In this paper, we explore the algorithmic objectivity of intelligent decision support systems (DSS) through a critical analysis of the literature. We first formulated a targeted query, then extracted and filtered articles from Scopus based on three main keywords. After rigorously applying inclusion and exclusion criteria, eight articles were selected for analysis. Our results indicate that automation does not guarantee the elimination of behavioral biases, neither the replacement of humans nor objectivity. On the contrary, algorithmic objectivity appears to be a contextual, systematic, and socially constructed phenomenon, influenced by governance, ethics, environment, context of use, and behavioral dimensions. This plurality of factors makes DSS both a technological and multidisciplinary object, where the lack of articulation between these levers leaves an imbalance. Among the limitations, our study is based solely on three keywords (“robo-advisor,” “behavioral finance,” “objectivity”), and a single database (Scopus), and focuses specifically on robo-advisors, which invites future research to broaden the keywords, vary the bibliographic sources, and compare other forms of intelligent DSS. In light of these results, our contribution is to open up an interdisciplinary perspective on the nature of algorithmic objectivity, no longer as a technical property but as a socio-technical process requiring appropriate governance and design, and to guide future research towards comparative experiments between simple and intelligent DSSs to better measure the impact on user decision-making.