  Computer vision (CV) techniques try to mimic human capabilities of visual
perception to support labor-intensive and time-consuming tasks like the
recognition and localization of critical objects. Nowadays, CV increasingly
relies on artificial intelligence (AI) to automatically extract useful
information from images that can be utilized for decision support and business
process automation. However, the focus of extant research is often exclusively
on technical aspects when designing AI-based CV systems while neglecting
socio-technical facets, such as trust, control, and autonomy. For this purpose,
we consider the design of such systems from a hybrid intelligence (HI)
perspective and aim to derive prescriptive design knowledge for CV-based HI
systems. We apply a reflective, practice-inspired design science approach and
accumulate design knowledge from six comprehensive CV projects. As a result, we
identify four design-related mechanisms (i.e., automation, signaling,
modification, and collaboration) that inform our derived meta-requirements and
design principles. This can serve as a basis for further socio-technical
research on CV-based HI systems.
