Using confirmatory factor analyses and multiple indicators per construct, we examined a number of theoretically derived factor structures pertaining to numerous trust-relevant constructs (from 9 to12) across four institutional contexts (police, local governance, natural resources, state governance) and multiple participant-types (college students via an online survey, community residents as part of a city’s budget engagement activity, a random sample of rural landowners, and a national sample of adult Americans via an Amazon Mechanical Turk study). Across studies, a number of common findings emerged. First, the best fitting models in each study maintained separate factors for each trust-relevant construct. Furthermore, post hoc analyses involving addition of higher-order factors tended to fit better than collapsing of factors. Second, dispositional trust was easily distinguishable from the other trust-related constructs, and positive and negative constructs were often distinguishable. However, the items reflecting positive trust attitude constructs or positive trustworthiness perceptions showed low discriminant validity. Differences in findings between studies raise questions warranting further investigation in future research, including differences in correlations among latent constructs varying from very high (e.g., 12 inter-factor correlations above .9 in Study 2) to more moderate (e.g., only 3 correlations above .8 in Study 4). Further, the results from one study (Study 4) suggested that legitimacy, fairness, and voice were especially highly correlated and may form a single higher-order factor, but the other studies did not. Future research is needed to determine when and why different higher-order factor structures may emerge in different institutional contexts or with different samples.

The purpose of this volume is to consider how trust research, particularly trust in institutions, might benefit from increased inter- or transdisciplinarity. In this introductory chapter, we first give some background on prior disciplinary, multidisciplinary, and interdisciplinary work relating to trust. Next, we describe how this many-disciplined volume on institutional trust emerged from the joint activities of the Nebraska Symposium on Motivation and a National Science Foundation-funded Workshop on institutional trust. This chapter describes some of the themes that emerged, while also providing an overview of the rest of the volume, which includes chapters that discuss conceptualizations, definitions, and measurement of trust; institutional trust across domains and contexts; and theoretical advances regarding the “dark” and “light” sides of institutional trust. Finally, we conclude with some thoughts about the future of and potential promises and pitfalls of trust as a focus of interdisciplinary study.

The sudden turn to artificial intelligence has been widely supported because of the several proposed positive outcomes of using such technologies to support or replace humans. Automating tedious processes and removing potential human error is exciting for society, but some concerns must be addressed. This essay aims to understand how artificial intelligence can automate domains that likely significantly impact underprivileged and underrepresented groups. This essay will address the potentially devastating effects of algorithmic biases and AI’s contribution to perpetual economic inequality by surveying different domains, such as the justice system and the real estate industry. Without society broadly understanding the potential negative side effects on systems that matter, the rapid growth of artificial intelligence is a recipe for disaster. Everyone must become educated about AI’s current and potential implications before it is too late to stop its damaging effects.