Technology in SW
** Page in building **
Move thoughtfully and fix things
Technology can play a pivotal role in social work not just as productivity hacks, but also in social work interventions. However, it is crucial to tread carefully, as there are potential dangers and risks. We do not want to “move fast and break things” (Taplin 2017). We should not think AI can solve all the problems [URL]. We want to move thoughtfully and fix things. Through thoughtful research and cautious implementation, we can harness the power of technology to enhance social work practice while staying true to our social work profession core values in Singapore.
Key Areas of Inquiry
What are the key areas I am interested to explore in my research on techSW:
Identify key competencies
1) Identify key competencies to be considered for social work education and continuing education regarding technology in social work practice
Social work competencies include “core knowledge, values, and skills in working .. in an area of particular practice … [as well as] competence from one situation to another irrespective of case, need, problem, or context” (as cited by McInroy 2021, 546)
McInroy (2021) identified five competencies of using ICT which can still be relevant to current tech:
- Continuing engagement with technologies
- Online professionalism
- Assessing risks and opportunities
- Applying professional ethics
- Thoughtful integration of technology into practice contexts
Other competencies need to be considered in the age of gen AI:
Data literacy and Computational reasoning. If we encourage workers to apply computational tools to solve complex problems or improve social work processes, we also need to help them develop the “ability to critically self-evaluate the way they apply these tools, and thus be able to reason effectively in a variety of contexts”.
Interdisciplinary tech collaborations (Storer et al. 2023)
In Long and Magerko (2020), which of these 16 competencies of AI are relevant to social work? . I think these competencies can be prioritized.
Social work has an obligation to enter into the discourse of AI-enhanced-everything to insert our ethical perspective into the development of algorithmic tools and products. To effectively enter the conversation, social work students need realtime examples, experience, and practice to acquire the expertise required to engage with computer engineers, data scientists, and other disciplines wrestling with the opportunities and challenges of AI. Social work must reckon with a technologically evolved human ecosystem, confronting the implications of interaction with embodied algorithms, cultivating the ability to evaluate data sources, and expanding the notion of collaboration to include the inevitability of working alongside algorithmic actors including chatbots and other digitally enhanced tools. We may need to expand our primary notions of both “person” and “environment,” working toward authentic and informed engagement with augmented and virtual realities and entering into dialogue with computer agents, including algorithms and robots. (Goldkind 2021)
Develop the science
3) Develop good science to drive the design and development of technology in social work
Applications build on LLMs are exciting and many of these could be potentially useful for social work practice. However, some apps are not built with strong theoretical conceptualization and evidence from existing social science research.
Developing apps for use in social work require skills from computing, AI, or design-thinking. But they also need social science. In social science, we emphasize measurements, causal thinking, rigour models, and theories.
If we want to build an AI tool to support social workers’ engagement skills, we need to tap on what we know about social work engagement. Building the science is key to developing effective apps.
Develop the tools
4) Design and develop the interventions that can harness the power of technology
One crucial question is how do we evaluate AI interventions? Work in this area, mostly with healthcare interventions, have found inadequate information reported by trials and missing critical information (e.g., what version of the algorithm was used? how were the training/test data selected? what was the interactions between AI and human?). See Ibrahim et al. (2021).
Process and outcome evaluation are crucial in understanding the effects of using these interventions. Bibbs et al. (2023) highlighted the need for social work users of technology to “engage in continuous and rapid ethical monitoring” as well as “..duty bound to proactively consider unintended consequences”(p. 141).
Interested to work together?
I am still building this page.
Projects I am working on this year:
- Survey of Tech/AI in social service sector
- Development of AI in text-base counseling/engagement
- COP for tech use in social service sector?
But I am just one person. Help, ideas, collaborations are always welcome. I welcome students who are interested to do research in these areas for their dissertations/ISMs/summer jobs. Practitioners in social service agencies and you want to explore more? We can talk over a coffee!
Social Work and Tech Research Group, SWAT, set up