Background
Digital technologies and digital transformation are increasingly present in all sectors of the economy (Newman, Mintrom, & O'Neill, 2022). In many cases, digital technologies can be used to create a more "livable and sustainable future" (Hagemann Snabe, 2022).
Although Al is only one of many technologies, it has infiltrated numerous sectors of the economy, such as manufacturing (Lee, J., Davari, Singh, & Pandhare, 2018) or healthcare (Davenport & Kalakota, 2019; Jiang, F. et al., 2017; Yu, Beam, & Kohane, 2018). Today, Al is often just being called a game changer (Castellanos, 2020; Ditsche, Schieler, & Steffan, 2023).
Al is also used in the context of human services. It is used, for example in child welfare (Gillingham, P., 2006; Schwartz, York, Nowakowski-Sims, & Ramos-Hernandez, 2017), youth work (Rice et al., 2018; Ting, Chu, Zeng, Li, D., & Chng, 2018), or mental health services (Gamble, 2020). Furthermore, artificial intelligence can be used in human services on different levels (Goldkind, 2021): micro/individual like virtual and personalized On-Demand-Counselors (Lisetti, Amini, Yasavur, & Rishe, 2013), meso like predictive analytics to improve services and life of client and staff (Goldkind, 2021) or digital-automated decision making (Ranerup & Henriksen, 2022) and on macro level to share information between providers, across sectors and governmental organizations in order to connect data (Susha et al., 2018).
Recent developments and discussions on ChatGPT and initial publications on its impact on human services like social work point out its practical importance (Patton, D. Upton, Landau, A., & Mathiyazhagan, 2023; Singer, Baez, & Rios, 2023). However, research and practice lag behind in and is still in its infancy (Zetino & Mendoza, 2019). For this, it is necessary to consider the respective possibilities, limits, challenges, and (un)foreseen positive and negative impacts of the Al application (Zetino & Mendoza, 2019). On the one hand, Al offers different opportunities like digital-assisted decision support (Baka et al., 2021; Gillingham, P., 2013), information and data exchange (Devlieghere, Bradt, & Roose, 2018; Fink, 2018), proactive and predictive thinking e.g. enabling risk assessment (Gillingham, P., 2016; Grzaldzielewska, 2021), increased quality and impact e.g. of services (Kum, Joy Stewart, Rose, & Duncan, 2015; Pan et al., 2017; Santiago & Smith, 2019), transparency and user orientation (Baka et al., 2021; Cresswell et al., 2020). On the other hand, it is important to consider its limitations, which are just as diverse: Dehumanization (Devlieghere, Gillingham, P., & Roose, 2022; Fink, 2018), data safety and security (Keen et al., 2021; Ranerup & Henriksen, 2022), data and algorithmic Injustice (Eubanks, 2018; Walter et al., 2021; Whelan, 2020) and as result inequalities (Eubanks, 2018) or misperception and bias (Cresswell et al., 2020; James & Whelan, 2021; Landau. A. Y. et al., 2022). These limitations are particular relevant to the human service context as human services professions are based on strong ethical and moral foundations (Rodriguez, DePanfilis, & Lanier, 2019; Schneider & Seelmeyer, 2019) work with often overlooked and marginalized, stigmatized, and discriminated groups and with very sensitive and complex psychological and emotional issues. As a consequence, Al can be a potent support tool but could also exacerbate existing inequalities, injustices, and challenges.
Vision and Aim:
Artificial intelligence could have the potential to radically transform the human service sector, but research and practical application remain in their infancy (Zetino & Mendoza, 2019). The vision of the special issue is to take a closer look at different application areas, levels of artificial intelligence in human service organizations. Our aim is to aggregate knowledge and topics as well as to bring researchers together and develop a research community.
Possible Topics (as an orientation)
The guest editors are pleased to invite submissions on the topic of artificial intelligence from different perspectives. Authors are free to choose the level or aspects on which they examine Al in human services. e.g. organization, client, profession, or cross-cutting. Model or pilot projects can also be presented. Suggested topics (all research Questions in context of human service) could include but are not limited to:
- What effects does artificial intelligence have on existing human service- and counseling models?
- How can revenue models be developed for and with Al?
- How can interdisciplinary and diverse teams develop Al? How can non-discriminatory practices be built into the design and development stages of Al (e.g., through team structures make-up, and processes)
- What are equality, diversity, and inclusion opportunities and challenges of Al integration into organizations and their processes (e.g., for gender (identity), race/ethnicity, disability, social class, religion, immigration status, sexual identity, martial status / civil partnership)?
- How can these challenges be avoided to ensure a non-discriminatory use of Al?
- How can clients be involved in Al processes through co-production, co-creation or other forms of collaboration?
- What are ethical challenges and potential guard rails and recommendations for action for the use of Al?
- What framework conditions (standards, financial support, political support and acceptance) are necessary?
- How can professional and executive staff be prepared for Al? How can(not) Al be integrated in organizational decision-making processes?
- How can the human-machine interaction be organized, for example in digital-automated decision-making?
- How can artificial intelligence be used in human services? What are the possibilities and limitations of using artificial intelligence?
- In which areas/areas of application of human services is Al useful (e.g. child protection, urban/ rural development, healthcare, reducing inequalities, environmental protection)?
- What role does data protection and security play?
- How can the use of artificial intelligence be monitored?
- What social impact (e.g., on quality of life, urban development, human wellbeing, sustainable development goals) can the use of artificial intelligence in human services create? How can this be measured?
- What resources are needed to use artificial intelligence (at all)?
