AI and advanced automation are involved in almost all aspects of our life. In  such systems, human 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 HCI . In two laboratory experiments, participants performed an aided decision task . We compared the theoretical responsibility values  to the actual responsibility a person took on and to the subjectively perceived responsibility. The ResQu model predictions were strongly correlated with the measured and the subjective responsibility. However, observed values differed from the model predictions, as less knowledgeable participants overestimated their own capabilities  and assumed greater-than-optimal responsibility. The results demonstrate the value of the ResQu model as a descriptive model, considering some systematic deviations. It can be used to aid system design and guide policy and legal decisions  regarding human responsibility in events involving intelligent systems.
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.
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the interaction with
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responsibility for
causal responsibility for the
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HCI
the interaction with intelligent systems
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an aided decision task
a classification task, and were aided by binary automated classification systems with different capabilities
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, predicted by the ResQu model,
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responsibility a person
measured responsibility participants
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the subjectively perceived
their subjective ranking of
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the measured and the
both measured and
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However, observed values differed from the model predictions, as less knowledgeable
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
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, relied less-than-optimally on the automated system
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can be used to
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
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,