What it is
Intake is where many people first meet a service, and a long queue treats every request the same. Triage assistance lets the assistant read each request, estimate what it needs, and route it. In the participatory design study cited below, practitioners valued AI most for relief from documentation, help thinking through assessments, guidance for junior workers, and support for supervision. The same practitioners raised concerns about deskilling and privacy.
What it pushes on in the Lab
In the Lab, this lever strengthens the pathway by which people receive the automated system's output, so more of the assistant's work goes to the people doing the job. It also raises operator deference drift, because a sort that arrives already made is rarely reopened. The same pathway carries the assistant's errors too, so when you pull this lever, a wrong sort reaches people as readily as a right one.
You can also pull this lever at a strong tier, which costs more. At the strong tier, each effect below the Lab's strongest setting pushes harder.
In the modes that offer aiming, you can aim this lever at particular parts and pathways of a network. Otherwise it applies to the whole network.
The pressures it answers
No pressure page lists this lever among the levers that answer it.
Where you can pull it
Networks in the Lab that offer this lever:4
The evidence behind its effects
The Lab cites these claims from the evidence registry for this lever's effects.
In a participatory-design study (CHI Late-Breaking Work) with 51 social-service practitioners across two stages (27 in co-design workshops, 24 in contextual inquiry), AI value concentrated in documentation relief, assessment brainstorming, guidance for junior workers, and supervision support — with deskilling and privacy concerns voiced inside the same sessions.[†]
Tan, Y., Soh, K. X., Zhang, R., Lee, J., Meng, H., Sen, B., & Lee, Y.-C. (2025). Empowering Social Service with AI: Insights from a Participatory Design Study with Practitioners. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '25). ACM. https://doi.org/10.1145/3706599.3719736
doi.org/10.1145/3706599.3719736
Appears in: PAN framework development
Topics: co-design, human-ai-interaction, social-work