Artificial Intelligence in Social Work Practice Embedded Development, Ethical Risks and the Path Forward

Li Wang*
School of Humanities and Social Sciences, Anhui Agricultural University, Hefei 230036, China
*Corresponding email: 820513477@qq.com
https://doi.org/10.71052/jsdh/YKAI9279

The deep embedding of artificial intelligence into social work has transitioned from theoretical discourse to practical reality, presenting a complex landscape of opportunities and risks. This paper unpacks the multi-layered embedding of artificial intelligence (AI) from four analytical dimensions: technological, epistemic, decisional, and relational. It identifies three interrelated risks: the erosion of professional autonomy accompanied by an accountability vacuum, the systematic entrenchment of algorithmic bias and social exclusion, and relational alienation leading to the dissolution of ethical safeguards. In response, the article proposes a framework emphasizing the redefinition of professional boundaries in human-machine collaboration, the embedding of ethical oversight and democratic governance mechanisms, and the promotion of interdisciplinary collaboration and participatory design. The article concludes that the future of social work in the digital age depends not on the boundaries of AI’s capabilities, but on the consciousness, courage, and judgment of the social work profession itself. This ensures that the core values of human dignity, self-determination, and social justice remain at the forefront of practice.

References
[1] Li, L., Wang, M., Jian, M. (2026) Artificial intelligence-assisted case management in social work services: a systematic review. Research on Social Work Practice, 36(3), 268-278.
[2] Pandya, S. P. (2026) Social work practice in the era of artificial intelligence: social workers’ voices from South Asia. Social Work, 71(1), 69-80.
[3] Yu, M. H., Rose, R. A. (2026) Algorithmic-assisted decision-making tools in child welfare practice: a systematic review. Research on Social Work Practice, 36(4), 382-397.
[4] Wong, J. M. S. (2026) Artificial intelligence- supported group facilitation: Emerging potentials, cautious approaches, and ethical considerations in social work practice. Social Work with Groups, 49(1), 77-90.
[5] Garkisch, M., Goldkind, L. (2025) Considering a unified model of artificial intelligence enhanced social work: a systematic review. Journal of Human Rights and Social Work, 10(1), 23-42.
[6] López Peláez, A., Marcuello-Servós, C., Kalenda Vávrová, S., Castillo de Mesa, J. (2026) Social work practice and AI at the digital frontier: Promoting well-being among vulnerable groups. Journal of Social Work Practice, 40(2), 175.
[7] Boetto, H. (2025) Artificial intelligence in social work: an EPIC model for practice. Australian Social Work, 78(3), 292-305.
[8] Lee, J. Y., Pace, G. T., Cha, H., Rao, S., Hahm, H. C., An, R., Denby-Brinson, R. (2025) Commentary: ETHICS – an ethical framework for artificial intelligence (AI) use in social work research. Journal of the Society for Social Work and Research, 16(4), 699-711.
[9] Eiermann, M., Fitzpatrick, M., Sadowski, K., Wildeman, C. (2026) How do (human) child welfare workers respond to machine-generated risk scores? Sociological Science, 13, 1-21.
[10] Wilkins, D., Benett, V. (2026) Making accurate judgements in child welfare: Comparing chatgpt with qualified social workers. Child & Family Social Work, 31(2), 703-712.
[11] Lehtiniemi, T. (2024) Contextual social valences for artificial intelligence: anticipation that matters in social work. Information, Communication & Society, 27(6), 1110-1125.
[12] Garrett, P. M. (2026) “Magic moments”: AI and the “disappearance” of social work ethics? The British Journal of Social Work, 56(1), 377-395.
[13] Perron, B. E., Goldkind, L., Qi, Z., Victor, B. G. (2025) Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s). Human Service Organizations: Management, Leadership & Governance, 49(1), 20-33.
[14] Segal, M. (2026) Social workers’ evaluation of ChatGPT for solving ethical dilemmas within the limits of confidentiality. Journal of Social Work Practice, 40(1), 5-18.
[15] Ahn, E., Tejeda, Y., Yang, Y. (2025) Examining fairness in machine learning applied to support families: a case study of preventive services. Family Relations, 74(3), 1285-1298.
[16] Báez, J. C., Bjugstad, A., Park, T. K., Jones, J. L., Bidwell, L. N., Sage, M., Hitchcock, L. I. (2025) Social work educators innovating with generative AI: an exploratory study. Journal of Social Work Education, 61(1), 14-29.
[17] Li, Y., Cheng, H., Qin, Q. (2025) Evaluations and Improvement Methods of Deep Learning Ability in Blended Learning. International Journal of e-Collaboration (IJeC), 21(1), 1-17.

Share and Cite
Wang, L. (2026) Artificial Intelligence in Social Work Practice Embedded Development, Ethical Risks and the Path Forward. Journal of Social Development and History, 2(3), 1-9. https://doi.org/10.71052/jsdh/YKAI9279

Published

14/07/2026