A Task-Driven Human-AI Collaboration: When to Automate, When to Collaborate, When to Challenge

TL;DR

This paper introduces a task-driven framework for human-AI collaboration that assigns AI roles based on task requirements, aiming to improve synergy and moral soundness.

Contribution

It proposes a novel approach to human-AI cooperation by categorizing AI roles according to task analysis, enhancing effectiveness and ethical considerations.

Findings

Identifies three AI roles: autonomous, assistive, and adversarial.

Shows how proper role assignment improves human-AI performance.

Provides structured guidance for context-sensitive automation.

Abstract

According to several empirical investigations, despite enhancing human capabilities, human-AI cooperation frequently falls short of expectations and fails to reach true synergy. We propose a task-driven framework that reverses prevalent approaches by assigning AI roles according to how the task's requirements align with the capabilities of AI technology. Three major AI roles are identified through task analysis across risk and complexity dimensions: autonomous, assistive/collaborative, and adversarial. We show how proper human-AI integration maintains meaningful agency while improving performance by methodically mapping these roles to various task types based on current empirical findings. This framework lays the foundation for practically effective and morally sound human-AI collaboration that unleashes human potential by aligning task attributes to AI capabilities. It also provides structured guidance for context-sensitive automation that complements human strengths rather than replacing human judgment.