Emotional A.I. research: The importance of data-philosophizing to account for cultural differences Ho Manh Tung Ritsumeikan Asia Pacific University Beppu, Oita, Japan August 27, 2020 The discourse on emotional A.I., i.e., technologies that read, classify, identify human emotions, is currently dominated by Western ideas1. Yet, even A.I. researchers in the West acknowledge there are cultural differences if neglected could magnify and affect A.I.'s accuracy. For example, many emotional surveillance technologies use the framework of eight basic emotions of Paul Ekman to record, classify, and store their data1,2. However, just a brief look at comparative psychology literature, one can find many subtle differences in the way people express and interpret emotions. For instance, there are apparent differences between the way Japanese and American subjects read and infer emotions.3 The feeling of losing one's face seems to be entirely unaccounted for in Ekman's framework.4 In the age of A.I., when more decisions are being delegated to smart algorithms, and there is a growing population of consumers and businesses in these non-dominant cultures, academics and engineers need to explore how newly-developed algorithms can account for these subtle differences in emotional displays and inferences worldwide. I believe that researchers should always take a step back and carefully philosophize about data to account for these differences. I think there are four categories of philosophizing: 1. Data unit: What counts as valid data for emotional recording? How should the data be structured? 2. Data storage and management: How should the data be cleaned and processed? 3. Data analysis: What statistical techniques are appropriate in mining these datasets? 4. Social structures beyond individual data: How should researchers account for the broader social hierarchies/framework that might affect emotional display? For example, people in Confucian cultures are susceptible to social harmony and hierarchy5,6, and this should have clear implications for emotional display and expressions. Thus, how should emotional A.I. engineers capture such nuances in their technologies? Then another essential discussion point is that in the age of open science,7 open data8 should be a guiding principle for researchers in the emerging field of emotional A.I. By publicly sharing data and as well as the philosophies behind them, academia can quickly close the research gap mentioned above. References 1. McStay, A., 2018. Emotional A.I.: The rise of empathic media. London, Sage. 2. Ekman, P., 1999. Basic Emotions. In: Dalgleish, T., Power, M., (eds.). Handbook of Cognition and Emotion. Sussex, UK: John Wiley & Sons 3. Matsumoto, D., 1999. American-Japanese cultural differences in judgements of expression intensity and subjective experience. Cognition & Emotion, 13(2), pp.201218. 4. Han, K. H., 2016. The feeling of "face" in Confucian society: From a perspective of psychosocial equilibrium. Frontiers in Psychology, 7, p.1055. 5. Vuong, Q. H., Ho, M. T., Nguyen, H. K. T., Vuong, T. T., Tran, T., Hoang, K. L., ... & La, V. P. (2020). On how religions could accidentally incite lies and violence: Folktales as a cultural transmitter. Palgrave Communications, 6(1), 82. 6. Vuong, Q. H., Bui, Q. K., La, V. P., Vuong, T. T., Nguyen, V. H. T., Ho, M. T., ... & Ho, M. T. (2018). Cultural additivity: behavioural insights from the interaction of Confucianism, Buddhism and Taoism in folktales. Palgrave Communications, 4(1), 143. 7. Vuong, Q. H. (2020). Reform retractions to make them more transparent. Nature, 582(7811), 149. 8. Vuong, Q. H., La, V. P., Vuong, T. T., Ho, M. T., Nguyen, H. K. T., Nguyen, V. H., Pham, H. H. and Ho, M. T., 2018. An open database of productivity in Vietnam's social sciences and humanities for public use. Scientific Data, 5, 180188.