Who Defines Beauty: Humans or Meitu?
As science seeks to make sense of the world through a digital lens, the abstract notion of beauty is no exception. Founded in 2008, China’s Meitu (“beautiful picture,” in Chinese) is a Hong Kong-listed company with a vision to become “the tech company that best understands beauty itself” .
Meitu’s approach to understanding beauty is to offer free photo-editing and virtual makeup features to its 450 million monthly active users, including options to enlarge eyes, elongate legs, and slim faces . These enhanced photos train Meitu’s machine learning algorithms on how humans view themselves at their most beautiful, which then feed back into expanding Meitu’s capabilities to accurately identify and automate opportunities to beautify a photo. Meitu thus establishes a virtuous cycle of user data, making machine learning core to its product development, organically training itself from data-driven product additions which then increases customer engagement. Meitu currently averages six billion photos a month, with Meitu-perfected faces dubbed “wang hong lian” (“Internet-celebrity face”) and over half of China’s social media selfies upgraded by Meitu .
In total, Meitu has nearly 200 patents and software copyrights on machine learning and augmented reality . Meitu’s R&D division, Meitu Imaging Laboratory (MTLab), is credited with industry breakthroughs in facial recognition, image identification, augmented reality, and machine learning. Once its free apps created a sizable user base and inflow of data, Meitu sought to monetize its machine learning technology with live-streaming and smart hardware.
Meipai has become the #1 video-based social media platform in China, allowing for easy video customization with refined accuracy in identifying backgrounds, faces, lighting, etc. . Meipai has over 140 million monthly active users , and unlike its previous photo apps, Meitu allows its users to monetize their videos with the platform receiving up to 30% of each user’s advertising generated income .
However, Meitu’s biggest cash cow comes from its smartphones (93% of sales) . Claiming to have the largest database of human portraits on the planet, Meitu takes its machine learning technology one step further with its smartphones’ “auto-beautification” — editing in real-time, as you pose for the camera, with smoother and lighter skin, rounder eyes, and whatever else Meitu has been trained to think of as beautiful (see Exhibit 2) .
Meitu has become so efficient with its instant beautification that a user is hardly given time to decide what’s beautiful before Meitu decides for her. As Meitu’s algorithms get smarter, is the user still training Meitu, or is the machine training the individual? The results of machine learning are only as robust and accurate as the data it’s being fed, with the risk of the machine reflecting biases inherent in the data.
As Meitu expands globally, with over one million users in each of the 39 countries it operates in, it faces global challenges of what beauty means for different cultures . Meitu is learning that Norwegians like freckles, but the Chinese hate all blemishes . Meitu has also been accused of racism for whitening people’s skin, and responded with a skin-darkening feature (see Exhibit 3) .
Rather than championing a specific East-Asian interpretation of beauty and reacting only when there is negative press, Meitu should segment its customers culturally and train separate data sets. From there, Meitu could even tailor its editing features per user based on what the app learns about that user’s preferences — for example, the “smoothing skin” feature could keep or remove freckles depending on what it’s learned from the user’s past actions. Ultimately, how a user chooses to beautify themselves should be a personal choice, not a homogenized suggestion from a phone application.
As Meitu steadily builds out its “beauty ecosystem” by expanding into advertising , e-commerce, external partnerships (including brand ambassadors and dermatology hospitals ), and customized cosmetic recommendations , it needs to be conscious of such potential subliminal messaging. To avoid generalizing beauty across cultures, Meitu should consider partnering with local social influencers and recommending products accessible from that region’s local market. By doing so, Meitu can also strengthen its machine learning data by not mixing data that would contaminate and confuse the algorithm, and refine the accuracy of its data.
This year, Meitu announced its long-term goal of becoming a social media platform —the equivalent of Instagram in China (which doesn’t currently exist) . If Meitu’s theory of beauty were to be layered onto Instagram’s significant cultural power, the consequences could be socially destructive, especially for young and impressionable users. Given the possibility, how should Meitu harness machine learning appropriately and what safeguards should the company create for itself as its social influence on Chinese and global beauty standards increases?
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