{"slug": "mediascience-finds-ai-labels-do-not-reduce-ad-performance", "title": "MediaScience Finds AI Labels Do Not Reduce Ad Performance", "summary": "A MediaScience study of 900 U.S. respondents found that AI-disclosure labels on video ads did not reduce brand choice, ad memory, brand recognition, brand attitude, ad liking, or perceived production quality, while all four tested formats increased awareness that content was AI-generated. The research, published as New York's AI Transparency in Advertising law and the EU AI Act take effect in 2026, showed that a text label in the first three seconds raised AI-creation awareness by 28% and a full-duration label raised it by 36%. MediaScience CEO Dr. Duane Varan stated that if the creative is good, disclosure does not hurt ad performance, and advertisers do not need to fear the label.", "body_md": "# MediaScience Finds AI Labels Do Not Reduce Ad Performance\n\nAccording to Marketing Dive and TV Technology reporting on a MediaScience study, researchers tested four AI-disclosure formats against a no-label control with **900 U.S. respondents** and found no decline in measured video ad performance. The study compared a text label shown in the first three seconds, a delayed text label shown in seconds four through six, a full-duration text label, and a full-duration icon. MediaScience found no significant differences in **brand choice**, **ad memory**, **brand recognition**, **brand attitude**, **ad liking**, or **perceived production quality**, while all four labeling formats increased awareness that content was AI-generated. The first-three-seconds text label raised AI-creation awareness by 28%; the full-duration label raised it by 36%, according to TV Technology. Dr. Duane Varan, CEO of MediaScience, was quoted: \"There has been a lot of anxiety in the industry about what happens when you tell people an ad was made with AI. The data gives us a clear answer: if the creative is good, disclosure does not hurt it. Advertisers do not need to be afraid of the label.\" The study was published as New York's **AI Transparency in Advertising** law takes effect in June 2026 and the **EU AI Act** introduces binding disclosure obligations in August 2026.\n\n### What happened\n\nAccording to Marketing Dive and TV Technology reporting on a MediaScience study, researchers ran an online experiment with **900 U.S. respondents** to measure the impact of AI-disclosure labels on video ad effectiveness. The study tested four labeling conditions versus a no-label control: a text label in the first three seconds, a delayed text label shown in seconds four through six, a full-duration text label, and a full-duration icon. MediaScience found no statistically significant decline across measured outcomes, including **brand choice**, **ad memory**, **brand recognition**, **brand attitude**, **ad liking**, and **perceived production quality**. All four labeling conditions increased viewers' awareness that the ad was AI-generated. Dr. Duane Varan, CEO of MediaScience, was quoted: \"There has been a lot of anxiety in the industry about what happens when you tell people an ad was made with AI. The data gives us a clear answer: if the creative is good, disclosure does not hurt it. Advertisers do not need to be afraid of the label.\"\n\n### Quantitative findings\n\nTV Technology reports specific awareness and ad memory scores from the study. Displaying a disclaimer during the first three seconds increased viewers' awareness that the content was AI-generated by 28%; running the label continuously raised awareness by 36%. On ad memory, text labels outperformed the no-label control score of 36 across all conditions: 46 for the 3-second label, 40 for the delayed label, and 49 for full-duration. The icon scored 38, close to the control. While 42% of respondents preferred the visual icon, it was the least effective format for increasing AI awareness. Audiences reported the strongest need for AI labeling when content generates humans (60%), followed by animals (46%), product placement (45%), and voices (45%).\n\n### Study methodology and partners\n\nTV Technology reports the study was conducted by MediaScience in collaboration with the **Ehrenberg-Bass Institute at Adelaide University**, a marketing science academic center, and **MediaPET.ai**, an AI video content platform developed by MediaScience. The experimental design contrasts early, delayed, persistent text, and persistent icon disclosures against a no-label control, and reports null effects on standard advertising KPIs across the tested U.S. sample.\n\n### Regulatory context\n\nThe research was published amid tightening regulation: TV Technology notes New York's **AI Transparency in Advertising** law takes effect in June 2026 and the **EU AI Act** introduces binding disclosure obligations in August 2026. Industry reporting frames the study as directly relevant to advertisers and platforms preparing for those disclosure regimes. The study's experimental design was described as reflecting frameworks under active consideration by U.S. and EU legislators.\n\n### What to watch\n\nWhether results replicate across broader demographics, ad formats (short social vs. long-form video), and real-world programmatic placements. Regulators, platforms, and measurement vendors may publish guidance specifying wording, timing, or iconography for disclosures; such specifications could alter user perception relative to the formats tested. Reported differences in subgroups (age, prior AI familiarity) and longer-term brand metrics, which the study summary does not detail, would further inform how practitioners interpret this single-study result.\n\n## Scoring Rationale\n\nA controlled study with a clear, practitioner-relevant finding: AI disclosure labels do not hurt well-made video ad performance across standard KPIs. The result is directly useful for compliance planning under New York and EU regulations, but represents a single experiment in a niche domain. Score unchanged from original - well-calibrated for a single-study finding with solid experimental detail.\n\nPractice with real Ad Tech data\n\n90 SQL & Python problems · 15 industry datasets\n\n[Active Search Campaigns by BudgetEasy](/problems/sql/active-search-campaigns-by-budget)\n\n[High CPC Clicks & Poor Landing PagesMedium](/problems/sql/high-cpc-clicks-poor-landing-page)\n\n[Campaign ROAS by Attribution ModelHard](/problems/sql/campaign-roas-by-attribution-model)\n\n250 free problems · No credit card\n\n[See all Ad Tech problems](/problems/datasets/adtech)", "url": "https://wpnews.pro/news/mediascience-finds-ai-labels-do-not-reduce-ad-performance", "canonical_source": "https://letsdatascience.com/news/mediascience-finds-ai-labels-do-not-reduce-ad-performance-71f9584e", "published_at": "2026-06-12 12:58:08.528605+00:00", "updated_at": "2026-06-12 12:58:12.135888+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-policy", "ai-ethics", "ai-research", "generative-ai"], "entities": ["MediaScience", "Marketing Dive", "TV Technology", "Duane Varan", "New York", "EU AI Act"], "alternates": {"html": "https://wpnews.pro/news/mediascience-finds-ai-labels-do-not-reduce-ad-performance", "markdown": "https://wpnews.pro/news/mediascience-finds-ai-labels-do-not-reduce-ad-performance.md", "text": "https://wpnews.pro/news/mediascience-finds-ai-labels-do-not-reduce-ad-performance.txt", "jsonld": "https://wpnews.pro/news/mediascience-finds-ai-labels-do-not-reduce-ad-performance.jsonld"}}