Closed-loop adaptive decision support based on automated trust assessment

This paper argues that it is important to study issues concerning trust and reliance when developing systems that are intended to augment cognition. Operators often under-rely on the help of a support system that provides advice or that performs certain cognitive tasks autonomously. The decision to rely on support seems to be largely determined by the notion of relative trust. However, this decision to rely on support is not always appropriate, especially when support systems are not perfectly reliable. Because the operator's reliability estimations are typically imperfectly aligned or calibrated with the support system's true capabilities, we propose that the aid makes an estimation of the extent of this calibration (under different circumstances) and intervenes accordingly. This system is intended to improve overall performance of the operator-support system as a whole. The possibilities in terms of application of these ideas are explored and an implementation of this concept in an abstract task environment has been used as a case study.