Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)

This paper examines the responsible integration of artificial intelligence (AI) in human services organizations (HSOs), proposing a nuanced framework for evaluating AI applications across multiple dimensions of risk. The authors argue that ethical concerns about AI deployment -- including professional judgment displacement, environmental impact, model bias, and data laborer exploitation -- vary significantly based on implementation context and specific use cases. They challenge the binary view of AI adoption, demonstrating how different applications present varying levels of risk that can often be effectively managed through careful implementation strategies. The paper highlights promising solutions, such as local large language models, that can facilitate responsible AI integration while addressing common ethical concerns. The authors propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing. They conclude by outlining a path forward that emphasizes empirical evaluation, starting with lower-risk applications and building evidence-based understanding through careful experimentation. This approach enables organizations to maintain high ethical standards while thoughtfully exploring how AI might enhance their capacity to serve clients and communities effectively.

A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work

Word embeddings represent a transformative technology for analyzing text data in social work research, offering sophisticated tools for understanding case notes, policy documents, research literature, and other text-based materials. This methodological paper introduces word embeddings to social work researchers, explaining how these mathematical representations capture meaning and relationships in text data more effectively than traditional keyword-based approaches. We discuss fundamental concepts, technical foundations, and practical applications, including semantic search, clustering, and retrieval augmented generation. The paper demonstrates how embeddings can enhance research workflows through concrete examples from social work practice, such as analyzing case notes for housing instability patterns and comparing social work licensing examinations across languages. While highlighting the potential of embeddings for advancing social work research, we acknowledge limitations including information loss, training data constraints, and potential biases. We conclude that successfully implementing embedding technologies in social work requires developing domain-specific models, creating accessible tools, and establishing best practices aligned with social work's ethical principles. This integration can enhance our ability to analyze complex patterns in text data while supporting more effective services and interventions.