The DOI matches its Crossref record.
Human-Centered AI
Abstract
Researchers, developers, business leaders, policy makers, and others are expanding the technology-centered scope of artificial intelligence (AI) to include human-centered AI (HCAI) ways of thinking. This expansion from an algorithm-focused view to embrace a human-centered perspective can shape the future of technology so as to better serve human needs. Educators, designers, software engineers, product managers, evaluators, and government agency staffers can build on AI-driven technologies to design products and services that make life better for people and enable people to care for each other. Humans have always been tool builders, and now they are supertool builders, whose inventions can improve our health, family life, education, business, the environment, and much more. The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits AI researchers, developers, business leaders, policy makers, and others who build on their working methods by including HCAI strategies of design and testing. This enlarged vision can shape the future of technology so as to better serve human needs. As many technology companies and thought leaders have said, the goal is not to replace people, but to empower them by making design choices that give humans control over technology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Generative Agents: Interactive Simulacra of Human Behavior
Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023) · arXiv · doi:10.48550/arxiv.2304.03442 · Cited by 2,173
Generative Agents: Interactive Simulacra of Human Behavior
Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023) · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3586183.3606763 · Cited by 2,187
Six Human-Centered Artificial Intelligence Grand Challenges
Ozmen Garibay, O., Winslow, B., Andolina, S., Antona, M., Bodenschatz, A., Coursaris, C., Falco, G., Fiore, S. M., Garibay, I., Grieman, K., et al. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2153320 · Cited by 501
Self-citationHuman-centred learning analytics and AI in education: A systematic literature review
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100215 · Cited by 281
Fu, Y., & Weng, Z. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100306 · Cited by 195
Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works
Ko, H.-K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J. (2023) · Proceedings of the 28th International Conference on Intelligent User Interfaces · doi:10.1145/3581641.3584078 · Cited by 189
Sison, A. J. G., Daza, M. T., Gozalo-Brizuela, R., & Garrido-Merchán, E. C. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2023.2225931 · Cited by 169
Sokol, K., Fackler, J., & Vogt, J. E. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01725-9 · Cited by 105
Transforming Assessment: The Impacts and Implications of Large Language Models and Generative AI
Hao, J., von Davier, A. A., Yaneva, V., Lottridge, S., von Davier, M., & Harris, D. J. (2024) · Educational Measurement: Issues and Practice · doi:10.1111/emip.12602 · Cited by 93
A multifaceted vision of the human-AI collaboration: a comprehensive review
Puerta-Beldarrain, M., Gómez-Carmona, O., Sánchez-Corcuera, R., Casado-Mansilla, D., López-de-Ipiña, D., & Chen, L. (2025) · IEEE Access · doi:10.1109/access.2025.3536095 · Cited by 73
Human-centered AI: ensuring human control while increasing automation
Shneiderman, B. (2022) · Proceedings of the 5th Workshop on Human Factors in Hypertext · doi:10.1145/3538882.3542790 · Cited by 58
Self-citationAdvancing the search frontier with AI agents
White, R. W. (2024) · Communications of the ACM · doi:10.1145/3655615 · Cited by 55
Exploring creativity in human-AI co-creation: a comparative study across design experience
Wang, N., Kim, H., Peng, J., & Wang, J. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1672735 · Cited by 59
AIdeation: Designing a Human-AI Collaborative Ideation System for Concept Designers
Wang, W.-F., Lu, C.-T., Ponsa i Campanyà, N., Chen, B.-Y., & Chen, M. Y. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714148 · Cited by 50
Human-centered artificial intelligence: a field experiment
Krakowski, S., Haftor, D., Luger, J., Pashkevich, N., & Raisch, S. (2026) · Management Science · doi:10.1287/mnsc.2022.03849 · Cited by 53
Deka, C., Shrivastava, A., Abraham, A. K., Nautiyal, S., & Chauhan, P. (2025) · Speech, Language and Hearing · doi:10.1080/2050571x.2024.2359274 · Cited by 44
AI Regulation Is (not) All You Need
Lucaj, L., van der Smagt, P., & Benbouzid, D. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594079 · Cited by 40
Kamath, P., Morreale, F., Bagaskara, P. L., Wei, Y., & Nanayakkara, S. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642040 · Cited by 37
Extending a Human-AI Collaboration Framework with Dynamism and Sociality
Muller, M., & Weisz, J. (2022) · 2022 Symposium on Human-Computer Interaction for Work · doi:10.1145/3533406.3533407 · Cited by 37
Zhang, S., Wang, H., & Yi, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757594 · Cited by 36
Human-Centered AI: A New Synthesis
Shneiderman, B. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-85623-6_1 · Cited by 27
Self-citationThe Impact of Generative AI on Creativity in Software Development: A Research Agenda
Jackson, V., Vasilescu, B., Russo, D., Ralph, P., Prikladnicki, R., Izadi, M., D’Angelo, S., Inman, S., Andrade, A., & van der Hoek, A. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708523 · Cited by 23
Amuse: Human-AI Collaborative Songwriting with Multimodal Inspirations
Kim, Y., Lee, S.-J., & Donahue, C. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713818 · Cited by 23
Reframing deception for human-centered AI
Umbrello, S., & Natale, S. (2024) · International Journal of Social Robotics · doi:10.1007/s12369-024-01184-4 · Cited by 23
Reflection Across AI-based Music Composition
Ford, C., Noel-Hirst, A., Cardinale, S., Loth, J., Sarmento, P., Wilson, E., Wolstanholme, L., Worrall, K., & Bryan-Kinns, N. (2024) · Creativity and Cognition · doi:10.1145/3635636.3656185 · Cited by 22
Invisible to Machines: Designing AI that Supports Vision Work in Radiology
Anichini, G., Natali, C., & Cabitza, F. (2024) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-024-09491-0 · Cited by 18
Kelly, S., Kaye, S.-A., White, K. M., & Oviedo-Trespalacios, O. (2025) · AI & SOCIETY · doi:10.1007/s00146-025-02334-6 · Cited by 17
Piecing Together Teamwork: A Responsible Approach to an LLM-based Educational Jigsaw Agent
Doherty, E., Perkoff, E. M., von Bayern, S., Zhang, R., Dey, I., Bodzianowski, M., Puntambekar, S., & Hirshfield, L. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713349 · Cited by 12
Fotaris, P. (2026) · Cogent Arts & Humanities · doi:10.1080/23311983.2026.2722084
Limits of Interventions in AI-Assisted Decision Making
Shrestha, S., Ahn, E., & Vorobeychik, Y. (2026) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v9i4.43309
Metadata
- Authors
- Shneiderman, B.
- Publisher
- Oxford University PressOxford
- Published
- 2022-01-13
- OpenAlex
- W4213145388
- Cited by
- 955
- References
- 0
- FWCI
- 193.94
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.