Integrating HCI Principles in AI: A Review of Human-Centered Artificial Intelligence Applications and Challenges
DOI:
https://doi.org/10.62411/faith.3048-3719-47Keywords:
Artificial Intelligence, Human-AI Interaction, Human-Centered Artificial Intelligence, Human-Computer Interaction, User Experience of AI DesignAbstract
This review explores the integration of Human-Computer Interaction (HCI) principles in AI to advance Human-Centered Artificial Intelligence (HCAI). It highlights how these fields intersect to create user-friendly AI systems that enhance human capabilities and align with human values. Given the recent interest of HCI in user-centered design and AI in technical innovation, this paper bridges this divide by infusing principles from HCI into AI systems. Relevant peer-reviewed articles, conference papers, and case studies have been selected from leading databases like IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar, encompassing publications from 2017 to 2024. The inclusion criteria for the review focus on interdisciplinary approaches, real-world applications, and challenges of HCAI, while studies that do not have a clear methodology or lack relevance to HCAI were excluded. This paper identifies some of the key gaps, highlights the successful applications of HCAI across healthcare, edu-cation, and entertainment, and discusses various challenges that have arisen, such as bias, transparency, and balancing automation with human control. Findings reveal that iterative design and hu-man-centered frameworks will lead to better usability and ethical fit for HCAI, but significant challenges remain. This study proposes an integrative framework for bringing HCI principles into AI design through interdisciplinary collaboration in developing systems that will enhance human capabilities while considering ethical aspects. Future directions include responsible AI, personalized healthcare, and effective human-AI collaboration.
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M. De Choudhury, M. K. Lee, H. Zhu, and D. A. Shamma, “Introduction to this special issue on unifying human computer interaction and artificial intelligence,” Human–Computer Interact., vol. 35, no. 5–6, pp. 355–361, Nov. 2020, doi: 10.1080/07370024.2020.1744146.
Interaction Design Foundation - IxDF, “What is Human-Centered AI (HCAI)?,” Interaction Design Foundation - IxDF, 2024. https://www.interaction-design.org/literature/topics/human-centered-ai (accessed Sep. 09, 2024).
HAI Stanford, “Human-Centered Artificial Intelligence,” HAI Stanford. https://hai.stanford.edu/about/values
J. M. Carroll, “Human–computer interaction: psychology as a science of design,” Int. J. Hum. Comput. Stud., vol. 46, no. 4, pp. 501–522, Apr. 1997, doi: 10.1006/ijhc.1996.0101.
S. J. H. Yang, H. Ogata, T. Matsui, and N.-S. Chen, “Human-centered artificial intelligence in education: Seeing the invisible through the visible,” Comput. Educ. Artif. Intell., vol. 2, p. 100008, 2021, doi: 10.1016/j.caeai.2021.100008.
S. Russell and P. Norvig, Artificial intelligence, 3rd ed. Upper Saddle River, NJ: Pearson, 2009.
J. Grudin, “AI and HCI: Two Fields Divided by a Common Focus,” AI Mag., vol. 30, no. 4, pp. 48–57, Dec. 2009, doi: 10.1609/aimag.v30i4.2271.
J. Auernhammer, “Human-centered AI: The role of Human-centered Design Research in the development of AI,” in DRS International Conference 2020, Sep. 2020. doi: 10.21606/drs.2020.282.
A. Adadi and M. Berrada, “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 6, pp. 52138–52160, 2018, doi: 10.1109/ACCESS.2018.2870052.
S. Panda and S. T. Roy, “Reflections on emerging HCI–AI research,” AI Soc., vol. 39, no. 1, pp. 407–409, Feb. 2024, doi: 10.1007/s00146-022-01409-y.
C. R. Flores-Carballo, G. A. Molina-Arenas, A. Macias, K. Caro, J. Beltran, and L. A. Castro, “Speaker Identification in Interactions between Mothers and Children with Down Syndrome via Audio Analysis: A Case Study in Mexico,” Int. J. Human–Computer Interact., vol. 39, no. 9, pp. 1922–1937, May 2023, doi: 10.1080/10447318.2022.2090610.
S. Bhatia Khan and S. Chandna, “Introduction to human-computer interaction using artificial intelligence,” in Innovations in Artificial Intelligence and Human-Computer Interaction in the Digital Era, Elsevier, 2023, pp. 1–6. doi: 10.1016/B978-0-323-99891-8.00009-7.
R. Bond et al., “Human Centered Artificial Intelligence: Weaving UX into Algorithmic Decision Making,” in RoCHI 2019: International Conference on Human-Computer Interaction, 2019, pp. 2–9. [Online]. Available: https://pure.ulster.ac.uk/en/publications/human-centered-artificial-intelligence-weaving-ux-into-algorithmi
C. Kolski, G. A. Boy, G. Melançon, M. Ochs, and J. Vanderdonckt, “Cross-Fertilisation Between Human-Computer Interaction and Artificial Intelligence,” in A Guided Tour of Artificial Intelligence Research, Cham: Springer International Publishing, 2020, pp. 365–388. doi: 10.1007/978-3-030-06170-8_11.
H. Torkamaan, M. Tahaei, S. Buijsman, Z. Xiao, D. Wilkinson, and B. P. Knijnenburg, “The Role of Human-Centered AI in User Modeling, Adaptation, and Personalization—Models, Frameworks, and Paradigms,” in A Human-Centered Perspective of Intelligent Personalized Environments and Systems, 2024, pp. 43–84. doi: 10.1007/978-3-031-55109-3_2.
W. Xu, M. J. Dainoff, L. Ge, and Z. Gao, “Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI,” Int. J. Human–Computer Interact., vol. 39, no. 3, pp. 494–518, Feb. 2023, doi: 10.1080/10447318.2022.2041900.
H. Reese, “A Human-Centered Approach to the AI Revolution,” HAI Stanford, 2022. https://hai.stanford.edu/news/human-centered-approach-ai-revolution
J. Shabbir and T. Anwer, “Artificial Intelligence and its Role in Near Future,” ArXiv. Apr. 01, 2018. [Online]. Available: http://arxiv.org/abs/1804.01396
W. Xu, M. J. Dainoff, L. Ge, and Z. Gao, “Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI,” Int. J. Human–Computer Interact., vol. 39, no. 3, pp. 494–518, Feb. 2023, doi: 10.1080/10447318.2022.2041900.
M. Alrizq, S. Ali Solangi, A. Alghamdi, M. Ali Nizamani, M. Ali Memon, and M. Hamdi, “An Architecture Supporting Intelligent Mobile Healthcare Using Human-Computer Interaction HCI Principles,” Comput. Syst. Sci. Eng., vol. 40, no. 2, pp. 557–569, 2022, doi: 10.32604/csse.2022.018800.
A. Rahman, “How Babylon Health is using AI to provide online healthcare services,” NS Medical Devices, 2019. https://www.nsmedicaldevices.com/analysis/babylon-health-ai-health-services/
Babylon, “Babylon User Guide.” https://assets.babylonhealth.com/business/Babylon-user-guide.pdf
B. Shneiderman, “Human-Centered AI: A New Synthesis,” in Human-Computer Interaction – INTERACT 2021, 2021, pp. 3–8. doi: 10.1007/978-3-030-85623-6_1.
M. Lytvyn, “A Framework for Responsible AI in Education,” Grammarly, 2023. https://www.grammarly.com/blog/institutions/responsible-ai-education/ (accessed Dec. 12, 2024).
C. S. Nam, J.-Y. Jung, and S. Lee, Eds., Human-Centered Artificial Intelligence. Elsevier, 2022. doi: 10.1016/C2020-0-02460-6.
L. C. Xue et al., Accelerating IBM watsonx.data with IBM Fusion HCI. [Online]. Available: https://www.redbooks.ibm.com/abstracts/redp5720.html
Y. Lu, Y. Yang, Q. Zhao, C. Zhang, and T. J.-J. Li, “AI Assistance for UX: A Literature Review Through Human-Centered AI,” arXiv. Feb. 08, 2024. [Online]. Available: http://arxiv.org/abs/2402.06089
J. V. Wright, “Announcing new generative AI experiences in Google Workspace,” Google Workspace, 2023. https://workspace.google.com/blog/product-announcements/generative-ai
L. Reid, “Generative AI in Search: Let Google do the searching for you,” Blog Google, 2024. https://blog.google/products/search/generative-ai-google-search-may-2024/
B. Cohen, “How Spotify Uses AI to Create an Ultra-Personalized Customer Experience and What Distributors Can Learn from It,” Distribution Strategy, 2022. https://distributionstrategy.com/how-spotify-uses-ai-to-create-an-ultra-personalized-customer-experience-and-what-distributors-can-learn-from-it/
Spotify Design Team, “Introducing Spotify’s New Design Principles,” Spotify Design, 2020. https://spotify.design/article/introducing-spotifys-new-design-principles
A. Trotta, M. Ziosi, and V. Lomonaco, “The future of ethics in AI: challenges and opportunities,” AI Soc., vol. 38, no. 2, pp. 439–441, Apr. 2023, doi: 10.1007/s00146-023-01644-x.
V. C. Müller, “Ethics of Artificial Intelligence and Robotics,” The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, 2023. [Online]. Available: https://plato.stanford.edu/cgi-bin/encyclopedia/archinfo.cgi?entry=ethics-ai
P. Gujar, “Building Trust In AI: Overcoming Bias, Privacy And Transparency Challenges,” Forbes, 2024. https://www.forbes.com/councils/forbestechcouncil/2024/11/19/building-trust-in-ai-overcoming-bias-privacy-and-transparency-challenges/?form=MG0AV3 (accessed Dec. 15, 2024).
M. Lavanchy, “Amazon’s sexist hiring algorithm could still be better than a human,” imd.org, 2018. https://www.imd.org/research-knowledge/digital/articles/amazons-sexist-hiring-algorithm-could-still-be-better-than-a-human/ (accessed Dec. 15, 2024).
Differential Privacy Team, “Learning with Privacy at Scale,” Machine Learning Research - Apple, 2017. https://machinelearning.apple.com/research/learning-with-privacy-at-scale (accessed Dec. 15, 2024).
F. Flemisch, M. Heesen, T. Hesse, J. Kelsch, A. Schieben, and J. Beller, “Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations,” Cogn. Technol. Work, vol. 14, no. 1, pp. 3–18, Mar. 2012, doi: 10.1007/s10111-011-0191-6.
A. Jatavallabha, “Tesla’s Autopilot: Ethics and Tragedy,” Sep. 2024, [Online]. Available: http://arxiv.org/abs/2409.17380
O. Ozmen Garibay et al., “Six Human-Centered Artificial Intelligence Grand Challenges,” Int. J. Human–Computer Interact., vol. 39, no. 3, pp. 391–437, Feb. 2023, doi: 10.1080/10447318.2022.2153320.
T. Abbas, “Boeing Crisis Management Case Study: A Detailed Analysis,” Change Management Insight, 2023. https://changemanagementinsight.com/boeing-crisis-management-case-study-a-detailed-analysis/ (accessed Dec. 15, 2024).
S. Amershi, M. Cakmak, W. B. Knox, and T. Kulesza, “Power to the People: The Role of Humans in Interactive Machine Learning,” AI Mag., vol. 35, no. 4, pp. 105–120, Dec. 2014, doi: 10.1609/aimag.v35i4.2513.
B. Shneiderman, “Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy,” Int. J. Human–Computer Interact., vol. 36, no. 6, pp. 495–504, Apr. 2020, doi: 10.1080/10447318.2020.1741118.
R. Binns, “On the apparent conflict between individual and group fairness,” in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Jan. 2020, pp. 514–524. doi: 10.1145/3351095.3372864.
I. Rahwan et al., “Machine behaviour,” Nature, vol. 568, no. 7753, pp. 477–486, Apr. 2019, doi: 10.1038/s41586-019-1138-y.
A. Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Inf. Fusion, vol. 58, pp. 82–115, Jun. 2020, doi: 10.1016/j.inffus.2019.12.012.
S. Dolgikh and O. Mulesa, “Collaborative Human-AI Decision-Making Systems,” in International Scientific Symposium «Intelligent Solutions» IntSol-2021, 2021, pp. 96–105. [Online]. Available: https://ceur-ws.org/Vol-3106/Paper_9.pdf
F. Doshi-Velez and B. Kim, “Towards A Rigorous Science of Interpretable Machine Learning,” arXiv. Feb. 27, 2017. [Online]. Available: http://arxiv.org/abs/1702.08608
V. Dignum, Responsible Artificial Intelligence. Cham: Springer International Publishing, 2019. doi: 10.1007/978-3-030-30371-6.
H. Suresh and J. Guttag, “A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle,” in Equity and Access in Algorithms, Mechanisms, and Optimization, Oct. 2021, pp. 1–9. doi: 10.1145/3465416.3483305.
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A Survey on Bias and Fairness in Machine Learning,” ACM Comput. Surv., vol. 54, no. 6, pp. 1–35, Jul. 2022, doi: 10.1145/3457607.
A. Abdul, J. Vermeulen, D. Wang, B. Y. Lim, and M. Kankanhalli, “Trends and Trajectories for Explainable, Accountable and Intelligible Systems,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Apr. 2018, pp. 1–18. doi: 10.1145/3173574.3174156.
W. Xu, Z. Gao, and M. Dainoff, “An HCAI Methodological Framework: Putting It Into Action to Enable Human-Centered AI,” ArXiv. Nov. 27, 2023. [Online]. Available: http://arxiv.org/abs/2311.16027
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