# From human teams to hybrid intelligence teams: identifying, characterizing, and evaluating foundational quality attributes

## TL;DR

This paper introduces a quality model for hybrid intelligence systems, combining human and AI teamwork properties to guide system design and evaluation.

## Contribution

The paper proposes a novel quality model for hybrid intelligence teams, derived from human team properties and validated through empirical studies.

## Key findings

- Seven high-level quality attributes for hybrid intelligence teams were identified and refined into 16 specific attributes.
- Empirical evaluation of 48 scenarios confirmed the relevance of the proposed quality attributes.
- The study highlights the importance of human-AI interdependence, competency, and purposefulness in hybrid intelligence systems.

## Abstract

Hybrid Intelligence (HI) is an emerging paradigm in which artificial intelligence (AI) augments human intelligence. The current literature lacks systematic models that guide the design and evaluation of HI systems. Further, discussions around HI primarily focus on technology, neglecting the holistic human-AI ensemble. In this paper, we take the initial steps toward the development of a quality model for characterizing and evaluating HI systems from a human-AI teams perspective. We first conducted a study investigating the adequacy of properties commonly associated with effective human teams to describe HI. The study features the insights of 50 HI researchers, and shows that various human team properties, including boundedness, interdependence, competency, purposefulness, initiative, normativity, and effectiveness, are important for HI systems. Based on these results, we developed a quality model for HI teams composed of seven high-level quality attributes, further refined into 16 specific ones. To evaluate the relevance and understanding of the proposed attributes, we conducted a second empirical investigation by staging competitions in which participants used the quality model to develop and analyze HI usage scenarios. Our analysis of 48 collected scenarios, which we openly release, confirms the proposed attributes’ relevance and highlights insights that emerge when designers consider the quality model in HI system design.