Though it could expand his outlet’s reach — especially, in his words, as email becomes increasingly important “as a distribution channel with social media declining as a traffic source” — Brodbeck didn’t think creating an additional newsletter was an optimal use of reporter time in the zero-sum, resource-strapped reality of running a hyperlocal news outlet.

“As much as I would love to have a 25-person newsroom covering Northern Virginia, the reality is that we can only sustainably afford an editorial team of eight across our three sites: two reporters/editors per site, a staff [photographer], and an editor,” he said. In short, tapping a reporter to write a morning newsletter would limit ARLnow’s reporting bandwidth.

But with the exponential improvement of AI tools like GPT-4, Brodbeck saw an opportunity to have it both ways: He could generate a whole new newsletter without cutting into journalists’ reporting time. So last month, he began experimenting with a completely automated weekday morning newsletter comprising an AI-written introduction and AI summaries of human-written stories. Using tools like Zapier, Airtable, and RSS, ARLnow can create and send the newsletter without any human intervention.

Since releasing the handbook, Amditis has heard that many publishers and reporters “seem to really appreciate the possibility and potential of using automation for routine tasks,” he told me in an email. Like Brodbeck and others, he believes “AI can save time, help small newsrooms scale up their operations, and even create personalized content for their readers and listeners,” though he raised the widely held concern about “the potential loss of that unique human touch,” not to mention the questions of accuracy, reliability and a hornets’ nest of ethical concerns.

Even when instructing AI to summarize content, Amditis described similar challenges to those Brodbeck has encountered. There’s “a tendency for the summaries and bullet points to sound repetitive if you don’t create variables in your prompts that allow you to adjust the tone/style of the responses based on the type of content you’re feeding to the bot,” he said.

But “the most frustrating part of the work I’ve been doing with publishers of all sizes over the last few months is the nearly ubiquitous assumption about using AI for journalism (newsletters or otherwise) is that we’re out here just asking the bots to write original content from scratch — which is by far one of the least useful applications, in my opinion,” Amditis added.

Brodbeck agrees. “AI is “not a replacement for original local reporting,” he said. “It’s a way to take what has already been reported and repackage it so as to reach more readers.”

When not covering AI for the FT, Murgia is finishing her first book, Code-Dependent, out in February 2024. We caught up via email. Our back-and-forth has been lightly edited for clarity and that British proclivity for the letter “zed.”

In its global survey on journalism and artificial intelligence, “New Powers, New Responsibilities,” researchers asked 71 news organizations from 32 countries if and how that currently use AI in their newsrooms and how they expect the technology to impact the news media industry. (Since what exactly constitutes AI can be fuzzy, the report defines it as “a collection of ideas, technologies, and techniques that relate to a computer system’s capacity to perform tasks normally requiring human intelligence.”)

Amazing and inspiring! Our smart news assistant Voitto highlighted in great company in just published #JournalismAI report https://t.co/FYJTotL9kU by @CharlieBeckett @PolisLSE @LSEnews and @GoogleNewsInit Big thanks for the team @yleuutiset @Yleisradio! #journalism #ai pic.twitter.com/WLiZtbIeTR

— Jarno M. Koponen (@ilparone) November 18, 2019

Right now, newsrooms mostly use AI in three areas: news gathering, production, and distribution. Of those surveyed, only 37 percent have an active AI strategy. The survey found that while newsrooms were interested in AI for efficiency and competitive purposes, they said they were mostly motivated by the desire to “help the public cope with a world of news overload and misinformation and to connect them in a convenient way to credible content that is relevant, useful and stimulating for their lives.”

“The hope is that journalists will be algorithmically turbo-charged, capable of using their human skills in new and more effective ways,” Polis founding director Charlie Beckett said in the report. “AI could also transform newsrooms from linear production lines into networked information and engagement hubs that give journalists the structures to take the news industry forward into the data-driven age.”

While most respondents said that AI would be beneficial as long as newsrooms stuck to their ethical and editorial policies, they noted that budget cuts as a result of implementing AI could lower the quality of news produced. They were also concerned about algorithmic bias and the role that technology companies will play in journalism going forward.

“AI technologies will not save journalism or kill it off,” Beckett writes. “Journalism faces a host of other challenges such as public apathy and antipathy, competition for attention, and political persecution…Perhaps the biggest message we should take from this report is that we are at another critical historical moment. If we value journalism as a social good, provided by humans for humans, then we have a window of perhaps 2-5 years, when news organisations must get across this technology.”

Here’s a video summary of the report:

And here is a brief response to the report from Johannes Klingebiel of Süddeutsche Zeitung.

A strategy for #AI adoption will vary depending on the nature of the news organisation. However, the #JournalismAI survey shows there are a few key key elements everyone should consider.

Read the report in full at: https://t.co/7sHArSUaPE pic.twitter.com/47qtPtY09U

— Polis@LSE (@PolisLSE) November 18, 2019

From the cited report: "…news organisations have to change (again). They need to adopt some form of AI strategy. They need to change their workflows, systems, and recruitment. Even then, it’s going to be tough." https://t.co/c8ChRn5uu9

— Aaron Bradley (@aaranged) November 18, 2019

One of the wisest things I have read on this subject, from thoughtful @CharlieBeckett at LSE on AI in #journalism: "we need to know what we are talking about when we talk about AI”. https://t.co/cmZqJppVhn

— PeterBale (@PeterBale) November 18, 2019

Half of respondents use AI for news gathering, two-thirds for production and just over half use AI for distribution. Read the findings of this global survey of journalism and artificial intelligence made by ⁦@CharlieBeckett⁩ at ⁦@PolisLSE⁩ https://t.co/fRTqkZ852u

— Alice Antheaume (@alicanth) November 18, 2019

"No, the robots are not going to take over journalism. Yes, the machines might soon be able to do much routine
journalism labour." This is how starts "Journalism AI" report. The first research on how journalism is using #AI in the newsroom. Check it out: https://t.co/ZSqneQth3s

— Beatriz Gálvez (@beaglvz) November 18, 2019

So whether intentionally or not, the creators of the text-generating algorithm GPT-2 played the PR game brilliantly in February when they announced that, well, it just may be too powerful to release to the general public. That generated a wave of global publicity that is, shall we say, uncommon for new text-generating algorithms. (Elon Musk is involved, you’ll be shocked to learn.)

In any event, now, nine months later, the folks at OpenAI have apparently decided that the infopocalypse is not right around the corner and released its secret superweapon GPT-2 into the wild. They say they have “seen no strong evidence of misuse so far” from more limited releases of the technology.

We're releasing the 1.5billion parameter GPT-2 model as part of our staged release publication strategy.
– GPT-2 output detection model: https://t.co/PX3tbOOOTy
– Research from partners on potential malicious uses: https://t.co/om28yMULL5
– More details: https://t.co/d2JzaENiks pic.twitter.com/O3k28rrE5l

— OpenAI (@OpenAI) November 5, 2019

The alleged threat is not, as some journalists have feared, that this machine is going to eventually cover city council meetings and aggregate viral tweets more efficiently than a human reporter could. It’s that the ease of generating semi-convincing output could make it efficient to pump out thousands or millions of individually tailored misinformation, spam emails, or some other text-based form of grift.

I suppose that’s something legitimate to worry about, but my experiences playing around with the GPT-2 has mostly matched this guy’s: Creating readable prose, sensical prose, and believable prose are still pretty different things.

The full GPT-2 model is finally out.

I dunno, guys. pic.twitter.com/k5dP8V2sa5

— Tomer Ullman (@TomerUllman) November 5, 2019

That something sounds/reads like it was generated by GPT-2 is an interesting new kind of an insult.

— Andrej Karpathy (@karpathy) November 6, 2019

To test out its capabilities, I thought I’d feed it the ledes of a few Nieman Lab stories and see what it autogenerated from there. For each of these below, the actual human-written text is in italics; everything after that is “by” GPT-2. (Note that GPT-2, as used on Talk to Transformer, often ends its output mid-sentence.)

Original story: “Meet The Salt Lake Tribune, 501(c)(3): The IRS has granted nonprofit status to a daily newspaper for the first time,” by Christine Schmidt, published Nov. 4.

Verdict: This is in the broad ballpark of news prose style. It’s interesting it chose to contradict the lede so quickly — specifically saying it rejected 501(c)(3) status. The real Fraser Nelson wouldn’t like that quote, I imagine. Call it a B+.

Original story: “Watch your language: ‘Data voids’ on the web have opened a door to manipulators and other disinformation pushers,” by me, published Nov. 1.

Verdict: Choppy short sentences without much flow or sense, despite sticking in the general topic area. I was sorta hoping it would throw in some more Spanish. I’ll give it a C-.

Original story: “The Daily Wire and black salve show that Facebook’s takedown policies have one thing in common: Inconsistency,” by Laura Hazard Owen, published Nov. 1.

Verdict: Pretty realistic at the sentence level, and maybe the self-contradicting “The problem is that the right-wing site has a left-leaning bias” is actually a galaxy-brain commentary on our current politics. A solid B.

Original story: “Why I’m starting a company to build out a new model for local news in the 2020s,” by Ken Doctor, published Oct. 25.

Verdict: Um, no. There’s no mention of Germany in Ken’s piece and I can’t quite figure out what would make the algorithm want to jump straight to Snowden and Merkel. The fractured lines would make this easy to spot. F.

And finally, to go full ouroboros, let’s feed the lede of this story right here into GPT-2.

Original story: This one, silly

Verdict: I’m sure it was right about to pivot back to text-generation algorithms in a couple more grafs. But this is a very reasonable continuation of the lede (other than that first sentence). B.

GPT-2 is not coming to take the jobs of journalists, as some have worried. Paid reporting jobs generally require a certain level of factuality that the algorithm can’t match.

Is it coming for the “jobs” of fake-news writers, those Macedonian teens who until now have had to generate their propaganda (gasp!) by hand? Probably not. Whether your intention is to make money off ad arbitrage or to elect Donald Trump as president of the United States, the key value-add comes in knowing how to exploit a reader’s emotions, biases, preconceptions, and other lizard-brain qualities that can make a lie really hit home. Baiting that hook remains something an algorithm can reliably do. And it’s not as if “lack of realistic writing in grafs 3 through 12” was a real problem limiting most misinformation campaigns.

But I can see some more realistic impacts here. This quality of generated text could allow you to create a website will what appear to be fully fleshed out archives — pages and pages of cogent text going back years — which might make it seem more legitimate than something more obviously thrown together.

GPT-2’s relative mastery of English could give foreign disinformation campaigns a more authentic sounding voice than whatever the B-team at the Internet Research Agency can produce from watching Parks & Rec reruns.

And the key talent of just about any algorithm is scale — the ability to do something in mass quantities that no team of humans could achieve. As Larry Lessig wrote in 2009 (and Philip Bump reminded us of this week), there’s something about a massive data dump that especially encourages the cherry-picking of facts (“facts”) to support one’s own narrative. Here’s Bump:

In October 2009, he wrote an essay for the New Republic called “Against Transparency,” a provocative title for an insightful assessment of what the Internet would yield. Lessig’s argument was that releasing massive amounts of information onto the Internet for anyone to peruse — a big cache of text messages, for example — would allow people to pick out things that reinforced their own biases…

Lessig’s thesis is summarized in two sentences. “The ‘naked transparency movement’…is not going to inspire change,” he wrote. “It will simply push any faith in our political system over the cliff”…

That power was revealed fully in the 2016 election by one of the targets of the Russia probe: WikiLeaks. The group obtained information stolen by Russian hackers from the Democratic National Committee and Hillary Clinton’s campaign chairman, John Podesta…In October, WikiLeaks slowly released emails from Podesta…Each day’s releases spawned the same cycle over and over. Journalists picked through what had come out, with novelty often trumping newsworthiness in what was immediately shared over social media. Activists did the same surveys, seizing on suggestive (if ultimately meaningless) items. They then often pressured the media to cover the stories, and were occasionally successful…

People’s “responses to information are inseparable from their interests, desires, resources, cognitive capacities, and social contexts,” Lessig wrote, quoting from a book called “Full Disclosure.” “Owing to these and other factors, people may ignore information, or misunderstand it, or misuse it.”

If you wanted to create something as massive as a fake cache of hacked emails, GPT-2 would be of legitimate help — at least as a starting point, producing something that could then be fine-tuned by humans.

The key fact of the Internet is that there’s so much of it. Too much of it for anyone to have a coherent view. If democracy requires a shared set of facts — facts traditionally supplied by professional journalists — the ability to flood the zone with alternative facts could take the bot infestation of Twitter and push it out to the broader world.

Well, I’m not going to say we avoided doom or gloom entirely — but we did get to have a fruitful discussion of some of the more tech-forward ways the industry is changing. In particular:

— Will blockchain meaningfully change the fundamental questions about how we journalism gets funded? (I’m skeptical.)

— Will AI and bots replace reporters? (Maybe on the fringes, but they’re mainly for scale and speed.)

— What is Apple News planning? (Dunno, but I’m hopeful the mobile OS companies can play a more useful role in news than Facebook does.)

It’s a fun conversation, and I hope you’ll give it a listen here.

The robots may still be a few years away, but the conversation is happening now (at least about today’s AI technology in newsrooms). At Al Jazeera’s Future of Media Leaders’ Summit earlier this month, a group of experts in areas from media to machine learning discussed how their organizations frame the ethics behind (and in front of!) artificial intelligence.

Can a #drone drop a robot into a war zone to report? Doesn’t take a meal break- but can’t empathise with a story! Relationship between man and machine- our future co workers #FMLSummit @AlJazeera pic.twitter.com/Bs2NLWcoNp

— Morwen Williams (@morwenw) March 5, 2018

Ethical AI was one of several topics explored during the gathering in Qatar, focused on data security, the cloud, and how artificial intelligence can automate and augment journalism. (“Data has become more valuable than oil,” Mohamed Abuagla told the audience in the same presentation as the drone-reporter concept.)

AI has already been seeded into the media industry, from surfacing trends for story production to moderating comments. Robotic combat correspondents may still be a far-fetched idea. But with machine learning strengthening algorithms day by day and hour by hour, AI innovations are occurring at a breakneck pace. Machines are more efficient than humans, sure. But in a human-centric field like journalism, how are newsrooms putting AI ethics into practice?

Ali Shah, the BBC’s head of emerging technology and strategic direction, explained his approach to the moral code of AI in journalism. Yaser Bishr, Al Jazeera Media Network’s executive director of digital, also shared some of his thinking on the future of AI in journalism. Here are some of the takeaways:

Ali Shah, the BBC

In both his keynote speech and subsequent panel participation, Shah walked the audience through the business and user implications of infusing AI into parts of the BBC’s production processes. He continued returning to the question of individual agency. “Every time we’re making a judgment about when to apply [machine learning]…what we’re really doing is making a judgment about human capacity,” he said. “Was it right for me to automate that process? When I’m talking about augmenting someone’s role, what judgment values am I augmenting?”

Shah illustrated how the BBC has used AI to perfect camera angles and cuts when filming, search for quotes in recorded data more speedily, and make recommendations for further viewing when the credits are rolling on the BBC’s online player. (The BBC and Microsoft have also experimented with a voice interface AI.) But he emphasized how those AI tools are intended to automate, augment, and amplify human journalists’ work, not necessarily replace or supersede them. “Machine learning is not going to be the answer to every single problem that we face,” he said.

The BBC is proud to be one of the world’s most trusted news brands, and Shah pointed to the need for balance between trust in the organization and individual agency. “We’re going to have to strike a balance between the utility and the effectiveness and the role it plays in society and in our business,” he said. “What we need to do is constantly recognize [that] our role should be giving a little bit of control back to our audience members.”

He also spoke about the need to educate both the engineers designing the AI and the “masses” who are the intended consumers of it. “Journalists are doing a fantastic job at covering this topic,” he said, but “our job as practitioners is to…break this down to the audience so they have control about how machine learning and AI are used to impact them.” (The BBC has published explainer videos about the technology in the past.) “We have to remember, as media, we are gatekeepers to people’s understanding of the modern world.”

“The use of AI changes our behavior. Decisions are influenced and social norms change/evolve but we must make sure we are consciously aware of the effects of machine learning.”- Ali Shah of @BBC at #FMLSummit #AI

— Al Jazeera PR (@AlJazeera) March 6, 2018

“It’s not about slowing down innovation but about deciding what’s at stake,” Shah said. “Choosing your pace is really important.”

Yaser Bishr, Al Jazeera Media Network

Bishr, who helped bring AJ+ to life and has since used Facebook to pull followers onto Al Jazeera’s new Jetty podcast network, also emphasized the need to tread carefully.

“The speed of evolution we are going through in AI far exceeds anything we’ve done before,” Bishr said, talking about the advancements made in the technology at large. “We’re all for innovation, but I think the discussion about regulating the policy needs to go at the same pace.”

In conversation with Shah, Rainer Kellerhais of Microsoft, and Ahmed Elmagarmid of the Qatar Computing Research Institute, Bishr reiterated the risks of AI algorithms putting people into boxes and cited Microsoft’s exiled Twitter bot as an example of input and output bias. “The risk is not only during the training of the machine, but also during the execution of the machine,” he said.

Dr Yaser Bishr of @ALJazeera: “In the context of generating news and information, there is some degree of bias because the info is gathered by a human so there is that editorial interference” #FMLSummit #journalism #AI

— Al Jazeera PR (@AlJazeera) March 6, 2018

Elmagarmid countered his concern about speed: “Things are in motion but things are continuous,” he said calmly. “We have time to adapt to it. We have time to harness it. I think if we look back to the Industrial Revolution, look back to the steam engine…people are always perceiving new technology as threatening.

“At the end of the day you will have [not just] newsrooms, but much better and more efficient and smarter newsrooms,” Elmagarmid said.

“AI is not the Industrial Revolution,” Bishr said, adding to his earlier comments: “We’re not really in a hurry in using AI right now.”

There are already elements of this in quite a few newsrooms but this is the first announcement (I’ve seen) of a large news org rearranging itself around AI…. https://t.co/oK7pZbj158

— emily bell (@emilybell) January 10, 2018

Xinhua News Agency president Cai Mingzhao said Xinhua will build a “new kind of newsroom based on information technology and featuring human-machine collaboration.” The agency has also introduced the “Media Brain” platform to integrate cloud computing, the Internet of Things, AI and more into news production, with potential applications “from finding leads, to news gathering, editing, distribution and finally feedback analysis.”

The agency’s announcement was sparse on details, but it’s the latest component of a deep push into AI by China. Last week the country announced plans for a $2.1 billion AI development park to be built in the next five years as part of its drive to become an AI world leader by 2030. Google has also committed to putting roots in China’s AI scene by opening a research center in Beijing, with Bloomberg quoting Google’s leader of the center Fei-Fei Li: “It will be a small team focused on advancing basic AI research in publications, academic conferences and knowledge exchange.” Microsoft also announced plans to create their own R&D lab for AI in Taiwan and hire 200 researchers over the next five years, investing about $34 million.

“We saw lots of interest in AI in China, and the sector is moving so fast in the country,” Chris Nicholson, former Bloomberg news editor and co-founder of AI startup Skymind, told Digiday. “Beijing supports AI, while Baidu, Alibaba and Tencent are all getting into AI. The U.S. still has the best AI talent, but there are many good engineers and AI researchers in China as well.”

Moving aside from the global AI armsrace, the reverberations from China investing in AI-media could echo in journalism worldwide. A report out today from the Reuters Institute’s Digital News Project on media trends for 2018 highlights some of the advances that Chinese AI journalism has made already:

Executive Editor of Quartz, Zach Seward, recently gave a speech in China at a conference organised by tech giant Tencent. This was turned into a news story by a combination of AI based speech to text software, automatic transcription, and an automated newswriting programme called Dreamwriter. Around 2,500 pieces of news on finance, technology, and sports are created by Dreamwriter daily.

Machine-translated English version of the machine-written Chinese article about a human speech in English https://t.co/01uICBfTR0

— Zach Seward (@zseward) November 19, 2017

Video of Dreamwriter hard at work on the side of the stage during another speech pic.twitter.com/vWBgwfqKRz

— Zach Seward (@zseward) November 19, 2017

With technology having saturated Western markets much of the opportunity for growth is shifting to markets like China and India. But Silicon Valley giants like Google and Facebook face restrictions in China in particular, leaving Asian tech firms driving new ideas at a relentless pace. Without a computer-based legacy to worry about, this is a part of the world that is able to fully embrace mobile first technologies. Increasingly we’ll be looking to the East for innovations in technology in 2018…

Frederic Filloux, author of the Monday Note, has been paying close attention to Toutiao, an app that uses artificial intelligence to aggregate content from around 4,000 traditional news providers as well as bloggers and other personal content. Toutiao has around 120m daily active users and an engagement time of 74 minutes per day. Newsfeeds are constantly updated based on what its machines have learnt about reading preferences, time spent on an article, and location. Toutiao claims to have a user figured out within 24 hours. In Korea, Naver is also looking to add AI recommendations to its mobile services. Line is another mobile news aggregator that is popular in Korea, Taiwan, and Japan. News aggregators like Flipboard and Laserlike48 have made little progress in the US and Europe. But that could change as Toutiao, leaning on its $22 billion valuation, is looking to move aggressively into the Western countries this year.

Chinese companies aren’t necessarily expanding internationally for an international advertising base; as Axios’ Sara Fischer points out, they’re interested in targeting Chinese nationals who have moved elsewhere but still use the same technology to stay in touch back home.

There are also some concerns about what the Chinese government could do with AI journalism: Nina Xiang, the co-founder of the artificial intelligence-based China Money Network, wondered about the potential security and privacy issues from Xinhua’s innovations. “The Media Brain…will raise significant concerns over the protection of personal data privacy, or the lack thereof. The tie-up between Alibaba and China’s state news agency — the first of its kind — creates an all-seeing digital eye that can potentially access data collected from countless surveillance cameras across the nation, estimated to total half a billion in the next three years, Internet of Things (IoT) devices, dashboard-mounted car cameras, air pollution monitoring stations and personal wearable devices. Whether people will be able to give permission for their data being used, or even know its being used, is questionable,” she wrote.

“To use a simple analogy, this partnership is as if Amazon, Paypal, CBS, News Corp and Fox were all working with state and city governments in the United States to share both publicly and privately collected data for the purpose of monitoring for potential news events anywhere, anytime in real time across America.”

While some American organizations are slowly introducing AI to their newsrooms, China’s Xinhua is going all in.

There’s never a shortage of fascinating scholarship in the digital news/social media space. This year, we’re spotlighting 10 of the most compelling academic articles and reports published in 2017, which delve into meaty topics such as venture-backed startups, artificial intelligence, personal branding, and the spread of disinformation. We conferred with a small group of scholars to pick the ones we think you’ll want to know about — and remember, this is just a sample. A big thank you to everybody who contributed suggestions on Twitter.

“Paying for Online News: A comparative analysis of six countries”: From the University of Oxford, published in Digital Journalism. By Richard Fletcher and Rasmus Kleis Nielsen.

This study offers both good news and bad news for publishers struggling to figure out pay models. The researchers used data collected via surveys in six countries, including the United States, to gauge who’s paying for news and who’s willing to pay in the future. The good news: Of those who are not paying for online news now, younger Americans are more willing to pay in the future, possibly because they often already pay for other forms of digital media. The bad news: No more than 2 percent of people surveyed in any country said they are “very likely” to pay for news in the future.

“News Use Across Social Media Platforms 2017”: From the Pew Research Center. By Elisa Shearer and Jeffrey Gottfried.

Throughout the year, the Pew Research Center releases survey-based reports examining journalism and news organizations. This report offers important insights into the role social media plays in distributing and accessing news. Some key takeaways: Almost 70 percent of U.S. adults reported getting news via social media. Meanwhile, a growing number of older adults, people of color, and adults without bachelor’s degrees said they turn to social media sites for news. Minority adults are much more likely than white adults to get news from social media — 74 percent reported doing so in 2017, up from 64 percent in 2016. Interestingly, only 5 percent of adults who go to Snapchat for news also often get news from newspapers.

“Venture-Backed News Startups and the Field of Journalism: Challenges, changes, and consistencies”: From George Washington University, published in Digital Journalism. By Nikki Usher.

How do venture-backed news startups compare themselves to traditional media outlets? This article examines 18 startups, including BuzzFeed, GeekWire, and Vox, to understand how this burgeoning area of digital media is changing journalism’s landscape. Usher interviewed top executives, founders, and others to learn how and why these companies formed as well as details about their editorial visions, technological visions, and plans for making money. The study also explores the rise of algorithms in predicting user behavior, the creation of scalable products, and new roles for journalists within an organization where reporters and technical staff are equals.

“Technology Firms Shape Political Communication: The Work of Microsoft, Facebook, Twitter, and Google With Campaigns During the 2016 U.S. Presidential Cycle”: From the University of North Carolina at Chapel Hill and The University of Utah, published in Political Communication. By Daniel Kreiss and Shannon C. McGregor.

This article offers a behind-the-scenes look at how Facebook, Google, Microsoft, and Twitter collaborated with political campaigns during the 2016 U.S. election season. The paper focuses on their role at the Democratic National Convention in 2016 and in providing extensive consulting services to candidates, including Donald Trump, over the course of the campaign. The researchers found that these technology firms “are increasingly the locus of political knowledge and expertise” in digital and data campaigning. Meanwhile, representatives from each firm said “the growth of their work in electoral politics was driven by the desire for direct revenues from their services and products, for candidates to give their services and platforms greater public visibility, and to establish relationships with legislators.”

“When Reporters Get Hands-On With Robo-Writing: Professionals consider automated journalism’s capabilities and consequences”: From LMU Munich and the University of Zurich, published in Digital Journalism. By Neil Thurman, Konstantin Dörr, and Jessica Kunert.

Media innovators continue to find new ways to integrate artificial intelligence into the newsroom, moving well past using crime stats and structured data from athletic games to generate news reports. While plenty of journalists have weighed in on the trend, most don’t have direct experience using the technology. For this study, researchers conducted workshops with a small group of journalists to show them how to use software to create data-driven news content. After getting hands-on experience, journalists were asked about the potentials and limitations of the technology.

Unsurprisingly, journalists had lots of criticisms — for example, there was concern that automation would make verifying information less likely. Some journalists did see benefits, including time savings and reductions in human error. For several, though, “the experience of creating news items this way was difficult, irritating, and did not utilize their innate abilities.”

“Artificial Intelligence: Practice and Implications for Journalism”: From the Brown Institute for Media Innovation and the Tow Center for Digital Journalism at Columbia Journalism School. By Mark Hansen, Meritxell Roca-Sales, Jon Keegan, and George King.

What problems do journalists and technologists uncover when they brainstorm about AI in newsrooms? This report summarizes a three-hour, wide-ranging discussion between journalists and technologists who gathered last summer for an event organized by Columbia University’s Tow Center for Digital Journalism and the Brown Institute for Media Innovation. Among the important takeaways: A knowledge and communication gap between the technologists who create AI and journalists who use it could “lead to journalistic malpractice.” News outlets need to provide audiences with clear explanations for how AI is used to research and report stories. Also, there needs to be “a concerted and continued effort to fight hidden bias in AI, often unacknowledged but always present, since tools are programmed by humans.”

“Partisanship, Propaganda, and Disinformation: Online Media and the 2016 U.S. Presidential Election”: From the Berkman Klein Center for Internet & Society at Harvard University. By Rob Faris, Hal Roberts, Bruce Etling, Nikki Bourassa, Ethan Zuckerman, and Yochai Benkler.

In this report, researchers examine the composition and behavior of media on the right and left to explain how Donald Trump and Hillary Clinton received differing coverage. The report covers a lot of ground in 142 pages, chock-full of bar charts, network maps, and other data visualizations. It even includes a case study on coverage of the Clinton Foundation. The researchers found that while mainstream media gave mostly negative coverage to both presidential candidates, Trump clearly dominated coverage and was given the opportunity to shape the election agenda.

According to the report, far-right media “succeeded in pushing the Clinton Foundation to the front of the public agenda precisely at the moment when Clinton would have been anticipated to (and indeed did) receive her biggest bounce in the polls: immediately after the Democratic convention.” Researchers also found that while fake news was a problem, it played a relatively small role in the 2016 presidential election. “Disinformation and propaganda from dedicated partisan sites on both sides of the political divide played a much greater role in the election,” the researchers wrote.

“Identity lost? The personal impact of brand journalism”: From The University of Utah and Temple University, published in Journalism. By Avery E. Holton and Logan Molyneux.

Newsrooms urge journalists to use social media to promote their work, interact with sources, and build their professional brands. How does that affect what journalists do on Twitter and Facebook when they’re off the clock? This study is one of several published in 2017 that look at how social media impacts journalists’ identities. This one is important because it lays the groundwork for the others. The authors interviewed 41 reporters and editors at U.S. newspapers to explore the challenges they face in integrating their personal and professional identities on social media. They found that reporters “feel pressure to stake a claim on their beat, develop a presence as an expert in their profession and area of coverage, and act as a representative of the news organization at all times. This leaves little room for aspects of personal identity such as family, faith, or friendship to be shared online.”

“How the news media activate public expression and influence national agendas”: From Harvard University, Florida State University, and MIT, published in Science. By Gary King, Benjamin Schneer, and Ariel White.

Journalism really does contribute to the democratic process and this study provides quantitative evidence. In an experiment involving 48 mostly small media organizations, researchers demonstrated that reporting on a certain policy topic prompts members of the public to take a stand and express their views on the topic more often than they would have if a news article had not been published. Researchers looked at website pageviews and social media posts to gauge impact. Their experiment, according to the researchers, “increased discussion in each broad policy area by ~62.7% (relative to a day’s volume), accounting for 13,166 additional posts over the treatment week.”

“Digital News Report 2017”: From the Reuters Institute for the Study of Journalism at the University of Oxford. By Nic Newman, Richard Fletcher, Antonis Kalogeropoulos, David A. L. Levy, and Rasmus Kleis Nielsen.

This latest annual report from the Reuters Institute offers a global look at digital news consumption based on a survey of more than 70,000 people in 36 countries, including the United States. There are lots of great insights to glean from this 136-page report, which examines such issues as news avoidance, access, distrust, polarization, and sharing. It may (or may not) be surprising that the U.S. ranked 7th highest in the area of news avoidance behind Greece, Turkey, Poland, Croatia, Chile, and Malaysia. Thirty-eight percent of Americans reported avoiding the news “often” or “sometimes.”

Worldwide, the amount of sharing and commenting on news via social media has fallen or stayed about the same the past two years. The U.S., which saw small increases in both habits, is an exception. Another interesting takeaway: Some countries are much more likely to pay for news. In Norway, 15 percent of people surveyed said they made ongoing payments for digital news in the last year, compared to 8 percent in the U.S., 6 percent in Japan, 4 percent in Canada, 3 percent in the United Kingdom and 2 percent in the Czech Republic.

The Scandinavian media giant Schibsted’s annual trends report — part predictions, part survey research, part self-promotion — is out today, free for anyone interested. The report features essays on everything from the promise and pitfalls of artificial intelligence to sustainability to the future of bicycles as a consistent mode of transportation, as well as a survey of millennials in France, Spain, and Sweden on their concerns about their digital footprint. (It’s also a useful document to browse in case you’re wondering what a 7,000-employee media company considers the most important new focus areas for its business in the coming years.)

Here are a few interesting points from the report to note.

— Svenska Dagbladet, Schibsted’s Stockholm-based daily newspaper, is designing a ratings system for the relative newsworthiness of each piece of news it publishes. An algorithm, trained on that data, is helping put together its homepage:

“What is a particular piece of news worth on a scale from 1–5?” … We tested different news scenarios. Stock market down 4 percent (news value 3.5), the Prime Minister proposes more CCTV cameras in central Stockholm (news value 4.0). A Strindberg play opens at the Royal Theatre (2.5).

These news ratings, combined with a time marker, how long we think the piece will draw interest and be relevant to the readers, are the very basic data in the algorithm that from now is going to steer our new front page. It was self-evident that it is journalism and the editors that, also in the future, are going to influence how news are evaluated on our front page.

— The context in which a digital ad appears is definitely important.

A study conducted jointly by Schibsted Sales and Inventory and the Stockholm School of Economics studied the impact of ads from 16 Swedish advertisers, including the buying intentions of people who saw the ads: “On average the effect doubles with the right audience and triples with the right context and the right target group. Therefore, even when there is a lot of data about who is being reached, the context almost always trumps data, especially when it comes to getting customers to act and, not least, with lesser-known brands.”

— Millennials in France, Spain, and Sweden might love to post about their lives on social media, but they’re also wary of how the information they leave online can be used for more targeted and sometimes more nefarious purposes. This is according to a study Schibsted commissioned, looking at 1,200 people born in those three countries between 1983 and 2001.

— Sixty-seven percent of Spanish and French millennials surveyed reported worrying that “the information they provide on social media can be used to influence political views.” Fifty-five percent of Swedish millennials felt the same.

— Sixty-one percent of Spanish millennials reported being willing to give up additional personal information online in order to receive better tailored products and services. That’s true for 51 percent for French millennials and 39 percent of Swedish millennials.

— Fifty-four percent of Swedish millennials have in the past year changed their phone settings to improve their digital privacy (49 percent for Spanish millennials; 37 percent for French millennials).

There’s more in the report to read here.

A report out this week from the Tow Center for Digital Journalism looks at how AI can be adapted to journalism. It summarizes a previously off-the-record meeting held back in June by the Tow Center and the Brown Institute for Media Innovation. (Full disclosure: Nieman Lab director Joshua Benton was part of the meeting.)

Among the report’s findings:

— AI “helps reporters find and tell stories that were previously out of reach or impractical.” Three areas where AI can be particularly helpful in the newsroom: “Finding needles in haystacks” (discovering things in data that humans can’t, which humans can then fact-check); identifying trends or outliers; and as a subject of a story itself: “Because they are built by humans, algorithms harbor human bias — and by examining them, we can discover previously unseen bias.”

— AI can deliver much more personalized news — for good and bad. AI could be used to monitor readers’ likes and dislikes, ultimately shaping stories to people’s individual interests. But, as one participant cautioned:

The first stage of personalization is recommending articles; the long-term impact is filter bubbles. The next step is using NLP [Natural Language Processing] to shape an article to exactly the way you want to read it. Tone, political stance, and many other things. At that point, journalism becomes marketing. We need to be very aware that too much personalization crosses the line into a different activity.

Another concern is that, if articles become too personalized, the public record is at risk: “When everyone sees a different version of a story, there is no authoritative version to cite.”

— AI brings up new ethical considerations. Participants agreed that news organizations need to disclose when AI has been used in creating a story, but “that description must be translated into non-technical terms, and told in a concise manner that lets readers understand how AI was used and how choices were made.” There’s a need for best practices around disclosures.

Also, most AI tools aren’t built specifically with newsrooms (or their editorial values) in mind. One engineer said: “A lot of these questions currently seem impenetrable to us engineers because we don’t understand the editorial values at a deep level, so we can’t model them. Engineers don’t necessarily think of the systems they are building as embodying editorial values, which is an interesting problem. The way a system like this is built does not reflect this underlying goal.”

The full report is here.

This summer, as a Knight Visiting Nieman Fellow at Harvard, I’ve been looking at another technology I think could lead to a similar step change in how publishers relate to their audiences: AI-driven voice interfaces, such as Amazon’s Alexa, Google’s Home and Assistant, Microsoft’s Cortana, and Apple’s upcoming HomePod. The more I’ve spoken to the editorial and technical leads building on these platforms in different news organizations, as well as the tech companies developing them, the more I’ve come to this view: This is potentially bigger than the impact of the iPhone. In fact, I’d describe these smart speakers and the associated AI and machine learning that they’ll interface with as the huge burning platform the news industry doesn’t even know it’s standing on.

This wasn’t how I planned to open this piece even a week before my Nieman fellowship ended. But as I tied together the research I’d done with the conversations I’d had with people across the industry, something became clear: As an industry, we’re far behind the thinking of the technology companies investing heavily in AI and machine learning. Over the past year, the CEOs of Google, Microsoft, Facebook, and other global tech giants have all said, in different ways, that they now run “AI-first” companies. I can’t remember a single senior news exec ever mentioning AI and machine learning at any industry keynote address over the same period.

Of course, that’s not necessarily surprising. “We’re not technology companies” is a refrain I’ve heard a lot. And there are plenty of other important issues to occupy industry minds: the rise of fake news, continued uncertainty in digital advertising, new tech such as VR and AR, and the ongoing conundrum of responding to the latest strategic moves of Facebook.

But as a result of all these issues, AI is largely being missed as an industry priority; to switch analogies, it feels like we’re the frog being slowly boiled alive, not perceiving the danger to itself until it’s too late to jump out.

“In all the speeches and presentations I’ve made, I’ve been shouting about voice AI until I’m blue in the face. I don’t know to what extent any of the leaders in the news industry are listening,” futurist and author Amy Webb told me. As she put it in a piece she wrote for Nieman Reports recently:

Talking to machines, rather than typing on them, isn’t some temporary gimmick. Humans talking to machines — and eventually, machines talking to each other — represents the next major shift in our news information ecosystem. Voice is the next big threat for journalism.

My original goal for this piece was to share what I’d learned — examples of what different newsrooms are trying with smart speakers and where the challenges and opportunities lie. There’s more on all that below. But I first want to emphasize the critical and urgent nature of what the news industry is about to be confronted with, and how — if it’s not careful — it’ll miss the boat just as it did when the Internet first spread from its academic cocoon to the rest of the world. Later, I’ll share how I think the news industry can respond.

Talking to objects isn’t weird any more

In the latest version of her annual digital trends report, Kleiner Perkins’ Mary Meeker revealed that 20 percent of all Google search was now happening through voice rather than typing. Sales of smart speakers like Amazon’s Echo were also increasing fast:

It’s becoming clear that users are finding it useful to interact with devices through voice. “We’re treating voice as the third wave of technology, following the point-and-click of PCs and touch interface of smartphones,” Francesco Marconi, a media strategist at Associated Press, told me. He recently coauthored AP’s report on how artificial intelligence will impact journalism. The report gives some excellent insights into the broader AI landscape, including automation of content creation, data journalism through machine learning, robotic cameras, and media monitoring systems. It highlighted smart speakers as a key gateway into the world of AI.

Since the release of the Echo, a number of outlets have tried to learn what content works (or doesn’t) on this class of devices. Radio broadcasters have been at an understandable advantage, being able to adapt their content relatively seamlessly.

In the U.S., NPR was among the first launch partners on these platforms. Ha-Hoa Hamano, a senior product manager at NPR working on voice AI, described its hourly newscast as “the gateway to NPR’s content.”

“We’re very bullish on the opportunity with voice,” Hamano said. She cited research showing 32 percent of people aged 18 to 34 don’t own a radio in their home — “which is a terrifying stat when you’re trying reach and grow audience. These technologies allow NPR to fit into their daily routine at home — or wherever they choose to listen.”

NPR was available at launch on the Echo and Google Home, and will be soon on Apple’s HomePod. “We think of the newscast as the gateway to the rest of NPR’s news and storytelling,” she said. “It’s a low lift for us to get the content we already produce onto these platforms. The challenge is finding the right content for this new way of listening.”

The API that drives NPR made it easy for Hamano’s team to integrate the network’s content into Amazon’s system. NPR’s skills — the voice-driven apps that Amazon’s voice assistant Alexa recognizes — can respond to requests like “Alexa, ask NPR One to recommend a podcast” or “Alexa, ask NPR One to play Hidden Brain.”

The Washington Post — owned by Amazon CEO Jeff Bezos — is also an early adopter in running a number of experiments on Amazon’s and Google’s smart speaker platforms. Senior product manager Joseph Price has been leading this work. “I think we’re at the early stages of what I’d call ambient computing — technology that reduces the ‘friction’ between what we want and actually getting it in terms of our digital activity,” he said. “It will actually mean we’ll spend less time being distracted by technology, as it effectively recedes into the background as soon as we are finished with it. That’s the starting point for us when we think about what voice experiences will work for users in this space.”

Not being a radio broadcaster, the Post has had to experiment with different forms of audio — from using Amazon’s Alexa automated voices on stories from its website to a Post reporter sharing a particular story in their own voice. Other experiments have included launching an Olympics skill, where users could ask the Post who had won medals during last year’s Olympics. That was an example of something that didn’t work, though — Amazon built the same capability into the main Alexa platform soon afterwards itself.

“That was a really useful lesson for us,” Price said. “We realized that in big public events like these, where there’s an open data set about who has won what, it made much more sense for a user to just ask Alexa who had won the most medals, rather than specifically asking The Washington Post on Alexa the same question.” That’s a broader lesson: “We have to think about what unique or exclusive information, content, or voice experience can The Washington Post specifically offer that the main Alexa interface can’t.”

One area that Price’s team is currently working on is the upcoming release of notifications on both Amazon’s Alexa and Google’s Home platforms. For instance, if there’s breaking news, the Post will be able to make a user’s Echo chime and flash green, at which point the user can ask “Alexa, what did I miss?” or “Alexa, what are my notifications?” Users will have to opt in before getting alerts to their device, and they’ll be able to disable alerts temporarily through a do-not-disturb mode.

Publishers like the Post that produce little or no native audio content have to work out the right way of presenting their text-based content on a voice-driven platform. One option is to allow Alexa to read stories that have been published; that’s easy to scale up. The other is getting journalists to voice articles or columns or create native audio for the platform. That’s much more difficult to scale, but several news organizations told me initial audience feedback suggests this is users’ preferred experience.

For TV broadcasters like CNN, early experiments have focused on trying to figure out when their users would most want to listen to a bulletin — as opposed to watching one — and how much time they might have specifically to do so via a smart speaker. Elizabeth Johnson, a senior editor at CNN Digital, has been leading the work on developing flash-briefing content for these platforms.

“We assumed some users would have their device in the kitchen,” she said. “This led us to ask, what are users probably doing in the kitchen in the morning? Making breakfast. How long does it take to make a bagel? Five minutes. So that’s probably the amount of time a user has to listen to us, so let’s make sure we can update them in less than five minutes. For other times of the day, we tried to understand what users might be doing: Are they doing the dishes? Are they watching a commercial break on TV or brushing their teeth? We know that we’re competing against a multitude of options, so what sets us apart?”

With Amazon’s recent release of the Echo Show — which has a built-in screen — CNN is taking the “bagel time” philosophy to developing a dedicated video news briefing at the same length as its audio equivalent.

CNN is also thinking hard about when notifications will and won’t work. “If you send a notification at noon, but the user doesn’t get home until 6 p.m., does it make sense for them to see that notification?” Johnson asked. “What do we want our users to hear when they come home? What content do we have that makes sense in that space, at that time? We already consider the CNN mobile app and Apple News alerts to be different, as are banners on CNN.com — they each serve different purposes. Now, we have to figure out how to best serve the audience alerts on these voice-activated platforms.”

What’s surprised many news organizations is how broad the age range of their audiences are on smart speakers. Early adopters in this space are very different from early adopters of other technologies. Many didn’t buy these smart speakers themselves, but were given them as gifts, particularly around Christmas. The fact there’s very little learning curve to use them means the technical bar is much lower. Speaking to the device is intuitive.

Edison Research was recently commissioned by NPR to find out more about what these users are doing with these devices. Music was unsurprisingly at the top of the reasons why they use these devices, but coming in second was to “ask questions without needing to type.” Also high up was an interest to listen to news and information — encouraging for news organizations.

While screens aren’t going away — people will always want to see and touch things — there’s no doubt that voice as an interface for devices is already becoming ingrained as a natural behavior among our audiences. If you’re not convinced, watch children interact with smart speakers: Just as we’ve seen the first Internet-connected generation grow up, we’re about to see the “voice generation” arrive feeling completely at ease with this way of engaging with technology.

The NPR–Edison research has also highlighted this trend. Households with kids that have smart speakers say engagement is high with these devices. Unlike phones or tablet, smart speakers are communal experiences — which also raises the likelihood of families spending time together, whether for education or entertainment purposes.

(It’s worth noting here that there have been some concerns raised about whether children asking for — or demanding — content from a device without saying “please” or “thank you” could have downsides. As San Francisco VC and dad Hunter Walk put it last year: “Amazon Echo is magical. It’s also turning my kid into an asshole.” To curb this, skills or apps for children could be designed in the future with voice responses requiring politeness.)

For the BBC, where I work, developing a voice-led digital product for children is an exciting possibility. It already has considerable experience of content for children on TV, radio, online and digital.

“Offering the ability to seamlessly navigate our rich content estate represents a great opportunity for us to forge a closer relationship with our audience and to serve them better,” Ben Rosenberg, senior distribution manager at the BBC, said. “The current use cases for voice suggest there is demand that sits squarely in the content areas where we consistently deliver on our ambitions — radio, news, and children’s genres.”

BBC News recently formed a working group to rapidly develop prototypes for new forms of digital audio using voice as the primary interface. Expect to hear more about this in the near future.

Rosenberg also highlights studies that have found voice AI interfaces appeared to significantly increase consumption of audio content. This is something that came out strongly in the NPR-Edison research too:

Owning a smart speaker can lead to a sizeable increase in consumption of music, news and talk content, podcasts, and audiobooks. Media organizations that have such content have a real opportunity if they can figure out how to make it as easily accessible through these devices as possible. That’s where we get to the tricky part.