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SUMMARY:The Label Paradox: Can AI Transparency Create More Distrust? | AI\, Media\, and Democracy Lab
DESCRIPTION:Our hopes are pinned on AI labels. AI labels are expected to help users navigate an increasingly synthetic information environment by distinguishing fact from fiction\, human from machine. Beginning in August 2025\, the EU AI Act requires professional users of GenAI\, including journalists\, advertisers\, political actors\, governments\, and public institutions\, to disclose AI-generated content. The EU AI Office has already proposed concrete approaches for what such labels should look like. \nWith these labels becoming governance mechanisms of trust online\, some crucial questions remain largely unanswered – what if labels do not work as intended? What if they produce unintended consequences\, or even undermine the very conditions of trust they are designed to protect? \nWhile public support for AI transparency is high\, and mandatory AI labels are an important step towards more transparency on AI for users\, we still know surprisingly little about how labels shape user perceptions\, platform behavior\, and information ecosystems. Does a “Generated with AI” label erode trust in content that is actually accurate and reliable? Does the absence of a label become a proxy for authenticity\, regardless of whether content is actually trustworthy? How do users interpret or strategically mobilize labels in politically polarized environments? And could mandatory labeling create a two-tier information ecosystem in which labeled content is dismissed while unlabeled content receives unearned credibility? \nThese questions become even more pressing in contexts such as journalism\, elections\, activism\, war reporting\, and platform moderation\, where trust and authenticity are already highly contested. Our online panel brings together experts in AI regulation\, media studies\, cognitive psychology\, computational linguistics\, and platform governance to explore the promises\, limits\, and unintended consequences of AI labeling. Together\, we will zoom out and investigate AI labels and their implications as part of the broader information ecosystem. Which problems exactly can AI labels solve\, which not\, and when do they backfire. \nClick here to learn more and register to attend. Note: This webinar is taking place in Central European Summer Time (UTC+2). Event details below are listed in ET (GMT-5). 
URL:https://mediawell.ssrc.org/event/the-label-paradox-can-ai-transparency-create-more-distrust-ai-media-and-democracy-lab/
LOCATION:Virtual
CATEGORIES:Webinar
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DTSTART;TZID=America/New_York:20260910T110000
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SUMMARY:LLMs are the Third Wave of Automated Content Moderation | Penn MEDIATED
DESCRIPTION:Content moderation is critical to protecting users against harmful and illegal online content\, although it may also restrict free expression. Over the past two decades\, platforms have increasingly turned to automation to perform content moderation at scale. First through hashing algorithms and later through predictive machine learning methods\, many large platforms have automated over 90% of content moderation actions. Today\, online platforms are increasingly exploring using large language models (LLMs)\, as a new component of the content moderation technology stack. With online platforms conducting mass layoffs of trust and safety teams\, while also increasing investments in their AI development\, this convening will explore how LLMs fit in the content moderation landscape and what that means for platforms\, policymakers\, and users. \nThis convening brings together researchers\, technologists\, and practitioners to examine what this third wave of automated content moderation means in practice. We will explore how LLMs are being deployed by platforms today\, how they compare to prior automated systems\, and what the emerging research in this space tells us. We will hear from experts who are building their own custom systems using LLMs for content moderation and researchers who study their implications for information integrity. The conversation will also examine the policy environment shaping adoption: regulatory frameworks in the EU\, Brazil and India\, are setting new expectations for the speed and transparency of content moderation decisions\, incentivizing further automation by online platforms. We will share a pre-read working paper on this topic in advance of this event. [Register at this link]
URL:https://mediawell.ssrc.org/event/llms-are-the-third-wave-of-automated-content-moderation-penn-mediated/
LOCATION:Virtual
CATEGORIES:Webinar
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