# Enabling Human-Centered AI: A Methodological Perspective

arXiv: https://arxiv.org/abs/2311.06703 · HTML: https://mlsanity.com/paper/2311.06703/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI](https://mlsanity.com/paper/2105.05424/) (2021)
- [Requirements Practices and Gaps When Engineering Human-Centered Artificial Intelligence Systems](https://mlsanity.com/paper/2301.10404/) (2023)

## Abstract (arXiv)

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.