The research, conducted by a team at the University of Al Dhaid in Sharjah, used a sequential explanatory mixed-methods design. First, a survey of 300 UAE residents aged 18 to 35 who consume digital news at least three times per week measured how strongly participants felt their feeds were personalized, how diverse they perceived their news exposure to be, and how much they trusted the news they encountered online. Then, 20 in-depth semi-structured interviews explored how participants interpreted and navigated these curated environments in daily life. The sample, recruited through university mailing lists and professional networks, had a mean age of 24 years, was 66 percent female, and comprised undergraduate students and early-career professionals — a deliberately focused group of young, highly connected news users rather than a representative slice of the population.

The quantitative results revealed a consistent and statistically robust pattern. Perceived personalization intensity — the degree to which users felt their feeds were shaped by their past clicks, likes, searches, and viewing time — was the strongest negative predictor of perceived news diversity, with a standardized beta of −0.41 (p < .001). In other words, the more tailored a feed felt, the narrower it seemed. The regression model predicting perceived diversity explained 43.2 percent of the variance, while the trust model explained 48.8 percent, though the authors caution that shared method variance likely inflates these figures. Platform differences also emerged: TikTok-primary users reported significantly lower perceived diversity than Instagram users (beta = −0.28, p < .001), while YouTube-primary users reported higher diversity (beta = +0.21, p = .012). TikTok users also faced roughly twice the odds of being classified into an echo-chamber category compared with Instagram users (OR = 2.45, p = .028).

Trust followed a parallel logic. Perceived diversity was the strongest positive predictor of trust in online news (beta = +0.38, p < .001), suggesting that users who felt their feeds exposed them to varied viewpoints also trusted the news more. Perceived personalization, by contrast, was negatively associated with trust (beta = −0.22, p < .001). Perhaps most intriguingly, algorithmic awareness — participants’ self-reported understanding of how their digital footprint shapes feed content — was negatively associated with trust (beta = −0.12, p = .041). Knowing how recommendation systems work did not reassure users; it deepened their skepticism. The authors interpret this as evidence that awareness without transparency or accountability may intensify rather than resolve distrust.

Proactive news practices emerged as a consistent protective factor. Fact-checking frequency was positively associated with both perceived diversity (beta = +0.16, p < .001) and trust (beta = +0.25, p < .001), and was associated with meaningfully lower odds of echo-chamber classification (OR = 0.61, p = .004). Consulting more news sources showed similar positive associations. The interviews clarified why: these behaviors were not merely verification habits but deliberate tactics for disrupting recommendation loops — searching headlines in private browser tabs, switching between Arabic and English sources, deleting watch histories to reset algorithms, and maintaining separate accounts dedicated solely to news.

The qualitative phase identified three recurring themes that explain the human logic behind the statistics. The first, the useful but opaque algorithm, captured a persistent tension: participants found personalization genuinely efficient and sometimes impressive in its accuracy, yet that very accuracy bred unease because it occurred without visible logic, explanation, or recourse. One 22-year-old TikTok user explained: I know TikTok gives me exactly what I want to hear about regional politics. It is smart. But because it is so tailored, I trust it less. I always wonder what the algorithm is intentionally hiding from me. Another participant described the feed as feeling like someone already knows what I think and just keeps confirming it — convenient, but also a bit like being managed.

The second theme, informational narrowing and algorithmic fatigue, described a gradual rather than sudden constriction of variety. Participants reported that small engagement decisions could lock them into an informational lane for weeks. One Instagram user noted that engaging with a single economic news reel transformed her entire feed, calling it an illusion of choice. Another admitted deliberately avoiding clicks on stories they cared about to prevent the app from flooding them with more of the same. A TikTok user described realizing only after about two weeks that almost nothing in the feed surprised them anymore — everything simply confirmed what they already felt.

The third theme, tactical evasion and digital agency, revealed that many participants saw themselves as active managers rather than passive recipients of their information environments. Their strategies were deliberate and often laborious: opening private browser tabs to search for headlines on international news sites, switching between Arabic and English sources because each language yielded a noticeably different version of the same event, deleting YouTube watch history every few weeks to reset the algorithm, and maintaining separate news-only Instagram accounts to keep recommendation profiles clean. One participant described checking every story on at least two platforms before forming an opinion, reasoning that each platform shows a different piece of the picture.

Theoretically, the authors argue that the Algorithmic Perception Paradox is not merely a local finding but a structural dynamic rooted in what Zuboff (2019) calls surveillance capitalism. Three components define it: a relevance signal, where the feed feels accurate and calibrated to the user’s interests; an exclusion inference, where users recognize that curation involves omission and suspect the omission may be commercially motivated; and a trust deficit, where perceived accuracy fails to translate into confidence in completeness or balance. The paradox is distinct from filter bubble theory, which concerns objective feed diversity, and from echo chamber theory, which concerns social reinforcement within homogeneous networks. Instead, it addresses how users evaluate the epistemic status of personalized news environments — a question that becomes especially acute where personalization is salient, transparency is limited, and commercial optimization is assumed to shape visibility.

The UAE context adds theoretical weight beyond geographic extension. Unlike Western frameworks built around partisan ideological polarization, perceived informational enclosure in the multilingual, transnationally connected Gulf operates through language segmentation, expatriate diversity, and platform-specific content cultures. Participants who moved between Arabic and English sources described linguistic switching as deliberate diversity work rather than a quirk. The authors acknowledge limitations — the cross-sectional design cannot establish causality, common method bias likely inflates model fit, and the small X subsample warrants caution — but the pattern held consistently across both phases. Their conclusion is clear: trust in personalized news depends not only on perceived accuracy but also on perceived opacity and perceived completeness, and meaningful transparency, diversity-sensitive design, and user controls remain structural necessities that individual tactics alone cannot replace.

Subject of Research: Algorithmic news personalization, perceived diversity, and trust among young UAE news users

Article Title: The algorithmic perception paradox: personalization, news diversity, and trust among young UAE news users

Article References: Al-Zoubi, A., Setoutah, S., Farhi, F., & Belghitia, S. (2026). The algorithmic perception paradox: personalization, news diversity, and trust among young UAE news users. Frontiers in Human Dynamics, 8, Article 1931633. https://doi.org/10.3389/fhumd.2026.1931633

Image Credits: AI Generated

DOI: 10.3389/fhumd.2026.1931633

Keywords: algorithmic personalization, news diversity, trust in news, UAE, filter bubbles, echo chambers, surveillance capitalism, TikTok, media literacy, digital platforms, algorithmic, perception

The stakes could hardly be higher. Public trust in journalism has been declining for years, and news avoidance is rising across democratic societies. Surveys consistently show that audiences are sceptical about AI in news, distrust fully automated content, and want publishers to be open about their use of the technology. Yet the same research reveals a paradox that has come to be known as the transparency backfire: disclosing AI involvement can itself reduce trust in news, making readers question content that was, in fact, produced or checked by human journalists. Publishers are therefore caught between an ethical obligation to disclose and a commercial imperative not to undermine the very credibility that makes people pay for journalism. As one senior FT manager put it bluntly in the study: mistrust is the top threat, because once you lose that, you forget the rest.

The study finds that the FT operationalises transparency through a hybrid of policy, process, and practice, underwritten by explicit endorsement from the very top of the organisation. Editor in Chief Roula Khalaf has publicly committed the newsroom to transparency, promising that all newsroom experimentation will be recorded in an internal register. The FT’s internal AI Principles state that the organisation will not allow AI to compromise the integrity of its journalism, which will be reported, written, and created by journalists and editors, while permitting experimentation with secondary content such as summaries, alt-text, captions, and translations, provided there is appropriate disclosure. New AI applications are vetted by cross-functional AI Use Case Panels, which decide whether a use case is approved and advise on the appropriate level of transparency. Some red lines are absolute: the FT does not generate photographic imagery with AI, except for clearly flagged illustrations in stories about AI itself.

Internally, transparency functions as a trust-building and risk-management mechanism rather than a public relations exercise. The FT’s internal AI Policy places accountability squarely on staff, requiring them to review AI output as if they had created it themselves. If an AI-generated summary contains an error, the journalist whose name appears on the story bears the responsibility just as before. AI functionality inside the content management system is signposted with the now-familiar sparkle emoji, accompanied by reminders that generated content may contain hallucinations. Technical systems even highlight portions of AI output that are statistically more likely to contain errors, directing human attention where it matters most. Regular sharing sessions across editorial, product, and technology teams, an intranet serving as a single source of truth, and an AI Transformation group designed to prevent information silos all reinforce the message. Managers described a culture in which even senior leaders are willing to admit they do not know, backed by what one participant called a lot of data and checks, and kill switches the whole way along.

Externally, the FT calibrates the prominence of disclosure to the degree of autonomy in the system. The clearest illustration is the contrast between two reader-facing features. Ask FT, a generative chatbot that lets subscribers interrogate nearly two decades of archival reporting, operates with no human in the loop, and therefore carries a strong disclaimer informing users they are interacting with an AI system provided by Anthropic rather than a human. Bullet-point summaries, by contrast, are generated by a language model but sit within articles subject to editorial oversight, and receive lighter labelling. The FT’s internal Responsible AI Guidelines codify this logic: the more automatic or impactful a process, or the larger the proportion of output generated by AI, the more prominent the disclosure should be. When in doubt, disclosure is the default option.

The researchers identified nine factors that systematically shape what, when, and how the FT discloses AI use to readers. Legal and provider requirements come first: some AI vendors contractually mandate disclosure, down to Google Translate’s requirement for a specific logo and a paragraph of capitalised text. Industry benchmarking follows, with the FT studying how publishers such as the New York Times, the Washington Post, and Aftonbladet, along with the BBC’s published AI transparency guidelines, approach the same problems. The nature of the task matters too: behind-the-scenes AI used by the search engine optimisation team, whose output never reaches readers directly, goes undisclosed. The degree of human oversight, the novelty and uncertainty of the system, audience expectations gathered through usability testing and pre- and post-deployment research, the perceived risk of harm or error, commercial sensitivities such as the cost of documenting AI methods in investigative work, and finally design constraints, including the limited screen real estate of mobile devices, all weigh on every disclosure decision.

That last point deserves emphasis, because it reveals how mundane the mechanics of transparency can be. Readers, the FT found, find AI disclosures far more obvious on desktop than on mobile, where there is simply less space to call out specific elements. Designers debated the background colour of AI-generated elements to signal that this is not the journalism itself but something layered on top. The sparkle emoji was adopted partly because readers already understood it and it was becoming an industry-wide standard. Yet cluttering pages with tiny explanatory text, one manager argued, becomes less and less helpful as AI use becomes intuitive to users. Transparency, in other words, is a design problem as much as an ethical one, and the optimal solution shifts as audiences grow accustomed to the technology.

The study also documents five cross-cutting challenges that persist despite the FT’s considerable resources. Achieving consistent, site-wide labelling remains elusive, with participants acknowledging the organisation still lacks a unified agreement on design components and transparency guidelines. Internal communication struggles against silos and fatigue: one manager estimated that no more than a couple hundred of the FT’s three and a half thousand staff had read the responsible AI framework. Standards themselves are moving targets, as models improve and public familiarity grows, prompting the question of when AI becomes just another editorial tool that no longer needs labelling, much as streaming platforms run recommendation algorithms without pop-up warnings. Human over-reliance on confident tools is an anticipated future risk. And audience misinterpretation is real: in usability testing, some participants assumed that the mere presence of an AI tool in an article meant the article itself had been machine-generated.

Beneath these practical difficulties, the researchers identified deeper tensions rooted in what institutional theorists call competing logics. Editorial participants framed transparency primarily in terms of reputation, accuracy, and accountability; product and analytics staff emphasised whether disclosure was intelligible at the point of use; legal and policy voices foregrounded provider requirements and liability exposure. The study interprets these dynamics through the lens of isomorphic pressure, the well-documented tendency of organisations to model themselves on peers under uncertainty. Regulation such as the EU AI Act exerts coercive pressure, professional norms and charters like the Paris Charter on AI and Journalism exert normative pressure, and the absence of any settled best practice drives mimetic copying of other leading publishers. Transparency at the FT is thus not owned by any single department; it is a site of ongoing cooperation and occasional conflict between professional, managerial, and commercial priorities.

The authors’ central argument is that AI transparency in news is best understood as a spectrum rather than a binary, a lived governance practice that evolves with technological capability, commercial reality, professional ethics, and shifting audience attitudes. Optimising for one factor, such as maximal disclosure demanded by regulators, can come at the expense of audience trust or revenue when transparency itself breeds distrust. The study offers several no-regret principles for other organisations: scale disclosure with a system’s autonomy and impact, pair AI labels with concise and comprehensible explanations, continuously measure how users actually interpret them, keep internal transparency and accountability high even when external disclosure is light, and favour consistent, reusable design components across products and platforms. The research carries limitations, being a single-organisation case study reliant on interviews and documents rather than ethnographic observation, with an insider researcher whose access was both asset and potential bias, mitigated by external ethical review and independent co-analysis. Still, its message resonates far beyond one newsroom: in the age of generative AI, telling audiences the truth about how the news is made is simple in principle, fiendishly complex in execution, and never a decision you make just once.

Subject of Research: AI transparency practices and disclosure decision-making in a major news organisation

Article Title: Tricky trade-offs on a transparency spectrum: how the Financial Times approaches transparency about AI use in news

Article References: Lohn, L., & Simon, F. M. (2026). Tricky trade-offs on a transparency spectrum: how the Financial Times approaches transparency about AI use in news. AI &amp; SOCIETY. https://doi.org/10.1007/s00146-026-03369-z

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03369-z

Keywords: artificial intelligence, journalism, transparency, Financial Times, generative AI, newsroom, trust in news, media ethics, disclosure, EU AI Act, audience trust, institutional logics