Humans are perceived as better, but weaker, than artificial intelligence: A comparison of affective impressions of humans, AIs, and computer systems in roles on teams - Daniel B. Shank (Elsevier, 2021)
Attributions of affective meaning – goodness, power, and activity – are important for understanding how people interact with both human and technological agents. Recent evidence indicates that labels of artificial intelligence (AI) and computer system differ in affective impressions, but it is unknown if people perceived affective differences when the AI or computer is an agent with a specific human-like role. For humans, roles (e.g., an online recruiter, a product assembling employee, a resource distributor) have stable affective meaning creating a stable social structure and predictable interaction patterns. In this paper, we examine how the 59 roles affectively differ based on whether they describe a human, a computer, or an AI identity (e.g., a product assembling employee, a product assembling computer system, a product assembling artificial intelligence). In an online study, participants (N ​= ​549) perceived both humans and computer systems as better (higher in goodness) than AIs, while also perceiving humans as weaker and less active than either computer systems or AIs. We also examine these role-identities in the context of team membership (e.g., a product assembling employee is part of an assembly line team), to understand whether the additional context of a team influences affective impressions. For goodness and power, nearly identical results indicate that team context makes no difference to affective perceptions; however human role-identities get a boost in activity when on teams, therefore wiping out the human-technology differences.