Human Centered AI Integrating Ethical Psychological and Computational Perspectives for Inclusive Innovation

Lina Nurjanah; Roby Syaiful Ubed; Prabawati Nurhabibah; Marta Rodriguez

doi:10.33050/7bgr2024

Authors

Lina Nurjanah IPB University Author
Roby Syaiful Ubed Polytechnic of State Finance STAN Author
Prabawati Nurhabibah Semarang State University Author
Marta Rodriguez Eduaward Incorporation Author

DOI:

https://doi.org/10.33050/7bgr2024

Keywords:

Human-Centered AI, Empathy, Responsible AI, Psychological, Dimension

Abstract

The rapid evolution of Artificial Intelligence (AI) has reshaped social, economic, and cultural landscapes, yet its development often prioritizes technical efficiency over human values. This study proposes the Human-Centered AI Integration Framework, a multidisciplinary model that unites ethical, psychological, and computational perspectives to promote inclusive and responsible AI innovation. Employing a mixed-method and Design Science Research (DSR) approach, data were gathered from literature studies, user surveys, and AI system analyses to identify gaps between ethical principles, user perception, and algorithmic design. The proposed framework consists of three interrelated layers: the Ethical Layer, emphasizing fairness, accountability, and transparency; the Psychological Layer, focusing on trust, empathy, and human experience; and the Computational Layer, ensuring algorithmic integrity through bias mitigation and explainability. Evaluation results from interdisciplinary experts confirm that the model effectively bridges human values with technical implementation, enhancing trust, inclusivity, and transparency across AI systems. This research contributes to the growing discourse on responsible AI by providing a holistic foundation for designing systems that are not only intelligent and efficient but also empathetic, equitable, and aligned with human well-being.

Downloads

Download data is not yet available.

References

[1] A. Saxena, G. Fletcher, and M. Pechenizkiy, “Fairsna: Algorithmic fairness in social network analysis,”ACM Computing Surveys, vol. 56, no. 8, pp. 1–45, 2024.

[2] V. Hassija, V. Chamola, A. Mahapatra, A. Singal, D. Goel, K. Huang, S. Scardapane, I. Spinelli, M. Mahmud, and A. Hussain, “Interpreting black-box models: a review on explainable artificial intelligence,” Cognitive Computation, vol. 16, no. 1, pp. 45–74, 2024.

[3] M. O. Syaidina, R. Fahrudin, and I. A. Mutiara, “Implementation of ethics of using artificial intelligence in the education system in indonesia,” Blockchain Frontier Technology, vol. 4, no. 1, pp. 63–71, 2024.

[4] Z. Chen, J. M. Zhang, M. Hort, M. Harman, and F. Sarro, “Fairness testing: A comprehensive survey and analysis of trends,” ACM Transactions on Software Engineering and Methodology, vol. 33, no. 5, pp.1–59, 2024.

[5] T. Mariyanti, I. Wijaya, C. Lukita, S. Setiawan, and E. Fletcher, “Ethical framework for artificial intelligence and urban sustainability,” Blockchain Frontier Technology, vol. 4, no. 2, pp. 98–108, 2025.

[6] O. Parraga, M. D. More, C. M. Oliveira, N. S. Gavenski, L. S. Kupssinsk¨u, A. Medronha, L. V. Moura, G. S. Sim˜oes, and R. C. Barros, “Fairness in deep learning: A survey on vision and language research,” ACM Computing Surveys, vol. 57, no. 6, pp. 1–40, 2025.

[7] A. Fabris, N. Baranowska, M. J. Dennis, D. Graus, P. Hacker, J. Saldivar, F. Zuiderveen Borgesius, and A. J. Biega, “Fairness and bias in algorithmic hiring: A multidisciplinary survey,” ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 1, pp. 1–54, 2025.

[8] A. Erica, S. Wulandari, and R. Widayanti, “Data security transformation: The significant role of blockchain technology,” Blockchain Frontier Technology, vol. 3, no. 2, pp. 107–112, 2024.

[9] E. Soremekun, M. Papadakis, M. Cordy, and Y. Le Traon, “Software fairness: An analysis and survey,” ACM Computing Surveys, vol. 58, no. 3, pp. 1–38, 2025.

[10] T. P. Pagano, R. B. Loureiro, F. V. Lisboa, R. M. Peixoto, G. A. Guimar˜aes, G. O. Cruz, M. M. Araujo, L. L. Santos, M. A. Cruz, E. L. Oliveira et al., “Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods,” Big

data and cognitive computing, vol. 7, no. 1, p. 15, 2023.

[11] C. Yu, G. Yao et al., “Enhancing student engagement with ai-driven personalized learning systems,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 1–8, 2024.

[12] S. Das, R. Stanton, and N. Wallace, “Algorithmic fairness,” Annual Review of Financial Economics, vol. 15, no. 1, pp. 565–593, 2023.

[13] Badan Riset dan Inovasi Nasional (BRIN), “Ai and blockchain as keys to national digital transformation,” Online news release, Sep. 2025, highlights AI (and blockchain) as essential for Indonesia’s digital transformation and national development. [Online]. Available: https: //www.brin.go.id/en/news/124783/brin-ai-and-blockchain-as-a-keys-to-national-digital-transformation

[14] D. Pessach and E. Shmueli, “A review on fairness in machine learning,” ACM Computing Surveys (CSUR), vol. 55, no. 3, pp. 1–44, 2022.

[15] D. Martinez, L. Magdalena, and A. N. Savitri, “Ai and blockchain integration: Enhancing security and transparency in financial transactions,” International Transactions on Artificial Intelligence, vol. 3, no. 1, pp. 11–20, 2024.

[16] M. Hort, Z. Chen, J. M. Zhang, M. Harman, and F. Sarro, “Bias mitigation for machine learning classifiers: A comprehensive survey,” ACM Journal on Responsible Computing, vol. 1, no. 2, pp. 1–52, 2024.

[17] S. Dehdashtian, R. He, Y. Li, G. Balakrishnan, N. Vasconcelos, V. Ordonez, and V. N. Boddeti, “Fairness and bias mitigation in computer vision: A survey,” arXiv preprint arXiv:2408.02464, 2024.

[18] G. Silva, G. Godwin, and O. Jayanagara, “The impact of ai on personalized learning and educational analytics,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 36–46, 2024.

[19] M. Zallio, C. B. Ike, and C. Chiv˘aran, “Designing artificial intelligence: Exploring inclusion, diversity, equity, accessibility, and safety in human-centric emerging technologies,” AI, vol. 6, no. 7, p. 143, 2025.

[20] M. F. Almufareh, S. Kausar, M. Humayun, and S. Tehsin, “A conceptual model for inclusive technology: advancing disability inclusion through artificial intelligence,” Journal of Disability Research, vol. 3, no. 1, p. 20230060, 2024.

[21] A. Sutarman, J. Williams, D. Wilson, and F. B. Ismail, “A model-driven approach to developing scalable educational software for adaptive learning environments,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 9–16, 2024.

[22] A. Tlili, M. Denden, M. Abed, and R. Huang, “Artificial intelligence ethics in services: are we paying attention to that?!” The Service Industries Journal, vol. 44, no. 15-16, pp. 1093–1116, 2024.

[23] E. G. Hern´andez, “Towards an ethical and inclusive implementation of artificial intelligence in organizations: a multidimensional framework,” arXiv preprint arXiv:2405.01697, 2024.

[24] N. Anwar, J. Anderson, T. Williams et al., “Applying data science to analyze and improve student learning outcomes in educational environments,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 72–83, 2024.

[25] E. Fosch-Villaronga and A. Poulsen, “Diversity and inclusion in artificial intelligence,” Law and artificial intelligence: Regulating AI and applying AI in legal practice, pp. 109–134, 2022.

[26] N. Lutfiani, N. P. L. Santoso, R. Ahsanitaqwim, U. Rahardja, and A. R. A. Zahra, “ Ai-based strategies to improve resource efficiency in urban infrastructure,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 121–127, 2024.

[27] G. Cachat-Rosset and A. Klarsfeld, “Diversity, equity, and inclusion in artificial intelligence: an evaluation of guidelines,” Applied Artificial Intelligence, vol. 37, no. 1, p. 2176618, 2023.

[28] N. Balasubramaniam, M. Kauppinen, A. Rannisto, K. Hiekkanen, and S. Kujala, “Transparency and explainability of ai systems: From ethical guidelines to requirements,” Information and Software Technology, vol. 159, p. 107197, 2023.

[29] . Fernanda, M. Huda, and A. R. F. Geovanni, “Application of learning cloud computing technology (cloud computing) to students in higher education,” International Journal of Cyber and IT Service Management, vol. 3, no. 1, pp. 32–39, 2023

[30] R. R. Hoffman, T. Miller, and W. J. Clancey, “Psychology and ai at a crossroads: How might complex systems explain themselves?” The American journal of psychology, vol. 135, no. 4, pp. 365–378, 2022.

[31] . K. Choi, K. B. Rii, and H. W. Park, “K-means and j48 algorithms to categorize student research abstracts,” International Journal of Cyber and IT Service Management, vol. 3, no. 1, pp. 61–64, 2023.[32] K. F. Ystgaard and K. De Moor, “Envisioning the future: A multi-disciplinary approach to human-centered intelligent environments,” Quality and User Experience, vol. 8, no. 1, p. 11, 2023.

[33] J. Friedrich, A. Brückner, J. Mayan, S. Schumann, A. Kirschenbaum, and C. Zinke-Wehlmann, “Human-centered ai development in practice—insights from a multidisciplinary approach,” Zeitschrift F¨ur Arbeitswissenschaft, vol. 78, no. 3, pp. 359–376, 2024.

[34] 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,” International Journal of Human–Computer Interaction, vol. 39, no. 3, pp. 494–518, 2023.

[35] T. Li, M. Vorvoreanu, D. DeBellis, and S. Amershi, “Assessing human-ai interaction early through factorial surveys: a study on the guidelines for human-ai interaction,” ACM Transactions on Computer-Human Interaction, vol. 30, no. 5, pp. 1–45, 2023.

[36] Badan Kebijakan Kesehatan, Kementerian Kesehatan RI, “The use of artificial intelligence in the duties of public–humas pranata,” Online article, Aug. 2024, describes AI adoption in government to improve operational efficiency and public services. [Online]. Available: https://www.badankebijakan.kemkes.go. id/en/penggunaan-artificial-intelligence-dalam-tugas-pranata-humas-bkpk-kemenkes/

[37] K. Adel, A. Elhakeem, and M. Marzouk, “Blockchain and artificial intelligence: scientometric analysis and visualization,” IEEE Access, vol. 11, pp. 137 911–137 928, 2023.

[38] M. Alaeddini, M. Hajizadeh, and P. Reaidy, “A bibliometric analysis of research on the convergence of artificial intelligence and blockchain in smart cities,” Smart Cities, vol. 6, no. 2, pp. 764–795, 2023.

[39] M. S. Al Jasem, T. De Clark, and A. K. Shrestha, “Toward decentralized intelligence: A systematic literature review of blockchain-enabled ai systems,” Information, vol. 16, no. 9, p. 765, 2025.

[40] H. Taherdoost, “Blockchain technology and artificial intelligence together: a critical review on applications,” Applied Sciences, vol. 12, no. 24, p. 12948, 2022.

[41] A. Mazarakis, C. Bernhard-Skala, M. Braun, and I. Peters, “What is critical for human-centered ai at work?–toward an interdisciplinary theory,” Frontiers in artificial intelligence, vol. 6, p. 1257057, 2023.

[42] S. T. H. Mortaji and M. E. Sadeghi, “Assessing the reliability of artificial intelligence systems: Challenges, metrics, and future directions,” International Journal of Innovation in Management, Economics and Social Sciences, vol. 4, no. 2, pp. 1–13, 2024.

[43] Q. Aini, P. Purwanti, R. N. Muti, E. Fletcher et al., “Developing sustainable technology through ethical ai governance models in business environments,” ADI Journal on Recent Innovation, vol. 6, no. 2, pp. 145–156, 2025.

[44] A. Hamza, G. Shi, and B. Hossain, “Migration as an adaptation measure to achieve resilient lifestyle in the face of climate-induced drought: Insight from the thar desert in pakistan,” Water, vol. 16, no. 18, p. 2692, 2024.

[45] M. A. Camilleri, “Artificial intelligence governance: Ethical considerations and implications for social responsibility,” Expert systems, vol. 41, no. 7, p. e13406, 2024.

[46] M. Jeon, “The effects of emotions on trust in human-computer interaction: A survey and prospect,” International Journal of Human–Computer Interaction, vol. 40, no. 22, pp. 6864–6882, 2024.

[47] A. Chinnaraju, “Explainable ai (xai) for trustworthy and transparent decision-making: A theoretical framework for ai interpretability,” World Journal of Advanced Engineering Technology and Sciences, vol. 14, no. 3, pp. 170–207, 2025.

[48] D. Ogbu, “Agentic ai in computer vision domain-recent advances and prospects,” International Journal of Research Publication and Reviews, vol. 4, no. 12, pp. 5102–5120, 2023.

[49] J. Wang, Y. Huo, J. Mahe, Z. Ge, Z. Liu, W. Wang, and L. Zhang, “Developing an ethical regulatory framework for artificial intelligence: integrating systematic review, thematic analysis, and Multidisciplinary Theories,” IEEE Access, 2024.

[50] A. Ellikkal and S. Rajamohan, “Ai-enabled personalized learning: empowering management students for improving engagement and academic performance,” Vilakshan-XIMB Journal of Management, vol. 22, no. 1, pp. 28–44, 2025.

[51] L. Meria, C. S. Bangun, and J. Edwards, “Exploring sustainable strategies for education through the adoption of digital circular economy principles,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 62–71, 2024.