Sounder’s AI-Powered Classification Technology Outperforms Keyword Analysis for Multicultural Audio Content, Encouraging Greater Monetization

Sounder, a leading AI-powered brand safety solution in podcasting, and Urban One, the largest diversified Black-owned media organization in the United States, today released the findings of a groundbreaking research project on AI/ML-driven brand safety and suitability for podcasting.

The research, which was conducted in partnership with Radio One & Reach Media, Urban One’s audio divisions, as well as the new Urban One Podcast Network, found that AI/ML-driven tools can accurately classify diverse content, leading to greater inclusivity, representation, and advertising opportunities for underrepresented voices.

“This research is a significant step forward in ensuring equal monetization opportunities for diverse creators in podcasting,” said Kal Amin, co-founder and CEO of Sounder. “By addressing concerns about AI/ML-driven brand safety and suitability models, Sounder’s AI-based technology supports authentic and inclusive content representation.”

The research found that traditional keyword-based approaches to brand safety and suitability are often inaccurate and can lead to the under-monetization of diverse content. For example, a keyword-based approach might flag a podcast episode that discusses race or social justice as unsafe, even if the episode features a well-produced, informative and educational conversation that is respectful of all viewpoints.

In contrast, Sounder’s AI-based technology is able to understand the context of a podcast episode and accurately assess its suitability for advertising. This means that Black content creators who share their unique stories and perspectives can be confident that their content has the ability to be appropriately classified and monetized.

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Additional findings include:

• When applying a standard keyword blocklist solution to Urban One’s podcast content, an overwhelming 92% of all episodes are removed from available inventory.
• When applying a semantically and contextually focused AI/ML-based model to classify brand safety and suitability, only 10% of episodes are potentially unavailable for monetization.
• Transcription quality is important for accurately classifying content. If Black English (AAVE) is transcribed incorrectly, this can lead to misclassifications and further marginalization of Black creators.

“At Urban One, we are passionate about having real, authentic dialogue with our audience around topics that matter to them, while also creating equitable conversations with brands and agencies, ensuring that they can invest confidently in brand safe, relevant environments,” said Josh Rahmani, CRO, Urban One, Audio Division. “This research is a step in the right direction to help advertisers see the bias that exists in legacy brand safety solutions which disproportionately impact Black-owned and Black-targeted media.”

The research findings have implications for the entire podcasting industry. As podcasting continues to grow in popularity, it is essential that the industry finds ways to ensure that all voices are represented. By using AI/ML-driven tools that are designed without bias, the podcasting industry can create a more inclusive and equitable space for all creators.

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