https://psyarxiv.com/qhwvx/
Designing trustworthy algorithmic decision-making systems is a central goal in system design. Additionally, it is crucial that external parties can adequately assess the trustworthiness of systems. Ultimately, this should lead to calibrated trust: trustors adequately trust and distrust the system. But the process through which trustors assess actual trustworthiness of a system to end up at their perceived trustworthiness of a system remains underexplored. Transferring from psychological theory about interpersonal assessment of human characteristics, we outline a “trustworthiness assessment” model with two levels. On the micro level, trustors assess system trustworthiness utilizing cues. On the macro level, trustworthiness assessments proliferate between different trustors – one stakeholder’s trustworthiness assessment of a system affects others’ trustworthiness assessments of the same system. This paper contributes a theoretical model that advances understanding of trustworthiness assessment processes when confronted with algorithmic systems. It can be used to inspire system design, stakeholder training, and regulation.
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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