Plain Statistical Terms to Avoid Prejudicial Rejection of Machine Learning in Territorial Data Analysis

This position paper advocates the use of a statistical language to popularize the analysis of territorial data conducted with machine learning (ML) in smart city projects. The jargon of ML and AI- two terms often confused in popular culture- includes psychological terms that can easily dazzle and mislead laypeople. This can lead to alarmist and rejective reactions. Recent studies have analyzed people’s perceptions of risk, their tendencies to anthropomorphize, and the presence of apocalyptic narratives about AI. Therefore, in this paper, a return to school logical concepts is recommended, delving into mathematics and specifically statistics when necessary, to soberly popularize AI branches. This approach would keep AI itself as a philosophical question. Moreover, the narrative of an ”AI revolution” should not be emphasized in territorial projects, which should be inclusive. Better is an evolution perspective. To illustrate this point, an example of a public workshop is sketched in which people with different generational, social, and work backgrounds focus on the ML risks related to the study of territorial data in a smart city. Far from sensationalist conditioning, participants glimpse and discuss the not entirely new nature of these risks, paving the way to progressive learning.