AI-powered facial assessment tools are reshaping how individuals evaluate appearance and internalize social judgments. This study examines the psychological impact of such tools on self-objectification, self-esteem, and emotional responses, with attention to gender differences. Two samples used distinct versions of a facial analysis tool: one overtly critical (N=75; M=22.9 years), and another more neutral (N=51; M=19.9 years). Participants completed validated self-objectification and self-esteem scales and custom items measuring emotion, digital/physical appearance enhancement (DAE, PAEE), and perceived social emotion (PSE). Results revealed consistent links between high self-objectification, low self-esteem, and increased appearance enhancement behaviors across both versions. Despite softer framing, the newer tool still evoked negative emotional responses (U=1466.5, p=0.013), indicating implicit feedback may reinforce appearance-related insecurities. Gender differences emerged in DAE (p=0.025) and PSE (p<0.001), with females more prone to digital enhancement and less likely to perceive emotional impact in others. These findings reveal how AI tools may unintentionally reinforce and amplify existing social biases and underscore the critical need for responsible AI design and development. Future research will investigate how human ideologies embedded in the training data of such tools shape their evaluative outputs, and how these, in turn, influence user attitudes and decisions.

This study reviews research on social emotions in robotics. In robotics, emotions are pursued for a long duration, such as recognition, expression, and computational modeling of the basic mechanism behind them. Research has been promoted according to well-known psychological findings, such as category and dimension theories. Many studies have been based on these basic theories, addressing only basic emotions. However, social emotions, also called higher-level emotions, have been studied in psychology. We believe that these higher-level emotions are worth pursuing in robotics for next-generation social-aware robots. In this review paper, while summarizing the findings of social emotions in psychology and neuroscience, studies on social emotions in robotics at present are surveyed. Thereafter, research directions towards implementation of social emotions in robots are discussed.

Emotional intelligence is the key for acceptance of intelligent agents by humans as equal partners, e.g., in ad hoc teams. At the same time, its existing implementations in intelligent agents are mostly limited to basic affects. Currently, there is no consensus in the understanding of complex and social emotions at the level of functional and computational models. The approach of this work is based on the mental state formalism, originally developed as a part of the cognitive architecture GMU BICA and recently extended to include affective building blocks (A.V. Samsonovich, AAAI Technical Report WS-12-06: 109-116, 2012). In the present work, complex social emotions like humor, jealousy, compassion, shame, pride, etc. are identified as emergent patterns of appraisals represented by schemas, that capture the cognitive nature of these emotions and enable their modeling. A general model of complex emotions and emotional relationships is constructed that can be validated by simulations of emotionally biased interactions and emergent relationships in small groups of agents. The framework will be useful in cognitive architectures for designing human-like-intelligent social agents possessing a sense of humor and other human-like emotionally intelligent capabilities.