To put it mildly, news media has been on the sidelines in AI development. As a consequence, in the age of AI-powered personalized interfaces, the news organizations don’t anymore get to define what’s real news, or, even more importantly, what’s truthful or trustworthy. Today, social media platforms, search engines and content aggregators control user flows to the media content and affect directly what kind of news content is created. As a result, the future of news media isn’t anymore in its own hands. Case closed?

The (Death) Valley of news digitalization

There’s a history: News media hasn’t been quick or innovative enough to become a change maker in the digital world. Historically, news used to be the signal that attracted and guided people (and advertisers) in its own right. The internet and the exponential explosion of available information online changed that for good.

In the early internet, the portals channeled people to the content in which they were interested. Remember Yahoo? As the amount of information increased, the search engine(s) took over, changing the way people found relevant information and news content online. As the mobile technologies and interfaces started to get more prominent, social media with News Feed and tweets took over, changing again the way people discovered media content, now emphasizing the role of our social networks.

Significantly, news media didn’t play an active role in any of these key developments. Quite the opposite, it was late in utilizing the rise of the internet, search engines, content aggregators, mobile experience, social media and other new digital solutions to its own benefit.

The ad business followed suit. First news organizations let Google handle searches on their websites and the upcoming search champion got a unique chance to index media content. With the rise of social media, news organizations, especially in the U.S., turned to Facebook and Twitter to break the news rather than focusing on their own breaking news features. As a consequence, news media lost its core business to the rising giants of the new digital economy.

To put it very strongly, news media hasn’t ever been fully digital in its approach to user experience, business logic or content creation. Think paywalls and e-newspapers for the iPad! The internet and digitalization forced the news media to change, but the change was reactive, not proactive. The old, partly obsolete, paradigms of content creation, audience understanding, user experience and content distribution still actively affect the way news content is created and distributed today (and to be 110 percent clear — this is not about the storytelling and the unbelievable creativity and hard work done by ingenious journalists all around the globe).

Due to these developments, today’s algorithmic gatekeepers like Google and Facebook dominate the information flows and the ad business previously dominated by the news media. Significantly, personalization and the ad-driven business logic of today’s internet behemoths isn’t designed to let the news media flourish on its own terms ever again.

From observers to change makers

News media have been reporting the rise of the new algorithmic world order as an outside observer. And the reporting has been thorough, veracious and enlightening — the stories told by the news media have had a concrete effect on how people perceive our continuously evolving digital realities.

However, as the information flows have moved into the algorithmic black boxes controlled by the internet giants, it has become obvious that it’s very difficult or close to impossible for an outside observer to understand the dynamics that affect how or why a certain piece of information becomes newsworthy and widely spread. For the mainstream news media, Trump’s rise to the presidency came as a “surprise,” and this is but one example of the new dynamics of today’s digital reality.

And here’s a paradox. As the information moves closer to us, to the mobile lock screen and other surfaces that are available and accessible for us all the time, its origins and background motives become more ambiguous than ever.

The current course won’t be changed by commenting on or criticizing the actions of the ruling algorithmic platforms.

The social media combined with self-realizing feedback loops utilizing the latest machine learning methods, simultaneously being vulnerable for malicious or unintended gaming, has led us to the world of “alternative facts” and fake news. In this era of automated troll-hordes and algorithmic manipulation, the ideals of news media sound vitally important and relevant: Distribution of truthful and relevant information; nurturing the freedom of speech; giving the voice to the unheard; widening and enriching people’s worldview; supporting democracy.

But, the driving values of news media won’t ever be fully realized in the algorithmic reality if the news media itself isn’t actively developing solutions that shape the algorithmic reality.

The current course won’t be changed by commenting on or criticizing the actions of the ruling algorithmic platforms. #ChangeFacebook is not on the table for news media. New AI-powered Google News is controlled and developed by Google, based on its company culture and values, and thus can’t be directly affected by the news organizations.

After the rise of the internet and today’s algorithmic rule, we are again on the verge of a significant paradigm shift. Machine learning-powered AI solutions will have an increasingly significant impact on our digital and physical realities. This is again a time to affect the power balance, to affect the direction of digital development and to change the way we think when we think about news — a time for news media to transform from an outside observer into a change maker.

AI solutions for news media

If the news media wants to affect how news content is created, developed, presented and delivered to us in the future, they need to take an active role in AI development. If news organizations want to understand the way data and information are constantly affected and manipulated in digital environments, they need to start embracing the possibilities of machine learning.

But how can news media ever compete with today’s AI leaders?

News organisations have one thing that Google, Facebook and other big internet players don’t yet have: news organizations own the content creation process and thus have a deep and detailed content understanding. By focusing on appropriate AI solutions, they can combine the data related to the content creation and content consumption in a unique and powerful way.

News organizations need to use AI to augment you and me. And they need to augment journalists and the newsroom. What does this mean?

Augment the user-citizen

Personalization has been around for a while, but has it ever been designed and developed in the terms of news media itself? The goal for news media is to combine great content and personalized user experience to build a seamless and meaningful news experience that is in line with journalistic principles and values.

For news, the upcoming real-time machine learning methods, such as online learning, offer new possibilities to understand the user’s preferences in their real-life context. These technologies provide new tools to break news and tell stories directly on your lock screen.

An intelligent notification system sending personalized news notifications could be used to optimize content and content distribution on the fly by understanding the impact of news content in real time on the lock screens of people’s mobile devices. The system could personalize the way the content is presented, whether serving voice, video, photos, augmented reality material or visualizations, based on users’ preferences and context.

Significantly, machine learning can be utilized to create new forms of interaction between people, journalists and the newsroom. Automatically moderated commenting is just one example already in use today. Think if it would be possible to build interactions directly on the lock screen that let the journalists better understand the way content is consumed, simultaneously capturing in real time the emotions conveyed by the story.

By opening up the algorithms and data usage through data visualizations and in-depth articles, the news media could create a new, truly human-centered form of personalization that lets the user know how personalization is done and how it’s used to affect the news experience.

And let’s stop blaming algorithms when it comes to filter bubbles. Algorithms can be used to diversify your news experience. By understanding what you see, it’s also possible to understand what you haven’t seen before. By turning some of the personalization logic upside down, news organizations could create a machine learning-powered recommendation engine that amplifies diversity.

Augment the journalist

In the domain of abstracting and contextualizing new information and unpredictable (news) events, human intelligence is still invincible.

The deep content understanding of journalists can be used to teach an AI-powered news assistant system that would become better over time by learning directly from the journalists using it, simultaneously taking into account the data that flows from the content consumption.

A smart news assistant could point out what kinds of content are connected implicitly and explicitly, for example based on their topic, tone of voice or other meta-data such as author or location. Such an intelligent news assistant could help the journalist understand their content even better by showing which previous content is related to the now-trending topic or breaking news. The stories could be anchored into a meaningful context faster and more accurately.

Innovation and digitalization doesn’t change the culture of news media if it’s not brought into the very core of the news business.

AI solutions could be used to help journalists gather and understand data and information faster and more thoroughly. An intelligent news assistant can remind the journalist if there’s something important that should be covered next week or coming holiday season, for example by recognizing trends in social media or search queries or highlighting patterns in historic coverage. Simultaneously, AI solutions will become increasingly essential for fact-checking and in detecting content manipulation, e.g. recognizing faked images and videos.

An automated content production system can create and annotate content automatically or semi-automatically, for example by creating draft versions based on an audio interview, that are then finished by human journalists. Such a system could be developed further to create news compilations from different content pieces and formats (text, audio, video, image, visualization, AR experiences and external annotations) or to create hyper-personalized atomized news content such as personalized notifications.

The news assistant also could recommend which article should be published next using an editorial push notification, simultaneously suggesting the best time for sending the push notification to the end users. And as a reminder, even though Google’s Duplex is quite a feat, natural language processing (NLP) is far from solved. Human and machine intelligence can be brought together in the very core of the content production and language understanding process. Augmenting the linguistic superpowers of journalists with AI solutions would empower NLP research and development in new ways.

Augment the newsroom

Innovation and digitalization doesn’t change the culture of news media if it’s not brought into the very core of the news business concretely in the daily practices of the newsroom and business development, such as audience understanding.

One could start thinking of the news organization as a system and platform that provides different personalized mini-products to different people and segments of people. Newsrooms could get deeper into relevant niche topics by utilizing automated or semi-automated content production. And the more topics covered and the deeper the reporting, the better the newsroom can produce personalized mini-products, such as personalized notifications or content compilations, to different people and segments.

In a world where it’s increasingly hard to distinguish a real thing from fake, building trust through self-reflection and transparency becomes more important than ever. AI solutions can be used to create tools and practices that enable the news organization and newsroom to understand its own activities and their effects more precisely than ever. At the same time, the same tools can be used to build trust by opening the newsroom and its activities to a wider audience.

Concretely, AI solutions could detect and analyze possible hidden biases in the reporting and storytelling. For example, are some groups of people over-presented in certain topics or materials? What has been the tone of voice or the angle related to challenging multi-faceted topics or widely covered news? Are most of the photos depicting people with a certain ethnic background? Are there important topics or voices that are not presented in the reporting at all? AI solutions also can be used to analyze and understand what kind of content works now and what has worked before, thus giving context-specific insights to create better content in the future.

AI solutions would help reflect the reporting and storytelling and their effects more thoroughly, also giving new tools for decision-making, e.g. to determine what should be covered and why.

Also, such data and information could be visualized to make the impact of reporting and content creation more tangible and accessible for the whole newsroom. Thus, the entire editorial and journalistic decision-making process can become more open and transparent, affecting the principles of news organizations from the daily routines to the wider strategical thinking and management.

Tomorrow’s news organizations will be part human and part machine. This transformation, augmenting human intelligence with machines, will be crucial for the future of news media. To maintain their integrity and trustworthiness, news organizations themselves need to able to define how their AI solutions are built and used. And the only way to fully realize this is for the news organizations to start building their own AI solutions. The sooner, the better — for us all.

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