AI-Driven Feedback Systems, Digital Labour, and Silent Quitting: Transforming African Workplaces
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arXiv:2609.00808v1 Announce Type: cross Abstract: As far-right actors increasingly exploit online platforms to disseminate ideology and mobilize supporters, civil society organizations (CSOs) play a...
arXiv:2606. 30656v1 Announce Type: cross Abstract: Artificial Intelligence (AI) has the potential to be transformative for development, but Africa is currently facing a fragmented and challenging "AI divide".
The paper reviews secondary studies and research agendas on generative AI (GenAI) in information systems, synthesizing evidence from 28 selected papers. It identifies GenAI’s transformative benefits—productivity, innovation, personalization, and democratized expertise—while highlighting challenges such as technical unreliability, ethical risks, and governance gaps. The authors propose a research agenda that shifts IS scholarship toward shaping the co‑evolution of AI capabilities with organizational routines, societal values, and regulatory institutions, emphasizing hybrid human‑AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance.
arXiv:2602. 00056v4 Announce Type: replace-cross Abstract: Large-scale data has fuelled the success of frontier artificial intelligence (AI) models over the past decade.
The paper presents an AI-based methodology that uses embedding-based similarity search and large language model classification to map Dutch job profiles to harmonised ESCO occupations. It introduces a Digital Semantic Score, which quantifies the association of job titles and skills with digital concepts relative to a non-digital reference, moving beyond keyword-based approaches. The study finds that digitalisation is unevenly distributed across occupations, with managerial, professional, and ICT roles showing the strongest digital language, while career-transition and skill analyses reveal pathway-dependent movement toward digital work and a multidimensional nature of digital capability.
The paper proposes a new interdisciplinary field called Cognitive Infrastructure Studies (CIS) to examine how AI systems act as invisible, foundational cognitive infrastructures that shape what people can know and do in digital societies. It argues that these infrastructures, through anticipatory personalization and adaptive invisibility, automate relevance judgments and shift epistemic agency to non‑human systems. CIS offers methodological tools, such as infrastructure breakdown experiments, to uncover the hidden cognitive dependencies created by AI preprocessing across individual, collective, and societal levels.