Leading through the Synthetic Media Era: Platform Governance to Curb AI-Generated Fake News, Protect the Public, and Preserve Trust

Generative AI has collapsed the cost of fabricating persuasive audio, images, and video; social platforms can then amplify these forgeries to millions within minutes. Ordinary users—not only public figures—now face wire-transfer fraud via deepfaked executives, non-consensual intimate imagery, cloned-voice “kidnapping” scams, celebrity-death hoaxes, health/finance misinformation, and market-moving fake photos. Beyond immediate losses, reputational abuse and automation-related job loss correlate with anxiety, depression, and, for some, suicidal ideation. We argue the central risk is not “AI” itself but the low-friction spread of forged sight/sound cues in engagement-optimized feeds. We propose a risk-tiered, authenticate-then-distribute regime anchored by a Pre-Publication Authenticity Verification (PPAV) pipeline that combines provenance (C2PA/Content Credentials), watermark signals, media forensics, similarity/history checks, and semantic claim verification. We add a governance blueprint (policy, operations, metrics), a quarter-by-quarter implementation roadmap, and a victim-first model with rapid takedowns and mental-health-aware UX.

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Publication Details

Journal
Open Journal of Leadership
Published
2025-01-01
DOI
https://doi.org/10.4236/ojl.2025.143020
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Leading through the Synthetic Media Era: Platform Governance to Curb AI-Generated Fake News, Protect the Public, and Preserve Trust

Prajkta Waditwar
Open Journal of Leadership
Hate Speech and Cyberbullying Detection
article

Leading through the Synthetic Media Era: Platform Governance to Curb AI-Generated Fake News, Protect the Public, and Preserve Trust

Prajkta Waditwar
article en

Abstract

Generative AI has collapsed the cost of fabricating persuasive audio, images, and video; social platforms can then amplify these forgeries to millions within minutes. Ordinary users—not only public figures—now face wire-transfer fraud via deepfaked executives, non-consensual intimate imagery, cloned-voice “kidnapping” scams, celebrity-death hoaxes, health/finance misinformation, and market-moving fake photos. Beyond immediate losses, reputational abuse and automation-related job loss correlate with anxiety, depression, and, for some, suicidal ideation. We argue the central risk is not “AI” itself but the low-friction spread of forged sight/sound cues in engagement-optimized feeds. We propose a risk-tiered, authenticate-then-distribute regime anchored by a Pre-Publication Authenticity Verification (PPAV) pipeline that combines provenance (C2PA/Content Credentials), watermark signals, media forensics, similarity/history checks, and semantic claim verification. We add a governance blueprint (policy, operations, metrics), a quarter-by-quarter implementation roadmap, and a victim-first model with rapid takedowns and mental-health-aware UX.

Open Journal of LeadershipVol. 14(03)
Box (United States) (US)
Openalex Percentile: Top 87%
Hate Speech and Cyberbullying Detection
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