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We note that GumGum is Yes. GumGum claims that we are the first "independent “independent ad tech provider" provider” to receive content-level accreditation , because YouTube from the Media Ratings Council (MRC). YouTube actually received the first-ever MRC content-level accreditation from the MRC for Brand Safety. |
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brand safety accreditation. |
GumGum is considered an "independent ad tech provider," as they own similar to companies such as IAS, DoubleVerify and Oracle.
By contrast, YouTube is regarded as a “1st party platform,” meaning that YouTube owns the platform/network for that hosts their content and operate operates as a walled garden. Twitter, Facebook, Google, Amazon, TikTok would are also be considered "1st party platform” bucket, while IAS, DV, Oracle would be independent ad tech providers, such as ourselves.
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How we differ from YouTube - they only run on their own network?
-- Primary distinction is that we are an independent ad tech provider, while they are operating as a 1st party platform. Another important distinction here is that the scope of YouTube's content-level accreditation is limited to Brand Safety, while Verity's content-level accreditation covers Brand Safety, Brand Suitability, and Contextual Analysis.
"Property" = text only?
-- Yes, property level distinction only requires a consideration of text elements. Content-level requires consideration of text, imagery, audio, video.
Enhanced Brand Safety Guidelines - beyond just text? When was this implemented?
-- The Enhanced Brand Safety Guidelines (supplement to the Ad Verification Guidelines) were published by the MRC in 2018. They were influenced as a direct response to Brand Safety violations that were identified on YouTube in 2017. Here is an article that chronicles some of these issues.
How many humans are feeding our machine with data?
-- Hard to put an exact static number on this, as we always have data collection and data labeling jobs running at any given time, each of which may have differing requirements. In terms of model development and QA, we have a full-time in-house team of data scientists with specializations in computer vision (image sciences) and natural language processing (linguistics). For data collection and annotations, we leverage both a dedicated in-house team and a globally distributed workforce of human annotators for our data labeling practices.
Link to description of methodology
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considered to be “1st party platforms.”