Attribution Errors by People and Intelligent Machines.

Objective: To explore the ramifications of attribution errors (AEs), initially in the context of vehicle collisions and then to extend this understanding into the broader and diverse realms of all forms of human-machine interaction.

Background: This work focuses upon a particular topic that John Senders was examining at the time of his death. He was using the lens of attribution, and its associated errors, to seek to further understand and explore dyadic forms of driver collision.

Method: We evaluated the utility of the set of Senders' final observations on conjoint AE in two-vehicle collisions. We extended this evaluation to errors of attribution generally, as applicable to all human-human, human-technology, and prospectively technology-technology interactions.

Results: As with Senders and his many other contributions, we find evident value in this perspective on how humans react to each other and how they react to emerging forms of technology, such as autonomous systems. We illustrate this value through contemporary examples and prospective analyses.

Applications: The comprehension and mitigation of AEs can help improve all interactions between people, between intelligent machines and between humans and the machines they work with.