Ethical Implications of AI-Driven IoT Systems: Perspectives From Data Practitioners

Abstract

The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) promises tremendous opportunities across domains through hyperconnected intelligent systems but also introduces opaque complexity, posing new socio-technical risks around ethics and accountability. As innovative applications harnessing IoT data expand, additional empirical insight is needed into the practical challenges, priorities, and governance needs perceived by the developers and managers who implement these AI-driven technologies. This study adopted qualitative methods, conducting semi-structured interviews with 26 professionals involved in AI and IoT projects across large multinationals and startups spanning sectors like autonomous vehicles, smart cities, logistics, and fintech. A detailed thematic analysis elicited key tensions around privacy, algorithmic fairness, transparency, and unintended impacts when operationalizing high-level principled AI guidelines under real-world constraints. Practitioners emphasized current governance gaps in managing risks emerging specifically from AI modeling utilizing continuous IoT data flows and called for tailored oversight processes that can evolve with technical advancement. Findings highlight priorities around novel monitoring procedures, incentives guiding responsible innovation investment, tools demonstrating trustworthiness, and cross-industry infrastructure for investigability and redress across increasingly interconnected AI-IoT ecosystems. Forward-looking policy formulations must synthesize multidisciplinary, evidence-based insights that resonate with on-the-ground decision drivers balanced against long-term considerations.