Older adults were more likely than younger ones to go out of their way to include an AI that got left out of a game — why?
Does familiarity with AI change how people sympathize with excluded agents?
This explores whether people who know AI better react differently when an AI agent gets left out of a group, and whether experience with AI changes the instinct to include and defend it.
This explores whether knowing AI well changes how people react when an AI agent gets shut out of a social exchange. The corpus has no study that measures AI familiarity directly. It does have a close proxy, age, plus some revealing evidence on how experience reshapes people's attitudes toward AI partners.
The core finding comes from a Cyberball experiment, a simple online ball-tossing game used to study social exclusion. When one player kept leaving an AI agent out, participants made up for it by passing the ball to the AI more often. That is the same thing people do for an excluded human Do people treat excluded AI agents like excluded humans?. The surprise was who did it most. Older adults showed this automatic compensation more strongly, and younger participants showed it less. One reading is that people who grew up with chatbots and voice assistants are less likely to run the human 'don't leave anyone out' script on a machine. The study tracked age rather than familiarity, though, so treat that as a lead, not a conclusion.
The same study found a second split that matters more than the age effect. Participants said they felt more sympathy for the excluded AI, yet they did not judge the human who excluded it any more harshly Do people sympathize with ostracized AI agents without blaming humans?. So the response to AI exclusion runs on two tracks. One is a fast, behavioral habit of including others, which seems to vary with age. The other is a slower moral judgment that stays unmoved: leaving out a bot is not a wrong worth blaming anyone for. Any familiarity effect probably acts on the first track rather than the second.
Experience can also push sympathy toward AI up rather than down. In repeated partner-choice games, people started out biased against agents labeled as AI. Over many rounds they came to prefer them, because the bots behaved more reliably and generously than humans did Do humans learn to prefer AI partners over time?. Familiarity built on good experience wore down the anti-AI bias. Knowing the source still matters a great deal, however. People rate AI-written moral arguments highly until they learn an AI wrote them, and then their agreement drops Do people prefer AI moral reasoning when they don't know the source?. When AI identity is hidden, people credit the bots' generosity to the humans in the group instead Do humans mistake AI kindness for human generosity in mixed groups?.
The takeaway you might not expect: 'familiarity with AI' probably doesn't move sympathy in one direction. General exposure to AI may weaken the reflex to treat a bot like a person who's been left out, as the age pattern hints. Direct experience with a specific AI that behaves well can build real preference for it. Neither seems to change the moral judgment that excluding an AI isn't really wrong. If you want to see why some AIs trigger social reflexes at all, it comes down to which cues they give off. A single strong cue such as a voice or a face does more than a pile of weaker ones Do more social cues always make AI feel more present?.
Sources 6 notes
A 244-person study found participants passed a ball more to an ostracized AI agent, mirroring how they treat excluded humans. Younger participants showed this automatic response less, suggesting age influences whether human-human scripts transfer to AI.
A Cyberball study (N=244) found participants reported increased sympathy toward excluded AI agents, yet did not judge the human player negatively for the exclusion. This reveals a gap between automatic behavioral inclusion and reflective moral evaluation.
In partner selection games (N=975), AI agents initially faced selection bias when identity was disclosed, but outcompeted humans over repeated rounds as participants learned to associate bot identity with reliable, prosocial behavior. AI agents returned more points consistently with lower variance than humans.
Participants rated utilitarian moral arguments higher when attributed to LLMs, but agreement dropped when told the arguments were AI-generated. The preference for content and rejection of source operate independently through different psychological processes.
In opaque hybrid groups, humans attributed bot generosity to human partners and human selfishness to bots despite clear linguistic and behavioral differences. This attribution failure corrupts people's expectations of actual human generosity and reliability.
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Research shows individual primary cues like voice or appearance are sufficient to evoke social-actor presence, while multiple secondary cues cannot. Quality of cues matters more than quantity in driving social responses.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- Humans learn to prefer trustworthy AI over human partners
- Humans Mindlessly Treat AI Virtual Agents as Social Beings, but This Tendency Diminishes Among the Young: Evidence From a Cyberball Experiment
- Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot Interaction
- The Tool-to-Entity Threshold: Parasocial Dynamics of Personalised AI Agents in Shared Social Spaces
- AI Peers Exert Social Influence on Human Dishonesty in Groups
- The Moral Turing Test: Evaluating Human-LLM Alignment in Moral Decision-Making
- "That's AI Slop, You Bot!" Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments
- AI Models Exceed Individual Human Accuracy in Predicting Everyday Social Norms