Artificial intelligence (AI) agents are often designed with stereotype-consistent social cues—such as feminized voice assistants in service roles or masculine security robots—to increase acceptance and interaction fluency. Yet these same design choices may reinforce the stereotypes they draw on. Recent work, however, proposes a more optimistic possibility: counter-stereotypical AI—for example, a female-coded technical chatbot or male-coded care robot—may reduce prejudice by functioning as synthetic intergroup contact, repeatedly exposing users to agents that serve as proxy members of social groups and challenge familiar stereotypes. We build on this account but argue that its implications depend critically on whether AI supplements or replaces human interaction. When counter-stereotypical AI adds exposure to underrepresented groups, it may challenge stereotypes by broadening the range of people users associate with particular traits or domains. When it substitutes for humans, however, its effects become more ambivalent. We identify three risks of counter-stereotypical AI as replacement: non-integration, when users treat the agent as a designed persona rather than revising beliefs about people; substitution, when synthetic contact displaces the reciprocal and agentic qualities of human contact; and illusion of inclusion, when visible AI representation is mistaken for institutional inclusion. We position replacement by counter-stereotypical AI as a critical boundary condition for theories of synthetic contact and representational AI, and as a key context for future research on prejudice, technology, and social change.
Research increasingly uses voice and audio features to understand human-technology relationships. But findings regarding AI and spoken communication often appear somewhat contradictory. People can be blunt or even aggressive toward chatbots, speak more politely to voice assistants, and self-censor themselves when AI listens in the background during a call. Why? To address this heterogeneity, we propose a Conversational Role Framework that distinguishes whether the AI is (1) a conversation Partner that is directly addressed (e.g., a chatbot), (2) a Filter that transforms speech signals before another person receives it (e.g., a real-time translator that re-voices a speaker), or (3) an Ambient Listener that silently records, transcribes, and summarizes a human exchange (e.g., an AI note-taker summarizing a doctor-patient conversation). This framework helps explain seemingly conflicting findings and sheds light on why the same vocal cue can capture different meanings depending on AI's role in a given interaction.
Negotiation research was among the first to document zero-sum thinking, in the form of the fixed-pie bias: treating negotiations as win-lose interactions and thus missing win-win opportunities. Recently, research on zero-sum thinking has reemerged, largely outside negotiation, documenting zero-sum perceptions of status, social relations, and even everyday exchange. We offer a three-part framework that brings these advances back to negotiation. First, we review evidence linking zero-sum thinking to negotiation avoidance, in part through the fear of conflict. Second, we propose that the desire to “win” may lead negotiators to accept deals that are worse than their outside options and to reject deals that are better. Third, we propose that zero-sum thinking may lead negotiators to undervalue the relationships on which a deal's value depends. These arguments suggest that zero-sum thinking in negotiation extends well beyond its best-known form, the fixed-pie bias.
Generative artificial intelligence (GenAI) is increasingly used for creative tasks, but whether and when it supports or undermines creativity is an open question. This article reviews emerging behavioral research on GenAI in creativity and reveals three central and interrelated tensions from this literature: GenAI may (1) increase collective creative productivity while reducing diversity, (2) stimulate idea generation or induce fixation, and (3) enhance or undermine creators' motivation and engagement in creative tasks. Two factors connect these tensions: GenAI’s tendency to produce many but insufficiently diverse ideas and the role assignment between humans and GenAI. Together, these insights outline boundary conditions for effective GenAI use and implications for future research.
