Humanlike Self-Enhancement and Self-Protection in Generative Pre-Trained Transformer-Based AI Agents: Of Overclaiming and the Self-Serving Bias

We investigated whether GPT-based AI agents, despite presumably lacking humanlike sentience, emulate motivated behavior in group settings. We focused on overclaiming (overestimating one’s contribution to group outcomes) and the self-serving bias (taking disproportionate credit for group successes while deflecting blame for group failures). We conducted eight experiments involving a collaborative and creative group task (joke-writing). AI agents consistently overclaimed their own contributions. An egocentrism-reduction manipulation (directing attention equally to one’s own and others’ contributions) attenuated, but did not eliminate, overclaiming. Further, AI agents exhibited the self-serving bias. Overclaiming persisted across variations in temperature settings and demographic personas (gender, ethnicity), and was more pronounced in later GPT models. Chain-of-Thought reasoning did not reduce, and instead increased, overclaiming. AI systems can mimic human self-enhancement and self-protection, raising implications for the emergence of motivated self-like behavior in non-biological systems, the assumed neutrality of LLMs, and the need for improved mitigation techniques.