Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30 & n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation. We show an AI Ghostwriter Effect : Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. Personalization of AI-generated texts did not impact the AI Ghostwriter Effect , and higher levels of participants’ influence on texts increased their sense of ownership. Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters. Rationalizations for authorship in AI ghostwriters and human ghostwriters were similar. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.

Psychological targeting tries to influence and manipulate users' behaviour. We investigated whether users can protect themselves from being profiled by a chatbot, which automatically assesses users' personality. Participants interacted twice with the chatbot: (1) They chatted for 45 minutes in customer service scenarios and received their actual profile (baseline). (2) They then were asked to repeat the interaction and to disguise their personality by strategically tricking the chatbot into calculating a falsified profile. In interviews, participants mentioned 41 different strategies but could only apply a subset of them in the interaction. They were able to manipulate all Big Five personality dimensions by nearly 10%. Participants regarded personality as very sensitive data. As they found tricking the AI too exhaustive for everyday use, we reflect on opportunities for privacy protective designs in the context of personality-aware systems.