AI image generation liability concerns the legal responsibility for creating or distributing AI-generated images, including issues of consent, likeness rights, and infringement, as seen in lawsuits over fake explicit images and threats of legal action.
How the framings classify across 3 articles. Each framing is labelled by a small AI stance classifier; see the methodology page for details.
Up to 12 framings across outlets and orientations — each a short phrase the extractor generated to characterise the piece's editorial angle (not a quote). Filter by lean to see how each bloc is spinning the story; click through to read the original.
transparency and accountability
US tech leaders are urgently calling for rules on AI – China already has themmodels lack legal recourse against synthetic image use
Huffer, the AI accusation and the question that remainsLobby groups, unions, think tanks and industry bodies whose own releases or whose people’s media appearances touched this topic over the last 12 months. Owned = their own press releases; earned = their people quoted across radio, press & commentary. A card carried in both — with the release coming first — is the agenda-setting signal. Co-occurrence isn’t proof a group drove the coverage.
debate over authenticity and consent in digital content
Stacked weekly counts; colour by lean. “n/a” covers government and iwi-Māori sources where lean isn't applicable.
How this topic has been named, week by week. A new alias winning out is usually a framing shift.
How the news corpus has covered this same topic over the last 12 weeks. 1 article from RNZ, Stuff, NZ Herald, ODT, 1News, Newsroom and The Spinoff. Click through to the press view for the full panel.
Social-media signal on the same topic, drawn from the social lens. Engagement is likes + 2×shares + 3×replies, the same weighting used across the digest cards. View on /social →
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