Fairness and Efficiency in Human-Agent Teams: An Iterative Algorithm Design Approach

TL;DR

This paper develops a new fairness metric and an algorithm for task allocation in human-agent teams, considering both capabilities and preferences from a first-person perspective to improve perceived fairness and team efficiency.

Contribution

It introduces a first-person fairness metric, fair-equity, and the Fair-Efficient Algorithm (FEA) that better aligns with human perceptions of fairness in team settings.

Findings

Agents balancing efficiency and fairness are perceived as fairer by humans.

The new fairness metric, fair-equity, improves alignment with human perceptions.

The FEA outperforms previous methods in human-agent team simulations.

Abstract

When agents interact with people as part of a team, fairness becomes an important factor. Prior work has proposed fairness metrics based on teammates' capabilities for task allocation within human-agent teams. However, most metrics only consider teammate capabilities from a third-person point of view (POV). In this work, we extend these metrics to include task preferences and consider a first-person POV. We leverage an iterative design method consisting of simulation data and human data to design a task allocation algorithm that balances task efficiency and fairness based on both capabilities and preferences. We first show that these metrics may not align with people's perceived fairness from a first-person POV. In light of this result, we propose a new fairness metric, fair-equity, and the Fair-Efficient Algorithm (FEA). Our findings suggest that an agent teammate who balances efficiency and fairness based on equity will be perceived to be fairer and preferred by human teammates in various human-agent team types. We suggest that the perception of fairness may also depend on a person's POV.