People Images & Algorithmic Inferences on Physical Attractiveness: Complain of the algorithms’ reductionist nature, their potential to infringe on users’ autonomy & well-being, & ethical & legal considerations

What Is Beautiful Continues to Be Good: People Images and Algorithmic Inferences on Physical Attractiveness. Maria Matsangidou, Jahna Otterbacher. IFIP Conference on Human-Computer Interaction: INTERACT 2019: Human-Computer Interaction – INTERACT 2019 pp 243-264. August 23 2019. https://link.springer.com/chapter/10.1007/978-3-030-29390-1_14

Abstract: Image recognition algorithms that automatically tag or moderate content are crucial in many applications but are increasingly opaque. Given transparency concerns, we focus on understanding how algorithms tag people images and their inferences on attractiveness. Theoretically, attractiveness has an evolutionary basis, guiding mating behaviors, although it also drives social behaviors. We test image-tagging APIs as to whether they encode biases surrounding attractiveness. We use the Chicago Face Database, containing images of diverse individuals, along with subjective norming data and objective facial measurements. The algorithms encode biases surrounding attractiveness, perpetuating the stereotype that “what is beautiful is good.” Furthermore, women are often misinterpreted as men. We discuss the algorithms’ reductionist nature, and their potential to infringe on users’ autonomy and well-being, as well as the ethical and legal considerations for developers. Future services should monitor algorithms’ behaviors given their prevalence in the information ecosystem and influence on media.

Keywords: Algorithmic bias Attractiveness Image recognition Stereotyping