Introduction
In recent years, ethical dilemmas in social work are best highlighted by the integration of artificial intelligence in social work practice. This has necessitated a comprehensive examination of both methodological rigor and ethical accountability. Drawing on qualitative research methodology and data analysis, studies such as those by Segal (2025) have adopted phenomenological approaches to capture the nuanced experiences of social work students when comparing their ethically grounded decision-making processes to those generated by AI tools like ChatGPT.
In these studies, open and axial coding techniques were employed to extract ‘units of meaning’ from participants’ assignments, illustrating the depth and complexity inherent in ethical dilemmas surrounding confidentiality. For example, a case study involving a dilemma where a social worker must decide whether to breach confidentiality to prevent harm demonstrated how nuanced analysis—when performed by experienced professionals—uncovered layers of ethical considerations that AI, in its current state, fails to replicate.
Integral to this research is the emphasis on research ethics and participant confidentiality;
- Strict protocols were followed to secure informed consent and ensure anonymity, thus protecting sensitive data while allowing researchers to analyze authentic responses.
- In one instance, master’s students provided candid reflections on their internal conflict between legal mandates and empathetic care, highlighting the indispensable role of human judgment in ethically ambiguous scenarios.
- Equally important is the rigorous evaluation of reliability and validity in qualitative research; prolonged engagement with the subject matter, reflexivity, and the use of thick description have all been employed to validate the findings, ensuring that the derived insights accurately reflect the real-world complexities of social work practice.
This methodological commitment is exemplified by case studies that not only detail the systematic coding of student responses but also compare these findings with established legal frameworks, such as those outlined in the Israeli Social Work Act. The legal foundations governing confidentiality are scrutinized through real-life examples where social workers, bound by strict statutory requirements, must navigate the tension between a client’s right to privacy and the societal imperative to prevent harm.
One illustrative case involved a client whose disclosed personal struggles highlighted a conflict between maintaining trust and fulfilling legal obligations—a scenario in which the human capacity to interpret and balance these competing demands far surpassed the superficial, rule-based responses offered by AI.
Furthermore, concerns over algorithmic bias and societal implications add another critical dimension to the debate. Research reveals that while AI can provide generalized responses based on statistical patterns, it risks perpetuating biases that may arise from its training data, potentially exacerbating existing social inequalities. For instance, when applied to sensitive cases involving vulnerable populations, algorithmic decision-making may inadvertently favor certain cultural or socioeconomic groups, thereby undermining the equitable distribution of care. Such instances underscore the importance of embedding robust ethical frameworks and continuous oversight into the deployment of AI systems within social work.
Collectively, these insights suggest that while AI has the potential to serve as an adjunctive tool in social work education and practice, its limitations in handling the complexities of ethical dilemmas, ensuring confidentiality, and avoiding inherent biases remain significant challenges. Consequently, a balanced approach that integrates rigorous qualitative methodologies, strict ethical safeguards, sound legal principles, and an acute awareness of the pitfalls of algorithmic bias is essential for advancing social work practice in an increasingly digital world. Social workers evaluation of ChatGPT for solving ethical dilemmas within the limits of confidentiality.
(Source) Michal Segal (20 Mar 2025): Social workers’ evaluation of ChatGPT for solving ethical dilemmas within the limits of confidentiality, Journal of Social Work Practice, DOI: 10.1080/02650533.2025.2480092.
