Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Rapid adoption of innovative technologies confront IT-Service-Management (ITSM) to incoming support requests of increasing complexity. As a consequence, job demands and turnover rates of ITSM support agents increase. Recent technological advances have introduced assistance systems that rely on hybrid intelligence to provide support agents with contextually suitable historical solutions to help them solve customer requests. Hybrid intelligence systems rely on human input to provide high-quality data to train their underlying AI models. Yet, most agents have little incentives to label their data, lowering data quality and leading to diminishing returns of AI systems due to concept drifts. Following a design science research approach, we provide a novel Human-in-the-Loop design and hybrid intelligence system for ITSM support ticket recommendations, which incentivize agents to provide high-quality labels. Specifically, we leverage agent’s need for instant gratification by simultaneously providing better results if they improve labeling automatically labeled support tickets.
Recommended Citation
Li, Mahei; Löfflad, Denise; Reh, Cornelius; and Oeste-Reiß, Sarah, "Towards the Design of Hybrid Intelligence Frontline Service Technologies – A Novel Human-in-the-Loop Configuration for Human-Machine Interactions" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 4.
https://aisel.aisnet.org/hicss-56/cl/machines_as_teammates/4
Towards the Design of Hybrid Intelligence Frontline Service Technologies – A Novel Human-in-the-Loop Configuration for Human-Machine Interactions
Online
Rapid adoption of innovative technologies confront IT-Service-Management (ITSM) to incoming support requests of increasing complexity. As a consequence, job demands and turnover rates of ITSM support agents increase. Recent technological advances have introduced assistance systems that rely on hybrid intelligence to provide support agents with contextually suitable historical solutions to help them solve customer requests. Hybrid intelligence systems rely on human input to provide high-quality data to train their underlying AI models. Yet, most agents have little incentives to label their data, lowering data quality and leading to diminishing returns of AI systems due to concept drifts. Following a design science research approach, we provide a novel Human-in-the-Loop design and hybrid intelligence system for ITSM support ticket recommendations, which incentivize agents to provide high-quality labels. Specifically, we leverage agent’s need for instant gratification by simultaneously providing better results if they improve labeling automatically labeled support tickets.
https://aisel.aisnet.org/hicss-56/cl/machines_as_teammates/4