# An HCAI Methodological Framework (HCAI-MF): Putting It Into Action to Enable Human-Centered AI

arXiv: https://arxiv.org/abs/2311.16027 · HTML: https://mlsanity.com/paper/2311.16027/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 155 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 24 of 24)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook](https://mlsanity.com/paper/2301.12243/) (2023)
- [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023)
- [Human-Centered Responsible Artificial Intelligence: Current & Future Trends](https://mlsanity.com/paper/2302.08157/) (2023)
- [Decision-Making in Driver-Automation Shared Control: A Review and Perspectives](https://mlsanity.com/paper/2007.12597/) (2020)
- [Requirements Engineering Framework for Human-centered Artificial Intelligence Software Systems](https://mlsanity.com/paper/2303.02920/) (2023)
- [On Controllability of AI](https://mlsanity.com/paper/2008.04071/) (2020)
- [Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems](https://mlsanity.com/paper/2103.15053/) (2021)
- [Enabling human-centered AI: A new junction and shared journey between AI and HCI communities](https://mlsanity.com/paper/2111.08460/) (2021)
- [New research paradigms and agenda of human factors science in the intelligence era](https://mlsanity.com/paper/2208.12396/) (2022)
- [Responsible AI Implementation: A Human-centered Framework for Accelerating the Innovation Process](https://mlsanity.com/paper/2209.07076/) (2022)
- [Sociotechnical Specification for the Broader Impacts of Autonomous Vehicles](https://mlsanity.com/paper/2205.07395/) (2022)
- [Ontology in Hybrid Intelligence: a concise literature review](https://mlsanity.com/paper/2303.17262/) (2023)

## Built on by (most cited first, 3 of 3)

- [Human-Centered Human-AI Interaction (HC-HAII): A Human-Centered AI Perspective](https://mlsanity.com/paper/2508.03969/) (2025)
- ["Over-the-Hood" AI Inclusivity Bugs and How 3 AI Product Teams Found and Fixed Them](https://mlsanity.com/paper/2510.19033/) (2025)
- [Exploring Human-in-the-Loop Themes in AI Application Development: An Empirical Thematic Analysis](https://mlsanity.com/paper/2603.05510/) (2026)

## Abstract (arXiv)

Human-centered artificial intelligence (HCAI) is a design philosophy that prioritizes humans in the design, development, deployment, and use of AI systems, aiming to maximize AI's benefits while mitigating its negative impacts. Despite its growing prominence in literature, the lack of methodological guidance for its implementation poses challenges to HCAI practice. To address this gap, this paper proposes a comprehensive HCAI methodological framework (HCAI-MF) comprising five key components: HCAI requirement hierarchy, approach and method taxonomy, process, interdisciplinary collaboration approach, and multi-level design paradigms. A case study demonstrates HCAI-MF's practical implications, while the paper also analyzes implementation challenges. Actionable recommendations and a "three-layer" HCAI implementation strategy are provided to address these challenges and guide future evolution of HCAI-MF. HCAI-MF is presented as a systematic and executable methodology capable of overcoming current gaps, enabling effective design, development, deployment, and use of AI systems, and advancing HCAI practice.