With the widespread availability and pervasiveness of artificial intelligence (AI) in many application areas across the globe, the role of crowdsourcing has seen an upsurge in terms of importance for scaling up data-driven algorithms in rapid cycles through a relatively low-cost distributed workforce or even on a volunteer basis. However, there is a lack of systematic and empirical examination of the interplay among the processes and activities combining crowd-machine hybrid interaction. To uncover the enduring aspects characterizing the human-centered AI design space when involving ensembles of crowds and algorithms and their symbiotic relations and requirements, a Computer-Supported Cooperative Work (CSCW) lens strongly rooted in the taxonomic tradition of conceptual scheme development is taken with the aim of aggregating and characterizing some of the main component entities in the burgeoning domain of hybrid crowd-AI centered systems. The goal of this article is thus to propose a theoretically grounded and empirically validated analytical framework for the study of crowd-machine interaction and its environment. Based on a scoping review and several cross-sectional analyses of research studies comprising hybrid forms of human interaction with AI systems and applications at a crowd scale, the available literature was distilled and incorporated into a unifying framework comprised of taxonomic units distributed across integration dimensions that range from the original time and space axes in which every collaborative activity take place to the main attributes that constitute a hybrid intelligence architecture. The upshot is that when turning to the challenges that are inherent in tasks requiring massive participation, novel properties can be obtained for a set of potential scenarios that go beyond the single experience of a human interacting with the technology to comprise a vast set of massive machine-crowd interactions.

Recently, GovTech has gained prominence as a novel approach to fostering government-driven innovation by leveraging technology startups, academia, and innovators to develop digital public services. Although it promises more agile, user-centric, and cost-effective solutions, limited research explores how academic-based startups navigate public procurement models. This paper addresses that gap by presenting three cases of an academic-based Brazilian startup engaging in the public procurement process introduced by the Legal Framework for Startups (Marco Legal das Startups). The company proposed solutions adapted from its principal product for three public agencies: Tribunal de Contas da União (TCU), Correios, and Copasa, focusing on remote oversight of construction projects and building maintenance management. The lessons learned underscore the importance of predictable project scope and costs, emphasizing that unclear requirements can undermine competitiveness. Moreover, strong teams and reputable track records significantly enhance success prospects while balancing technological maturity with innovation, which remains critical for meeting specific public sector needs. Additionally, positioning a product at the technological frontier only proves beneficial when addressing actual government challenges. Effective communication, including well-crafted video pitches and presentations, further ensures evaluators grasp the solution’s value. These insights aim to guide both aspiring and established academic entrepreneurs in bridging their research-based innovations with GovTech opportunities, ultimately strengthening collaboration between the public sector and academia.