Theoretical, Measured and Subjective Responsibility in Aided Decision Making
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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.
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