Media
Auditing the Auditors: Does Community-based Moderation Get It Right?
Alimohammadi, Yeganeh, Huang, Karissa, Borgs, Christian, Chayes, Jennifer
Online social platforms increasingly rely on crowd-sourced systems to label misleading content at scale, but these systems must both aggregate users' evaluations and decide whose evaluations to trust. To address the latter, many platforms audit users by rewarding agreement with the final aggregate outcome, a design we term consensus-based auditing. We analyze the consequences of this design in X's Community Notes, which in September 2022 adopted consensus-based auditing that ties users' eligibility for participation to agreement with the eventual platform outcome. We find evidence of strategic conformity: minority contributors' evaluations drift toward the majority and their participation share falls on controversial topics, where independent signals matter most. We formalize this mechanism in a behavioral model in which contributors trade off private beliefs against anticipated penalties for disagreement. Motivated by these findings, we propose a two-stage auditing and aggregation algorithm that weights contributors by the stability of their past residuals rather than by agreement with the majority. The method first accounts for differences across content and contributors, and then measures how predictable each contributor's evaluations are relative to the latent-factor model. Contributors whose evaluations are consistently informative receive greater influence in aggregation, even when they disagree with the prevailing consensus. In the Community Notes data, this approach improves out-of-sample predictive performance while avoiding penalization of disagreement.
Ros Atkins on... Trump's mixed messages on the war
Ros Atkins on... Trump's mixed messages on the war For every day of this war, President Trump has been sharing his perspective and his thinking - whether in press conferences, in video statements or in posts on social media. In the last week, that's continued - as strikes have been exchanged - and pressure has built on the supply of oil and gas from the region. The BBC's Analysis Editor Ros Atkins has looked at what the President's been saying. Watch: Sean Penn receives'Oscar' in Ukraine after skipping US ceremony The Academy Award winning US actor won his third Oscar on Sunday, but skipped the ceremony to visit Ukraine. Voiced by Domhnall Gleeson and directed by John Kelly, Retirement Plan is nominated for Best Animated Short Film at the 98th Academy Awards.
Watch: Trump compares attack on Iran to Pearl Harbor in meeting with Japanese PM
In a meeting with Japanese Prime Minister Sanae Takaichi in the Oval Office, US President Donald Trump was asked why he didn't inform allies about his plan to attack Iran. Trump responded by raising Japan's attack on Pearl Harbor during World War II, saying, Who knows better about surprise than Japan? Watch: Sean Penn receives'Oscar' in Ukraine after skipping US ceremony The Academy Award winning US actor won his third Oscar on Sunday, but skipped the ceremony to visit Ukraine. Voiced by Domhnall Gleeson and directed by John Kelly, Retirement Plan is nominated for Best Animated Short Film at the 98th Academy Awards. 'I don't know why we're doing it' - Americans divided on Iran war Ten days since President Trump first announced the attack, people from across the US tell the BBC what they think the best outcome of the conflict could be.
MotionBooth: Motion-Aware Customized Text-to-Video Generation
In this work, we present MotionBooth, an innovative framework designed for animating customized subjects with precise control over both object and camera movements. By leveraging a few images of a specific object, we efficiently fine-tune a text-to-video model to capture the object's shape and attributes accurately. Our approach presents subject region loss and video preservation loss to enhance the subject's learning performance, along with a subject token cross-attention loss to integrate the customized subject with motion control signals. Additionally, we propose training-free techniques for managing subject and camera motions during inference. In particular, we utilize cross-attention map manipulation to govern subject motion and introduce a novel latent shift module for camera movement control as well. MotionBooth excels in preserving the appearance of subjects while simultaneously controlling the motions in generated videos. Extensive quantitative and qualitative evaluations demonstrate the superiority and effectiveness of our method. Models and codes will be made publicly available.
It's so easy to do bad things with Canva's Magic Layers
PCWorld reports that Canva's new Magic Layers AI feature converts images into editable templates, allowing users to modify text, remove objects, and edit individual elements within photos. The tool poses significant disinformation risks by enabling easy manipulation of news content while preserving credible visual elements like logos and matching original fonts seamlessly. Magic Layers requires a Canva Pro subscription and can make AI-generated fake content appear more polished than originals, complicating detection efforts. I know that there is indeed something good and useful about Canva's Magic Layers tool, which uses AI to transform an image into an editable template. But all I can think of it is how people can and will use it for nefarious purposes. Canva's Magic Layers tool was launched last week .