Tow Report: "Artificial Intelligence in the News" and How AI Reshapes Journalism and the Public Arena

Challenging the poor understanding of the effects of AI on the news industry and our information environment.

March 06, 2024

Despite growing interest, the effects of AI on the news industry and our information environment — the public arena — remain poorly understood. Insufficient attention has also been paid to the implications of the news industry’s dependence on technology companies for AI. Drawing on 134 interviews with news workers at 35 news organizations in the United States, the United Kingdom, and Germany — including outlets such as The Guardian, Bayerischer Rundfunk, the Washington Post, The Sun, and the Financial Times — and 36 international experts from industry, academia, technology, and policy, this report examines the use of AI across editorial, commercial, and technological domains with an eye to the structural implications of AI in news organizations for the public arena. In a second step, it considers how a retooling of the news through AI stands to reinforce news organizations’ existing dependency on the technology sector and the implications of this.

Chapter 1 is broken down into three parts, exploring (i) news organizations’ motives for introducing AI into their businesses; (ii) the ways in which AI is currently being used for the production and distribution of journalism; and (iii) the expectations being placed on AI’s scope to deliver efficiency. 

  • In terms of motivations, news organizations have adopted AI as a result of recent technological advancements, market pressures stemming partially from the industry’s financial challenges, competitive dynamics with a focus on innovation, and the pervasive sense of uncertainty, hype, and hope surrounding AI.
  • AI is now applied across an ever greater range of tasks in the production and distribution of news. Contrary to some assertions, many of the most beneficial applications of AI in news are relatively mundane, and AI has often not proved to be a silver bullet in many cases.
  • AI’s potential to increase efficiency in news organizations is a central motivator for its adoption. Various examples demonstrate that efficiency and productivity gains have been achieved, including dynamic paywalls, automated transcription, and data analysis tools in news production. 
  • Such efficiency gains are task- and context-dependent. Potential efficiency gains can be curtailed by factors such as the unreliability of AI outputs, concerns about reputational damage resulting from inaccurate AI outputs, and the difficulty of automating certain tasks.

Reflecting on the extent to which AI has impacted news organizations, I argue that it presents a further rationalization of news work through AI, as work processes that traditionally relied on human intuition are increasingly becoming suffused with or replaced by a technology that is imbued with ideas of rationality, efficiency, and speed — and that does indeed provide greater efficiency and effectiveness in some contexts. However, the effects of AI in the news are subject to contextual factors, with professional norms, resistance from news workers, regulations, audience preferences, and existing technological infrastructures all acting as constraints.

Chapter 2 explores the questions of how and why news organizations rely on technology companies for AI. Again, it is broken down into three parts, analyzing (i) the contexts in which publishers rely on AI and AI infrastructure from platform companies; (ii) the reasons for this reliance; and (iii) the implications of this relationship. Key takeaways include:

  • News organizations make extensive use of AI products and infrastructure from major tech companies like Google, Amazon, and Microsoft across various aspects of their operations.
  • Larger, better resourced news organizations are more likely to engage in in-house AI development. The majority of other publishers, especially smaller ones, opt for third-party solutions from platform companies because of the high costs associated with custom AI.
  • Publishers turn to platform companies’ AI offerings due to the costs and challenges associated with independent development, including the need for extensive computing power, competition for tech talent, and the scarcity of large datasets. The convenience, scalability, and cost-effectiveness of platform offerings make them attractive, allowing publishers to leverage AI capabilities without the financial burden of in-house development.
  • Despite reservations in some quarters of the news industry, the adoption of “platform AI” is largely viewed as a pragmatic choice driven by economic challenges and the competitive landscape for tech talent.
  • The complexity of AI increases platform companies’ control over news organizations, creating lock-in effects that risk keeping news organizations tethered to technology companies. This limits news organizations’ autonomy and renders them vulnerable to price hikes or the shifting priorities of technology companies that may not align with their own. 
  • The lack of transparency in AI systems raises worries about biases or errors creeping into journalistic output, especially as generative AI models gain prominence. There is also a risk that the use of AI undercuts journalists’ autonomy by limiting their discretionary decision-making abilities.

The growing use of AI in news work tilts the balance of power toward technology companies, raising concerns about “rent” extraction and potential threats to publishers’ autonomy business models, particularly those reliant on search-driven traffic. As platforms prioritize AI-enhanced search experiences, publishers fear a shift where users opt for short answers, impacting audience engagement and highlighting the increasing control exerted by platform companies over the information ecosystem.

Bringing all this together, Chapter 3 interrogates the question of whose interests are being served by the increasing adoption of AI in the news and how this shift stands to reshape the public arena — our information ecosystem. In this chapter I argue:

  • Currently, AI aids news workers rather than replaces them, but there are no guarantees this will remain the case. AI is sufficiently mature to enable the replacement of at least some journalism jobs, either directly or because fewer workers are needed.
  • It is not a given that AI will free up news workers to do deeper or better journalism. It is just as likely that any time savings will immediately be filled with new or additional demands.
    • AI’s effects on the news and the public arena will largely be determined by the decisions news organizations and managers make about when, where, and how the technology is used. The use of AI will not automatically improve journalism or the quality of information available to the public; this will only be achieved if the technology is used for this purpose.
    • The increasing use of AI will likely reinforce existing inequalities among news organizations, with well-resourced, international publishers getting a head start. Local news organizations and publishers in the Global South are often an afterthought in the current conversations around AI in the news.
  • On a macro level, news organizations are a vital component of the public arena. They act as gatekeepers for the common attention space most of us inhabit. As news organizations change through AI, so does the makeup of the broader system that they constitute and shape.
  • The adoption of AI is shifting newswork, and the public arena, further toward the technical and the logics of platform companies, e.g. prioritizing greater rationalization and calculability (on the audience side in particular), and efficiencies and productivity in journalistic work. But this approach may not necessarily prioritize the welfare of journalism or the needs of audiences. Not every problem the news faces can be addressed with technological solutions.
  • Publishers’ use of platforms’ AI for their own services, and their growing dependence on technology companies for AI more generally, could further weaken the news industry. The visibility of news content could shrink as AI user experiences become more popular.
  • At times, publishers’ use of AI helps improve the AI systems of major technology giants. This provides a pathway for platform companies to build better general-purpose AI products and services, further cementing their control over information, and potentially enabling them to take over tasks that were once central to journalism.

Finally, the conclusion summarizes my overall insights into how AI shapes and reshapes the news and the public arena.

  • For now, I argue, AI mostly constitutes a retooling of the news rather than a fundamental change in the needs and motives of news organizations. It does not impact the fundamental need to access and gather information, to process and package that information into “news,” to reach existing and new audiences, and to make money.
  • AI will play a transformative role in reshaping news work, from editorial to the business side. We are witnessing — to a degree — a further rationalization of news work through AI. It is important to recognize that the extent of this reshaping will be context- and task-dependent, and will also be influenced by institutional incentives and decisions.
  • Winners and losers will emerge. In fact, they already have. News organizations that have been able to invest in research and development, devote staff time, attract and retain talent, and build infrastructure already have something of a head start. These “winners” are also in a stronger position to demand better terms when negotiating with platforms and technology companies.
  • As news organizations get reshaped by AI, so too will the public arena that is so vital to democracy and for which news organizations play a vital gatekeeper role. The way this takes shape will depend on decisions made by two sets of actors: one that wields direct control over the conditions of news work (executives and managers, journalists) and, increasingly, one that does not (technology companies, regulatory bodies, and the public).
  • AI will be far from the only thing that shapes the news and the public arena in the coming years. Journalism is not fundamentally altered by a single technology: It interacts with institutions and other forces in society and the economy.
  • Productivity gains from AI in the news will not be straightforward. The benefits of AI to the news will be staggered. They will incur costs in the early stages and necessitate changes at the organizational and strategic level.
  • The adoption of AI in news organizations will not be frictionless. Regulation, resistance from news workers, audience preferences, and incompatible technological infrastructure are just some of the variables that will shape the speed at which news organizations adopt AI, and, by extension, the rate at which tangible effects on the news come into focus. 
  • AI will not be a panacea for the many deep-seated problems and challenges facing journalism and the public arena. Technology alone cannot fix intractable political, social, and economic ills. News organizations will continue to be forced to make a case for why they still matter in the modern news environment — and why they deserve audiences’ attention and money. 
  • The concentration of control over AI by a small handful of major technology companies must — and will — remain a key area of scrutiny. Control over infrastructure confers power.
  • Developing frameworks to balance innovation — which is bound to continue — through AI in the news with concerns around issues like copyright and various forms of harms will remain a difficult and imperfect but necessary task. 
  • As with any new technology entering the news, the effects of AI will neither be as dire as the doomsayers predict, nor as utopian as the enthusiasts hope.

Read more at the Columbia Journalism Review.