Methodology FAQ

The scan was performed with No-Kno’s Diversity Tracker. A tool specifically designed by Belgian start-up No-Kno to track diversity in marketing at scale.

Our measurements of demographics are based on the visual appearance of the people depicted in the advertisements. Make-up, for instance, may make some talents appear younger or older than they actually are. This is not a problem. We are not interested in finding out the exact demographic of the talents in the ads, but rather which demographic they represent.

As an example, if a talent is 37 years old in reality, but looks 27 thanks to make-up, they will represent the age group 25-34 to your viewers, and not 35-44.

Age: The image analysis model will return an age range, we take the mean of the age range as the visual age.

Gender: We realise that gender is not a simple binary attribute that can be determined by visual appearance alone. However, the model looks at facial features to classify a person in a binary way, as male or female.

Ethnicity: A person's dominant and secondary races/ethnicities are inferred by the model, along with their probability of belonging to each.

The purpose of No-Kno is to analyze advertisements at scale. As with any quantitative method, it has its limitations. Algorithms used to analyze images detect visual features and do not take into account cultural or personal context.

When the input image is sufficiently large, has good lighting, is sharp, etc., our model is at least as accurate as a human reviewer.

We always keep a human in the loop, however. An image will be flagged if it is too small, blurry, or if the model returns low confidence in a prediction. An editor may then decide to keep or edit the results.

If a face is < 64 pixels or the face angle > 75 degrees, it is skipped, because the accuracy will be too low, even for a human reviewer.Item description

To avoid bias in race/ethnicity inference, we use a model based on the Fairface dataset: a face image dataset containing 108,501 images which is balanced on 7 ethnic/race groups: White, Black, South Asian, East Asian, Southeast Asian, Middle Eastern, and Latino/Hispanic.