverage and making it more personalized for an individual reader or viewer.

Newsroom work has always adapted to waves of new technology, including photography, telephones, computers – or even just the copy machine. Journalists will adapt to work with AI, too. As a technology, it is already and will continue to change newswork, often complementing but rarely substituting for a trained journalist.

New work

I’ve found that more often than not, AI technologies appear to actually be creating new types of work in journalism.

Take for instance the Associated Press, which in 2017 introduced the use of computer vision AI techniques to label the thousands of news photos it handles every day. The system can tag photos with information about what or who is in an image, its photographic style, and whether an image is depicting graphic violence.

The system gives photo editors more time to think about what they should publish and frees them from spending lots of time just labeling what they have. But developing it took a ton of work, both editorial and technical: Editors had to figure out what to tag and whether the algorithms were up to the task, then develop new test data sets to evaluate performance. When all that was done, they still had to supervise the system, manually approving the suggested tags for each image to ensure high accuracy.

Stuart Myles, the AP executive who oversees the project, told me it took about 36 person-months of work, spread over a couple of years and more than a dozen editorial, technical and administrative staff. About a third of the work, he told me, involved journalistic expertise and judgment that is especially hard to automate. While some of the human supervision may be reduced in the future, he thinks that people will still need to do ongoing editorial work as the system evolves and expands.

Semi-automated content production

In the United Kingdom, the RADAR project semi-automatically pumps out around 8,000 localized news articles per month. The system relies on a stable of six journalists who find government data sets tabulated by geographic area, identify interesting and newsworthy angles, and then develop those ideas into data-driven templates. The templates encode how to automatically tailor bits of the text to the geographic locations identified in the data. For instance, a story could talk about aging populations across Britain, and show readers in Luton how their community is changing, with different localized statistics for Bristol. The stories then go out by wire service to local media who choose which to publish.

The approach marries journalists and automation into an effective and productive process. The journalists use their expertise and communication skills to lay out options for storylines the data might follow. They also talk to sources to gather national context, and write the template. The automation then acts as a production assistant, adapting the text for different locations.

RADAR journalists use a tool called Arria Studio, which offers a glimpse of what writing automated content looks like in practice. It’s really just a more complex interface for word processing. The author writes fragments of text controlled by data-driven if-then-else rules. For instance, in an earthquake report you might want a different adjective to talk about a quake that is magnitude 8 than one that is magnitude 3. So you’d have a rule like, IF magnitude > 7 THEN text = “strong earthquake,” ELSE IF magnitude < 4 THEN text = “minor earthquake.” Tools like Arria also contain linguistic functionality to automatically conjugate verbs or decline nouns, making it easier to work with bits of text that need to change based on data.

Authoring interfaces like Arria allow people to do what they’re good at: logically structuring compelling storylines and crafting creative, nonrepetitive text. But they also require some new ways of thinking about writing. For instance, template writers need to approach a story with an understanding of what the available data could say – to imagine how the data could give rise to different angles and stories, and delineate the logic to drive those variations.

Supervision, management or what journalists might call “editing” of automated content systems are also increasingly occupying people in the newsroom. Maintaining quality and accuracy is of the utmost concern in journalism.

RADAR has developed a three-stage quality assurance process. First, a journalist will read a sample of all of the articles produced. Then another journalist traces claims in the story back to their original data source. As a third check, an editor will go through the logic of the template to try to spot any errors or omissions. It’s almost like the work a team of software engineers might do in debugging a script – and it’s all work humans must do, to ensure the automation is doing its job accurately.

Developing human resources

Initiatives like those at the Associated Press and at RADAR demonstrate that AI and automation are far from destroying jobs in journalism. They’re creating new work – as well as changing existing jobs. The journalists of tomorrow will need to be trained to design, update, tweak, validate, correct, supervise and generally maintain these systems. Many may need skills for working with data and formal logical thinking to act on that data. Fluency with the basics of computer programming wouldn’t hurt either.

As these new jobs evolve, it will be important to ensure they’re good jobs – that people don’t just become cogs in a much larger machine process. Managers and designers of this new hybrid labor will need to consider the human concerns of autonomy, effectiveness and usability. But I’m optimistic that focusing on the human experience in these systems will allow journalists to flourish, and society to reap the rewards of speed, breadth of coverage and increased quality that AI and automation can offer.

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WASHINGTON: For its updated news application, Google is doubling down on the use of artificial intelligence as part of an effort to weed our disinformation and help users get viewpoints beyond their own “filter bubble.”

Google chief Sundar Pichai, who unveiled the updated Google News earlier this month, said the app now “surfaces the news you care about from trusted sources while still giving you a full range of perspectives on events.”

It marks Google’s latest effort to be at the center of online news and includes a new push to help publishers get paid subscribers through the tech giant’s platform.

According to product chief Trystan Upstill, the news app “uses the best of artificial intelligence to find the best of human intelligence — the great reporting done by journalists around the globe.”

While the app will enable users to get “personalized” news, it will also include top stories for all readers, aiming to break the so-called filter bubble of information designed to reinforce people’s biases.

“Having a productive conversation or debate requires everyone to have access to the same information,” Upstill said.

He said the “full coverage” feed would be the same for everyone — “an unpersonalized view of events from a range of trusted news sources.”

Some journalism industry veterans were skeptical about the effort to replace human editors with machine curators.

“There’s been a fantasy of (algorithmic) personalized news for a log time,” said New York University journalism professor Meredith Broussard.

“Nobody has ever gotten it right. I think that news designers and home page editors do a good job of curating already.”

Google and Facebook have also been criticized for scooping up most online ad revenues and for enabling false information to spread.

Recently, News Corp. CEO Robert Thomson called for an “algorithm review board” that would “oversee these historically influential digital platforms and ensure that there is no algorithmic abuse or censorship.”
Changing news ecosystem 
In the new app, Google’s “newsstand” addresses some concerns by allowing users to sign up for subscriptions using their Google accounts, and will enable publishers to connect with readers directly.

Dan Kennedy, a Northeastern University journalism professor, said the revamped news app appears to be positive for the news ecosystem.

“For many years, Google refused to share ad revenues with news organizations on the grounds that Google was driving traffic to them, and that it was up to those organizations to figure out how to monetize that traffic,” Kennedy said.

“Now, with more than 90 percent of all new digital ad revenues going to Google and Facebook, Google is finally acknowledging that it’s time to try something else.”

Nicholas Diakopoulos, a Northwestern University professor specializing in computational and data journalism, said the impact of Google’s changes remain to be seen.

Diakopoulos said algorithmic and personalized news can be positive for “engagement” but may only benefit a handful of news organizations.

His research found that “Google concentrates its attention on a relatively small number of publishers, it’s quite concentrated.”

Google’s effort to identify and prioritize “trusted” news sources may also be problematic, according to Diakopoulos.

“Maybe it’s good for the big guys, or the (publishers) who have figured out how to game the algorithm,” he said. “But what about the local news sites, what about the new news sites that don’t have a long track record?”

Diakopoulos said that while AI can offer some advantages in news curation, “you still need people involved in many different ways. you need to reflect human values, editorial values… you can’t quantify every aspect that might be important to an editorial decision.”

The growing importance of the platforms raise the questions of whether Google and Facebook are “media” companies and not simply technology firms, a moniker both have resisted.

Diakopoulos said Google “is becoming more and more like a media company,” although that is sometimes difficult to define.

“Yahoo started as a tech company and became a media company, and maybe Google is headed in that direction,” he said.

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