## 2 Materials and Methods  
For this preliminary study, 25 male and 25 female faces generated by the AI were downloaded from the website Generated Photos (Newark, Delaware, USA, https://generated. photos, accessed on 5 December 2023 for male pictures and on 14 December 2023 for female pictures) based on the following criteria [75]:  
* Age: young adult;
* Pose: front facing;* Ethnicity: Caucasian;
* Pose: natural. The AI-generated faces were used to eliminate GDPR (general data protection regulation)-related issues and to assure the same projection of all faces (ideal light conditions, constant face angulation, and distance from the camera). The face pictures were downloaded and then further enhanced using AI. AI-powered face enhancement technologies, such as FaceApp, utilize deep learning to improve facial aesthetics by adjusting features such as symmetry and skin texture [70; 71; 72; 73]. For this study, FaceApp (ver. 11.10, FaceApp Technology Limited, Limassol, Cyprus) with a premium account was used [76]. The \"natural\" filter for females (level 1) and \"star\" filter for males (level 1) were used because the team of three authors concluded these filters yielded the most realistic outcomes. The selection of female and male faces took place on 5 December 2023 and 14 December 2023, respectively, and based on chosen statistical methods, the number of generated faces was set to 100 pictures. The goal of the study is to monitor the frequency of individual answers when choosing which picture is more attractive to the respondent as well as to monitor the frequency of point differences in the evaluation of two versions for a given face. For this purpose, the Chi-square goodness of fit test is used. The calculation of Chi-square goodness of fit test was performed by the function _chisq.test_ from the _stats_ package [77]. This test verifies whether the empirical probability distribution matches the given theoretical probability distribution. In other words, it measures how well a statistical model fits a set of observations. In this study, when answering the question of which of the two versions of the pictures is more attractive to the respondent, the agreement with the theoretical distribution, where both options are represented equally often, is observed. The idea is similar for the point differences between an AI-edited pictures and the original picture. The differences were examined in this order, because in almost every case, a picture edited by artificial intelligence was evaluated as more attractive. Therefore, a higher score for these pictures options was expected. It means that the difference should be positive or equal to 0. In this case, however, the differences do not have only two options, as was the case when choosing a more attractive picture, but several options. Theoretically, these could be discrete values from \(-10\) (the case where the edited picture has a score of 0 and the original has a score of 10) to 10 (where the edited photo would have 10 points and the original 0). In both cases, the correspondence of the distribution with a discrete uniform distribution is tested. The _ggplot_ function from the package _ggplot_2_ was used to display the plots [78]. Based on a discussion with a psychologist, who considered assessing 100 faces in one go as overly tiring and a potential threat to valid results, it was decided to create two separate online forms to be sent to 1800 respondents in total. The first form contained 50 pictures of male faces and the second online form distributed a week later contained 50 pictures of female faces. The forms were created so that each page showed one altered and one unaltered face picture. The respondents were asked to state their age by selecting one of four age groups. Then, they were supposed to evaluate which face looked more attractive and then to score each picture on a scale 1-10 (1--least attractive, 10--most attractive). To eliminate the tiredness of the assessors, the second form was sent to the respondents after one week.