While brands have been called out for a lack of diversity in their marketing, it can be a difficult issue to quantify. Longtime marketer and former Google marketing strategist Asha Shivaji started SeeMe Index to come up with that data, with some help from AI. I talked to Shivaji, the cofounder and CEO, about how marketing can be more inclusive and why it’s important.

This conversation has been edited for length, continuity and clarity. It was excerpted in the Forbes CMO newsletter.

How much of a problem is the lack of inclusive marketing today?

Shivaji: There was a KPMG study a few years back that showed the number one reason brands weren’t taking action to be more inclusive was a lack of understanding how to move forward. I think what that exemplifies in the market is that a lot of people may have good intentions to be inclusive, but don’t exactly know where to start or how to go about doing that. That’s really where our company came to be.

We are helping brands grow through inclusivity, using responsible AI and measuring everything they put in the world. In measuring everything they put in the world, we are measuring their ads, their product, their website, their external commitments across six identity dimensions: gender expression, age, body size, visible disabilities, sexual orientation and skin tone. With that 360 view, what we often see is brands leaning into one identity dimension or one of those components [with] their ads or products. But very few are consistent across the board in being inclusive. That’s what we’re really trying to help them see and realize as a gap. I think when we are working with brands, they’re so appreciative of having someone hold up a mirror to help them understand how a consumer’s really experiencing them.

SeeMe Index cofounder and CEO Asha Shivaji.

How does SeeMe Index work?

We use two types of AI at a high level. One is computer vision and the other is knowledge extraction.

For the computer vision, we look at publicly available ads. We’re looking at TV spots, YouTube, TikTok, Meta, Instagram. Across all of those platforms, we’re looking for presence and screen time from a brand’s assets for different identity dimensions. It’s not just who’s in the ad, but how long they’re in the ad, and what intersectionality of identity dimensions we see.

For the knowledge extraction part, we’re looking across all the brand’s publicly available assets—their website, their product lines, commitments they make in the press—for mentions of different identity dimensions. Let’s say a brand stands for food insecurity. We know food insecurity affects different groups more than others—the LGBTQ community, Black and brown people. We’re always looking: Does the brand acknowledge that this platform that they stand for has identity significance as well?

There have been a lot of well-publicized issues out there with AI having issues with race and identity and getting things wrong. How do you make sure that your AI gets it right?

We position ourselves and truly want to live the belief of being a responsible AI company. To us, that means it’s not just what you measure, but how you measure it. We’re hyperconscious that any bias built into the model would be bias coming out. When we talk about skin tone, we use the Monk Skin Tone Scale that has 10 shades, versus the Fitzpatrick Scale, built by a dermatologist for the beauty industry many years ago [with] six........

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