According to the World Health Organization, 4.4 % of the world population or around 300 million people are affected by mental disorders. This statistic has been risen, especially in the lower socioeconomic countries or developing countries. It is important that a reliable mental health tool is made available so that psychological disorders could be early detected. Moreover, the recent COVID-19 pandemic has forced consultation and education to migrate the services into online platforms. Many people have faced communication challenges due to the lack of psychological cues from conversational partners during the interaction, which urges the need for affective computing systems. However, in today’s interconnected global world, the understanding of emotional expressions can be diverse in different cultures and nations due to individual exposure to social norms, education, religion, etc. Although there are mutual traits of actions that can be commonly found to decipher emotions, these social messages can be interpreted differently according to the cultural background of audiences. With the consideration of multicultural elements in affective computing, it can elevate the level of autonomy of AI agents delivering users natural and comfortable interactions. Moreover, as security and privacy is one of the key concerns in affective computing as it could be invasive to personal spaces, discouraging people to use the system, it is expected to have a secured affective computing framework that can preserve users' privacy and allows people to comfortably interact with machines without worrying about their information leak to unwanted parties.
The international Workshop on Affective Interaction between Humans and Machines in Multicultural Society (IWAM) is a kick-off workshop gathering researchers from Japan Advanced Institute of Science and Technology (JAIST, Japan), Kanazawa University (KU, Japan), Ho Chi Minh City University of Education (HCMUE, Vietnam), Mahidol University (MU, Thailand) for knowledge exchange and discussion across the research fields, aiming to establish a unified human-machine interaction framework to understand the emotional expressions of people from different cultural backgrounds by the multidisciplinary combination of computer vision, machine learning, psychology, and information security methods.