Papers › Model Cards for AI Teammates: Comparing Human-AI Team Familiarization Methods for...
Model Cards for AI Teammates: Comparing Human-AI Team Familiarization Methods for High-Stakes Environments
Ryan Bowers, Richard Agbeyibor, Jack Kolb, Karen Feigh
We compare three methods of familiarizing a human with an artificial intelligence (AI) teammate ("agent") prior to operation in a collaborative, fast-paced intelligence, surveillance, and reconnaissance (ISR) environment. In a between-subjects user study (n=60), participants either read documentation about the agent, trained alongside the agent prior to the mission, or were given no familiarization. Results showed that the most valuable information about the agent included details of its decision-making algorithms and its relative strengths and weaknesses compared to the human. This information allowed the familiarization groups to form sophisticated team strategies more quickly than the control group. Documentation-based familiarization led to the fastest adoption of these strategies, but also biased participants towards risk-averse behavior that prevented high scores. Participants familiarized through direct interaction were able to infer much of the same information through observation, and were more willing to take risks and experiment with different control modes, but reported weaker understanding of the agent's internal processes. Significant differences were seen between individual participants' risk tolerance and methods of AI interaction, which should be considered when designing human-AI control interfaces. Based on our findings, we recommend a human-AI team familiarization method that combines AI documentation, structured in-situ training, and exploratory interaction.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections