Attitudes of Social Workers Toward AI-Supported Case Analysis: Opportunities, Risks and Ethical Dimensions

Authors

  • Sora Pazer IU International University of Applied Science, Germany

DOI:

https://doi.org/10.54536/ajsde.v4i2.5989

Keywords:

Artificial Intelligence, Case Analysis, Ethical Concerns, International Cross-Sectional Study, Privacy, Professional Autonomy, Social Work, Technology Acceptance

Abstract

The digital transformation is increasingly reaching social work. In addition to classic digital tools, artificial intelligence (AI) systems are increasingly appearing to support decision-making processes in case analysis. This international cross-sectional study examines the attitudes of social workers towards AI-supported case analysis. Based on a sample of 121 professionals from five countries, attitudes towards opportunities, efficiency potential, ethical concerns, professional autonomy, data protection and willingness to implement were assessed using a standardized questionnaire with six subscales. The results show an ambivalent picture: While technical opportunities are moderately recognized (M = 3.19) and efficiency gains appear visible (M = 3.36), ethical concerns (M = 3.82) and concerns about the restriction of professional autonomy (M = 4.08) predominate. The willingness to implement remained comparatively low (M = 2.83). Group comparisons reveal significant differences in terms of age, professional experience, and field of work. Younger and less experienced professionals showed more openness, while older and very experienced professionals reacted more skeptically. Regression analyses confirm an affinity for technology as a positive predictor, while ethical concerns and fears of autonomy reduce the willingness to implement. Overall, the study makes it clear that social workers neither reject AI across the board nor welcome it unreservedly, but reflect on it in a differentiated and normative way. For implementation strategies, it is necessary to ensure ethical standards, transparency and participatory implementation processes in addition to technical advantages.

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References

Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.

Banks, S. (2012). Ethics and values in social work (4th ed.). Palgrave Macmillan.

Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.

Chowdhury, T. H. (2025). Relevancy of NGOs in social advancement: Experience in Bangladesh. American Journal of Social Development and Entrepreneurship, 4(1), 7–12.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.

Gillingham, P. (2019). Predictive risk modelling to prevent child maltreatment and other adverse outcomes for service users: Inside the “black box” of machine learning. British Journal of Social Work, 49(5), 1344–1360.

Gillingham, P. (2021). Artificial intelligence, algorithms, and social work: Replacing or augmenting professional judgment? Journal of Technology in Human Services, 39(2), 94–112.

Mishna, F., Bogo, M., Root, J., Sawyer, J. L., & Khoury-Kassabri, M. (2012). “It just crept in”: The digital age and implications for social work practice. Clinical Social Work Journal, 40(3), 277–286.

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.

Parrott, L. (2020). Artificial intelligence and social work: The value of critical reflection. Journal of Social Work, 20(6), 756–773.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Sabour, M. (2025). Exploring volunteering in Moroccan rural areas: Challenges and opportunities. American Journal of Social Development and Entrepreneurship, 4(1), 61–67.

Tregeagle, S., & Darcy, M. (2008). Child welfare and information and communication technology: Today’s challenge. British Journal of Social Work, 38(8), 1481–1498.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Wang, J. (2025). Research on the role of digital education in sustainable development. American Journal of Social Development and Entrepreneurship, 4(1), 1–6.

Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019). Transparency in algorithmic and human decision-making: Is there a double standard? Philosophy & Technology, 32(4), 661–683.

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Published

2025-11-20

How to Cite

Pazer, S. (2025). Attitudes of Social Workers Toward AI-Supported Case Analysis: Opportunities, Risks and Ethical Dimensions. American Journal of Social Development and Entrepreneurship, 4(2), 32–38. https://doi.org/10.54536/ajsde.v4i2.5989