# Human-Centered Responsible Artificial Intelligence: Current & Future Trends

arXiv: https://arxiv.org/abs/2302.08157 · HTML: https://mlsanity.com/paper/2302.08157/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [How Different Groups Prioritize Ethical Values for Responsible AI](https://mlsanity.com/paper/2205.07722/) (2022)
- [Whose AI Dream? In search of the aspiration in data annotation](https://mlsanity.com/paper/2203.10748/) (2022)
- [Overcoming Failures of Imagination in AI Infused System Development and Deployment](https://mlsanity.com/paper/2011.13416/) (2020)
- ["Look! It's a Computer Program! It's an Algorithm! It's AI!": Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?](https://mlsanity.com/paper/2108.11486/) (2021)

## Built on by (most cited first, 8 of 8)

- [Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions](https://mlsanity.com/paper/2308.02312/) (2023)
- [OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews](https://mlsanity.com/paper/2509.00285/) (2025)
- [An HCAI Methodological Framework (HCAI-MF): Putting It Into Action to Enable Human-Centered AI](https://mlsanity.com/paper/2311.16027/) (2023)
- [Making Data Work Count](https://mlsanity.com/paper/2311.18046/) (2023)
- [The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platform](https://mlsanity.com/paper/2504.06016/) (2025)
- [FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing](https://mlsanity.com/paper/2309.12877/) (2023)
- [Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals](https://mlsanity.com/paper/2504.00860/) (2025)
- ["I'm happy even though it's not real": GenAI Photo Editing as a Remembering Experience](https://mlsanity.com/paper/2602.03104/) (2026)

## Abstract (arXiv)

In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. While different research communities may use different terminology to discuss similar topics, all of this work is ultimately aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map current and future research trends to advance this important area of research by fostering collaboration and sharing ideas.