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Trust and robotics: a multi-staged decision-making approach to robots in community

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Abstract

With the desired outcome of social good within the wider robotics ecosystem, trust is identified as the central adhesive of the human–robot interaction (HRI) interface. However, building trust between humans and robots involves more than improving the machine’s technical reliability or trustworthiness in function. This paper presents a holistic, community-based approach to trust-building, where trust is understood as a multifaceted and multi-staged looped relation that depends heavily on context and human perceptions. Building on past literature that identifies dispositional and learned stages of trust, our proposed decision to trust model considers more extensively the human and situational factors influencing how trust manifests within social relations. Priority is given to the human user of technology—the initiator of human—robot trust relations—at all stages of decision-making. The envisioned formation of optimal conditions in which trust emerges requires the collective participation of practitioners, policymakers, and members of the community. With trust facilitating the smooth transition of robots into more socially embedded roles, positive receptivity of the best engineering project arises from the presence of harmonious robot-human trust relations in community spaces.

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Notes

  1. The scope of our discussion does not cover such industrial robots, since they are designed to primarily serve functional purposes and elements of human interaction are not of essential significance.

  2. Notably, emotions do not necessarily equate to irrationality that hinders optimal decision making [Lerner et al. 2015]. They can manifest as an efficient tool in decision-making that helps avoid excessively effortful cognitive processes.

  3. On the other hand, humans also have the tendency to underestimate the likelihood of low-probability, high-risk events, in part because people lack direct experience with such risk and the risks are less salient in the human mind. For instance, despite road accidents being one of the leading causes of death in the U.S., motorists tend to perceive their risk of suffering a severe accident as very low [Camerer and Kunreuther 1989].

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This research is supported by the National Research Foundation, Singapore under its Emerging Areas Research Projects (EARP) Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.

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  1. Centre for AI and Data Governance, Yong Pung How School of Law, Singapore Management University, Singapore, Singapore

    Wenxi Zhang, Willow Wong & Mark Findlay

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Zhang, W., Wong, W. & Findlay, M. Trust and robotics: a multi-staged decision-making approach to robots in community. AI & Soc (2023). https://doi.org/10.1007/s00146-023-01705-1

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