Deep learning techniques can be used to extract facial imaging biomarkers of human health status and to track the effects of cosmetic interventions. Here we present a set of tools for analysis of perception of human age and health status. We also demonstrate that when certain population groups are under-represented in the training sets, these populations are left out or may be subject to higher error rates. This is why Youth Laboratories launched Diversity.AI, a think tank for anti-discrimination by the deep-learned systems. The presentation describes the strategies for evaluating human appearance for machine-human interaction and reveals the risks and dangers of deep-learned biomarkers.
Anastasia Georgievskaya is the co-founder and research scientist at Youth Laboratories, a company developing tools to study aging and discover effective anti-aging interventions using advances in machine vision and artificial intelligence. She helped organize the first beauty competition judged by the robot jury, Beauty.AI and develop an app for tracking age-related facial changes and testing the effectiveness of various treatments called RYNKL. Anastasia has a degree in bioengineering and bioinformatics from the Moscow State University. She won numerous math and bioinformatics competitions and successfully volunteered for some of the most prestigious companies in aging research including Insilico Medicine.