Digital nudging increasingly shapes behavior, impressions, and decision-making in public spaces. However, existing empirical research has largely focused on nudging in relation to individualized decision-making, offering limited insight into how diverse publics interpret such technologies when embedded in public infrastructures—what we refer to here as infrastructural digital nudging. In such settings, the influence of digital nudging may become less visible and less easily avoidable, while asymmetries between those who design the technology and those who encounter it may become more consequential. These conditions, together with the possibility that public expectations and social implications extend beyond expert-defined frameworks, highlight the need for early engagement. In this study, we investigate public perceptions of infrastructural digital nudging based on multi-display signage through a demonstration conducted at a national science museum in Japan. We used a convergent mixed-methods design that combined quantitative analyses of structured questionnaire responses with qualitative analyses of open-ended questionnaire responses and sticky notes. This research design enabled both statistical analysis and fine-grained interpretation of participants’ concerns, interests, and imagined uses. Our results show that, at an aggregate level, participants prioritized understanding the technology’s mechanisms, purposes, and social impacts over canonical ethical categories such as autonomy and transparency. However, qualitative analyses revealed considerable heterogeneity: some participants expressed strong concerns about unconscious influence, manipulation, and misuse. Co-occurrence analyses further indicated that participants’ evaluations were structured around different perspectives, including science-focused assessment and social concerns. These findings suggest that public perceptions of infrastructural digital nudging are heterogeneous and organized around multiple perspectives, not reducible to expert-defined categories. This underscores the importance of empirically examining how such technologies are interpreted when encountered in environments approximating real-world situations.
The literature on algorithmic fairness has concentrated on the choice amongst fairness metrics, a choice constrained by well-known impossibility results (Chouldechova 2017; Kleinberg et al. 2017). This paper argues that a prior question deserves equal attention: how the categories, variables, and objective functions that constitute an algorithmic system’s representation of its domain are chosen, and when those choices themselves discriminate? We propose a criterion for distinguishing two kinds of algorithmic discrimination. A disparity is contingent, relative to a legitimate purpose and a class of admissible designs, when an alternative design within that class serves the purpose whilst reducing the disparity; it is constitutive, relative to the same parameters, when no such alternative is available. Because contingency is an existential claim and constitutivity a negative existential over a class of designs, the two carry asymmetric obligations: contingency is shown by exhibit, constitutivity by documented exhaustion, and neither is discharged by assertion. Every constitutivity claim therefore states in advance the exhibit that would refute it, which is what makes the distinction actionable in design review, audit, and litigation rather than merely classificatory. The criterion adapts the comparative logic of the less-discriminatory alternative from disparate-impact doctrine (Laufer et al. 2025) and shifts it from the level of models and outcomes to the level of representational choices, whose distinctive rigidity we analyse through the notion of open texture (Waismann 1945; Hart 1961): computational systems fix ex ante the precisifications that human decision-makers perform case by case. Three case studies (the ImageNet person categories, tokeniser-level disparities in large language models, and automated hiring together with clinical risk scoring) are used to test, not merely to illustrate, the criterion. We conclude by proposing ontological auditing, an upstream evaluation of representational choices articulated through an Ontological Impact Statement, and by locating it within the risk management and data governance provisions of the EU AI Act.
