Empowering Social Service with AI: Insights from a Participatory Design Study with Practitioners

2025-02-27

AI Summary

In social service practice, administrative burden and decision-making complexity impede casework efficacy, while generative AI (GenAI) introduces both efficiency gains and ethical risks—including algorithmic bias, skill atrophy, and professional identity erosion. This study employs a two-phase participatory design—comprising a co-design workshop with 27 practitioners and real-world implementation testing with 24 frontline workers—to systematically examine GenAI applications in documentation, needs assessment, and supervision support. It identifies, for the first time, three core tensions in GenAI adoption within social work: efficiency gains versus professional capability degradation; algorithmic assistance versus client safety; and tool-enabled empowerment versus occupational identity threat. The research delivers a deployable prototype, pinpoints high-value use cases (e.g., document automation, preliminary risk screening, and supervisory feedback generation), and proposes 12 evidence-informed design principles—including algorithmic transparency, human override rights, and case de-identification—to guide ethically grounded, practice-aligned GenAI integration.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In social service, administrative burdens and decision-making challenges often hinder practitioners from performing effective casework. Generative AI (GenAI) offers significant potential to streamline these tasks, yet exacerbates concerns about overreliance, algorithmic bias, and loss of identity within the profession. We explore these issues through a two-stage participatory design study. We conducted formative co-design workshops ( extit{n=27}) to create a prototype GenAI tool, followed by contextual inquiry sessions with practitioners ( extit{n=24}) using the tool with real case data. We reveal opportunities for AI integration in documentation, assessment, and worker supervision, while highlighting risks related to GenAI limitations, skill retention, and client safety. Drawing comparisons with GenAI tools in other fields, we discuss design and usage guidelines for such tools in social service practice.

Problem

• Address administrative burdens in social service
• Mitigate risks of overreliance on generative AI
• Enhance decision-making with AI integration

Innovation

• Generative AI tool creation
• Participatory design study
• Real case data integration