AI and advanced automation are involved in almost all aspects of our life. In the interaction with such systems, human causal responsibility for the 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 , and were aided by binary automated classification systems with different capabilities. We compared the  theoretical responsibility values , predicted by the ResQu model, to the actual measured responsibility participants took on and to their subjective ranking of responsibility. The ResQu model predictions were strongly correlated with both measured and subjective responsibility. The model generally provided quite accurate predictions of the actual values of the measured responsibility. A bias existed only when participants ' classification capabilities were much worse than those of the automated classification system. In this case, the participants overestimated their own capabilities, relied less-than-optimally on the automated system and assumed greater-than-optimal responsibility. The  results demonstrate the  value of the ResQu model as a descriptive model, considering some systematic deviations. It is possible to compute a ResQu model score to predict behavior or perceptions of responsibility , taking into account the characteristics of the human, the intelligent system and the environment . The ResQu model provides a new quantitative method that may aid system design and  guide policy and legal decisions , regarding human responsibility in events involving intelligent systems.
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.
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