Context-Based Human Influence and Causal Responsibility for Assisted Decision-Making.

Abstract

Objective: The impact of the context in which automation is introduced to a decision-making system was analyzed theoretically and empirically.

Background: Previous work dealt with causality and responsibility in human-automation systems without considering the effects of how the automation's role is presented to users.

Methods: An existing analytical model for predicting the human contribution to outcomes was adapted to accommodate the context of automation. An aided signal detection experiment with 400 participants was conducted to assess the correspondence of observed behavior to model predictions.

Results: The context in which the automation's role is presented affected users' tendency to follow its advice. When automation made decisions, and users only supervised it, they tended to contribute less to the outcome than in systems where the automation had an advisory capacity. The adapted theoretical model for human contribution was generally aligned with participants' behavior.

Conclusion: The specific way automation is integrated into a system affects its use and the perceptions of user involvement, possibly altering overall system performance.

Application: The research can help design systems with automation-assisted decision-making and provide information on regulatory requirements and operational processes for such systems.