Preferences for the decision weight and accountability assignment in risky decision-making under human-machine collaboration contexts

Open Access
Conference Proceedings
Authors: Wei XiongLiuxing TsaoLiang Ma

Abstract: Collaboration between humans and machines has demonstrated considerable potential. In the future, we can assume that humans and machines will collaborate in partnerships and sharing decision outcomes. This prompts us to examine the extent to which machine inputs are introduced and to clarify the accountability for both positive and negative outcomes. We conducted a questionnaire survey through social networks, collecting 123 valid responses. Respondents were tasked with imagining a collaborative scenario with an intelligent machine for a risky decision-making task. We compared decision weights and accountability assignments for decision outcomes (profit and/or loss) under different risky decision-making descriptions. We also analyzed accountability assignments under a range of human-machine partnerships with given decision weights. Our results revealed the preference of humans to take the lead in human-machine partnerships and they were willing to assume more accountability. We also observed significant differences between decision weight and the assignment of accountability for decision outcomes. Interestingly, a gender-based analysis indicated that women tended to favor higher decision weight in scenarios involving loss-sharing descriptions and were more likely to assume more accountability for negative outcomes. Furthermore, under given human-machine decision weights, both men and women participants took more accountability for profits than for losses. In particular, women compared to their male counterparts, tended to attribute significantly more accountability to themselves for losses. This study would facilitate work designs for human-machine teams and contribute to fostering better human-machine relationships.

Keywords: human-machine collaboration, decision weight, accountability assignment, human-machine team (HMT)

DOI: 10.54941/ahfe1004518

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