Unpredictable robots elicit responsibility attributions

Area of Science:

• Human-Robot Interaction
• Social Robotics
• Moral Psychology

Background:

• Existing frameworks for interpreting social robots, like Clark and Fischer's, offer valuable insights.
• However, these models may not fully capture the nuances of human attributions of responsibility to robots.

Purpose of the Study:

• To investigate the extent to which people assign responsibility to robots.
• To identify contexts where responsibility assignment to robots occurs, particularly when actions are unpredictable.

Main Methods:

• Analysis of existing theoretical frameworks on robot interpretation.
• Argumentation based on specific contextual factors influencing responsibility assignment.

Main Results:

• Current frameworks inadequately explain people's willingness to assign responsibility to robots.
• Responsibility is more likely assigned when robot actions deviate from user or programmer expectations.

Conclusions:

• Human-robot interaction involves complex attributions of responsibility.
• Future frameworks should incorporate the impact of unpredictability on assigning agency and accountability to robots.