The Marketing Association of Australia is reviewing its Code of Ethics to establish clearer guidelines for AI-generated content and synthetic media in advertising. Industry stakeholders are advocating for standardized disclosure requirements that align with Australian Consumer Law to maintain transparency and trust.
The push for standardized AI disclosure reflects a growing need for legal certainty as synthetic media becomes indistinguishable from reality. By aligning the code with Australian Consumer Law, the MAA aims to prevent deceptive advertising while giving strategy teams the confidence to use generative tools without risking reputational damage. This move mirrors automated labeling efforts already underway at Meta and TikTok, suggesting that platform-level accountability will soon become a baseline requirement for the streaming and digital advertising ecosystem. Watch for the final release of the updated code late this year to see if the MAA adopts a standalone AI rule or integrates these standards into existing misleading conduct provisions.
The Marketing Association of Australia is reviewing its Code of Ethics to create standardized disclosure rules for AI-generated content. This initiative addresses concerns regarding synthetic media, such as voice clones and avatars, ensuring alignment with Australian Consumer Law to prevent deceptive advertising and provide legal certainty for digital strategy teams.
The MAA is reviewing its code to establish standardized disclosure rules for AI-generated content and synthetic media, ensuring these practices align with Australian Consumer Law.
Updated guidelines for AI-generated claims and synthetic media are expected by late 2026.
Agencies like Yango and Next&Co are calling for front-end disclosure of AI that alters product truths, while The Digital Picnic has raised concerns about platform accountability for AI creative on services like Meta and TikTok.
Proposed changes include a 'fair and reasonable' test for data collection and stricter regulations regarding the derivation of sensitive information.
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