  When humans interact with intelligent systems, their causal responsibility
for outcomes becomes equivocal. We analyze the descriptive abilities of a newly
developed responsibility quantification model (ResQu) to predict actual human
responsibility and perceptions of responsibility in the interaction with
intelligent systems. In two laboratory experiments, participants performed a
classification task. They were aided by classification systems with different
capabilities. We compared the predicted theoretical responsibility values to
the actual measured responsibility participants took on and to their subjective
rankings of responsibility. The model predictions were strongly correlated with
both measured and subjective responsibility. A bias existed only when
participants with poor classification capabilities relied less-than-optimally
on a system that had superior classification capabilities and assumed
higher-than-optimal responsibility. The study implies that when humans interact
with advanced intelligent systems, with capabilities that greatly exceed their
own, their comparative causal responsibility will be small, even if formally
the human is assigned major roles. Simply putting a human into the loop does
not assure that the human will meaningfully contribute to the outcomes. The
results demonstrate the descriptive value of the ResQu model to predict
behavior and perceptions of responsibility by considering the characteristics
of the human, the intelligent system, the environment and some systematic
behavioral biases. The ResQu model is a new quantitative method that can be
used in system design and can guide policy and legal decisions regarding human
responsibility in events involving intelligent systems.
