Dancing With Algorithms: Interaction Creates Greater Preference and Trust in Machine-Learned Behavior

Abstract

Objective: We examined a method of machine learning (ML) to evaluate its potential to develop more trustworthy control of unmanned vehicle area search behaviors. Background: ML typically lacks interaction with the user. Novel interactive machine learning (IML) techniques incorporate user feedback, enabling observation of emerging ML behaviors, and human collaboration during ML of a task. This may enable trust and recognition of these algorithms. Method: Participants judged and selected behaviors in a low and a high interaction condition (IML) over the course of behavior evolution using ML. User trust in the outputs, as well as preference, and ability to discriminate and recognize the behaviors were measured. Results: Compared to noninteractive techniques, IML behaviors were more trusted and preferred, as well as recognizable, separate from non-IML behaviors, and approached similar performance as pure ML models. Conclusion: IML shows promise for creating behaviors by involving the user; this is the first extension of this technique for vehicle behavior model development targeting user satisfaction and is unique in its multifaceted evaluation of how users perceived, trusted, and implemented these learned controllers. Application: There are many contexts where the brittleness of ML cannot be trusted, but the advantage of ML over traditional programmed behaviors may be large, as in some military operations where they could be scaled. IML in this early form appears to generate satisfactory behaviors without sacrificing performance, use, or trust in the behavior, but more work is necessary.