Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals

arXiv:2505.16057v2 Announce Type: replace-cross Abstract: AI-Generated (AIG) content has become increasingly widespread by recent advances in generative models and the easy-to-use tools that have significantly lowered the technical barriers for producing highly realistic audio, images, and videos through simple natural language prompts. In response, platforms are adopting provable provenance with platforms recommending AIG to be self-disclosed and signaled to users. However, these indicators may be often missed, especially when they rely solely on visual cues and make them ineffective to users
The rapid proliferation of highly realistic AI-generated content necessitates urgent solutions for provenance, particularly as generative AI tools become more accessible.
The effectiveness of AI provenance directly impacts trust in digital information, the integrity of content platforms, and the regulatory landscape around AI.
The reliance on purely visual cues for AI-generated content identification is highlighted as insufficient, pushing for more inclusive and robust signaling mechanisms.
- · Platforms adopting multi-modal provenance indicators
- · AI safety and ethics researchers
- · Developers of inclusive AI verification technologies
- · Regulators focused on disinformation
- · Content platforms relying solely on visual watermarks
- · Creators of AI-generated disinformation
- · Users vulnerable to misleading AI content
- · Traditional media struggling with verification
Increased pressure on platforms to implement more effective and accessible AI provenance tools beyond visual indicators.
Development of industry standards for AI content marking that accommodate diverse user needs, including those with visual impairments.
Potential for new 'AI-blindness' or 'AI-disorientation' as a recognized societal challenge, requiring broad technological and educational responses.
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Read at arXiv cs.AI