Misplaced trust: A bias in human-machine trust attribution - In contradiction to learning theory

Human-machine trust is a critical mitigating factor in many HCI instances. Lack of trust in a system can lead to system disuse whilst over-trust can lead to inappropriate use. Whilst human-machine trust has been examined extensively from within a technicosocial framework, few efforts have been made to link the dynamics of trust within a steady-state operatormachine environment to the existing literature of the psychology of learning. We set out to recreate a commonly reported learning phenomenon within a trust acquisition environment: Users learning which algorithms can and cannot be trusted to reduce traffic in a city. We failed to replicate (after repeated efforts) the learning phenomena of 'blocking', resulting in a finding that people consistently make a very specific error in trust assignment to cues in conditions of uncertainty. This error can be seen as a cognitive bias and has important implications for HCI.