# Human-AI Collaboration: Trade-offs Between Performance and Preferences

arXiv: https://arxiv.org/abs/2503.00248 · HTML: https://mlsanity.com/paper/2503.00248/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://mlsanity.com/paper/1904.08980/) (2019)
- [Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots](https://mlsanity.com/paper/2310.13724/) (2023)
- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration](https://mlsanity.com/paper/2010.09890/) (2020)
- [Inequity aversion improves cooperation in intertemporal social dilemmas](https://mlsanity.com/paper/1803.08884/) (2018)
- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](https://mlsanity.com/paper/2107.07630/) (2021)
- [Cooperating with Machines](https://mlsanity.com/paper/1703.06207/) (2017)
- [Uncalibrated Models Can Improve Human-AI Collaboration](https://mlsanity.com/paper/2202.05983/) (2022)
- [Warmth and competence in human-agent cooperation](https://mlsanity.com/paper/2201.13448/) (2022)
- [A Case for Backward Compatibility for Human-AI Teams](https://mlsanity.com/paper/1906.01148/) (2019)
- [Mixtures of g-priors in Generalized Linear Models](https://mlsanity.com/paper/1503.06913/) (2015)

## Built on by (most cited first, 1 of 1)

- [Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration](https://mlsanity.com/paper/2602.20104/) (2026)

## Abstract (arXiv)

Despite the growing interest in collaborative AI, designing systems that seamlessly integrate human input remains a major challenge. In this study, we developed a task to systematically examine human preferences for collaborative agents. We created and evaluated five collaborative AI agents with strategies that differ in the manner and degree they adapt to human actions. Participants interacted with a subset of these agents, evaluated their perceived traits, and selected their preferred agent. We used a Bayesian model to understand how agents' strategies influence the Human-AI team performance, AI's perceived traits, and the factors shaping human-preferences in pairwise agent comparisons. Our results show that agents who are more considerate of human actions are preferred over purely performance-maximizing agents. Moreover, we show that such human-centric design can improve the likability of AI collaborators without reducing performance. We find evidence for inequality-aversion effects being a driver of human choices, suggesting that people prefer collaborative agents which allow them to meaningfully contribute to the team. Taken together, these findings demonstrate how collaboration with AI can benefit from development efforts which include both subjective and objective metrics.