Enabling Human-Centered AI: A Methodological Perspective

TL;DR

This paper introduces a comprehensive, systematic framework for human-centered AI, aiming to guide practitioners in designing AI systems that prioritize human benefits and mitigate risks.

Contribution

It presents a novel integrated HCAI framework with a three-layer approach to facilitate practical implementation and address current methodological gaps.

Findings

Framework covers design goals, principles, and methods

Three-layer approach enhances implementation feasibility

Systematic framework aims to improve HCAI adoption

Abstract

Human-centered AI (HCAI) is a design philosophy that advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI to humans and avoid potential adverse impacts. While HCAI continues to influence, the lack of guidance on methodology in practice makes its adoption challenging. This paper proposes a comprehensive HCAI framework based on our previous work with integrated components, including design goals, design principles, implementation approaches, interdisciplinary teams, HCAI methods, and HCAI processes. This paper also presents a "three-layer" approach to facilitate the implementation of the framework. We believe this systematic and executable framework can overcome the weaknesses in current HCAI frameworks and the challenges currently faced in practice, putting it into action to enable HCAI further.