In recent years, there have been significant advancements in computer vision which have led to the widespread deployment of image recognition and generation systems in socially relevant applications, from hiring to security screening. However, the prevalence of biases within these systems has raised significant ethical and social concerns. The most extensively studied biases in this context are related to gender, race and age. Yet, other biases are equally pervasive and harmful, such as lookism, i.e., the preferential treatment of individuals based on their physical appearance. Lookism remains under-explored in computer vision but can have profound implications not only by perpetuating harmful societal stereotypes but also by undermining the fairness and inclusivity of AI technologies. Thus, this paper advocates for the systematic study of lookism as a critical bias in computer vision models. Through a comprehensive review of existing literature, we identify three areas of intersection between lookism and computer vision. We illustrate them by means of examples and a user study. We call for an interdisciplinary approach to address lookism, urging researchers, developers, and policymakers to prioritize the development of equitable computer vision systems that respect and reflect the diversity of human appearances.

One of the great ironies of playing sport is that when you are older your experience is a great advantage, but your body no longer operates at its peak. Thanks to experience, game situations seem to slow down for you mentally and you can almost see what is happening before it happens. This is why older players are often better at making others look good than at making themselves look good. The problem they run into is that the great value of their experience is often overlooked. With high-level sports that makes sense, because the body needs to be in optimum state.

A new study has suggested that a great personality trumps good looks when finding a match. Researchers found that people's perceptions of potential dates' attractiveness goes up after they have a positive face-to-face interaction - but only for those who were rated mid to low attractiveness based on their photo. Because those who were deemed good looking could not increase in attractiveness, it was those in the middle who received higher ratings for being friendly and having a good sense of humor. Researchers found that people's perceptions of potential dates' attractiveness goes up after they have a positive face-to-face interaction - but only for those who were rated mid to low attractiveness based on their photo The recent study, conducted by researchers at the University of Kansas, investigated how a person's perception changes of person they'meet' on a dating app when they come face-to-face in real life. By rating someone's attractiveness before meeting them diminishes the rater's evaluation of that person afterward, probably because the rater is comparing their conversation partner to all the other potential partners they saw online.