The Role of Artificial Intelligence (AI) and machine learning in social work practice
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into social work practice is transforming the landscape of service delivery and decision-making. This paper explores how these technologies enhance case management, predictive analytics, and resource allocation in critical areas such as child welfare, mental health, and substance abuse treatment. Key trends highlighted include the use of AI-based predictive analytics to identify at-risk populations and facilitate early interventions, as well as the deployment of chatbots and virtual assistants for providing accessible mental health counselling and social support. Furthermore, the paper addresses ethical considerations and challenges associated with AI implementation, particularly the potential biases in algorithms that may affect the assessment of social needs. Additionally, the integration of AI tools into social work education and training is examined to prepare future professionals for a technology-driven environment. By analysing the current applications of Natural Language Processing (NLP) for client data analysis, AI-powered software for predictive risk assessments, and automated case management systems, this paper advocates for a balanced approach to AI adoption, ensuring that the core values of social work, such as equity and social justice, remain central to practice. Ultimately, this exploration underscores the potential of AI and ML to enhance social work outcomes while also emphasizing the necessity of ethical frameworks and ongoing training for practitioners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".