Spektrum Iran

Abstract Nearly five decades after Hubert Dreyfus underscored the importance of accounting for the social character of intelligence in the development of artificial intelligence, practical implementations have progressed more rapidly than corresponding theoretical inquiry. This remains the case notwithstanding artificial intelligence’s consolidation as an actant within the news media. Because the capacity for meaning-making within a social institution presupposes the socialization of a cognitive system, the socialization of artificial intelligence may be examined along a trajectory comparable to that of human forms of natural intelligence. On this basis, the present article investigates the processes through which AI becomes socialized so as to assume a meaning-making role within a social institution such as the news media, addressing the central question: What constitutes socialized artificial intelligence? To this end, the study integrates the Dual-spacization of Intelligence with representation theory within a socio-organizational framework and adopts a retroductive theoretical approach to address the research question. Within this analysis, social order is understood as a function of AI’s socialization process. The dual-spacization of the world consequently gives rise to a dual-spatial social order. The study’s findings suggest that AI may either be engineered to replicate existing forms of knowledge and entrenched social stereotypes in a manner analogous to human cognition, or be subject to social regulation that fosters an algorithmic rationality oriented toward the common good and toward a sustainable and just social order. Such an order depends on opening representational practices through reflexive engagement with social stereotypes, enabling transformations in representation and supporting increased diversity of identities. The contribution of this article lies in proposing an integrated model for understanding the mechanisms of AI socialization across meaning-producing social institutions. Furthermore, the model offers a comprehensive perspective on the socialization of both natural and artificial cognitive systems within the evolving structures of dual-spatial institutional social orders.

Abstract In the contemporary landscape of crisis management, decision-makers are increasingly overwhelmed by the sheer volume, velocity, and variety of media data generated during emergencies. Traditional manual analytical methods are often insufficient to process this influx effectively, necessitating a paradigm shift toward advanced computational approaches. The primary goal of this study is to bridge the gap between technical data science and practical crisis communication by establishing a clear analytical link between specific machine learning (ML) paradigms and their operational capabilities. This article utilizes a narrative review methodology, underpinned by a theoretical framework grounded in machine learning. The study systematically synthesizes existing literature to categorize and analyze how distinct ML architectures—specifically supervised, unsupervised, and deep learning—are applied within the domain of media data analysis to support decision-making processes during crises. The analysis confirms that artificial intelligence significantly enhances crisis management effectiveness by automating media monitoring and generating actionable real-time insights. The findings delineate specific roles for different algorithms: supervised learning serves as the theoretical foundation for rapid misinformation detection and precise crisis classification. Conversely, unsupervised learning and deep learning are identified as critical tools for detecting data anomalies and recognizing emerging patterns, which are essential for the functionality of proactive early warning systems. While AI offers transformative potential, this study provides a critical reflection on significant implementation challenges. It highlights the “black box” problem—characterized by a lack of algorithmic interpretability—and inherent data biases as major ethical hurdles that can compromise accountability and fairness in crisis response. The present study contributes a structured framework for understanding AI’s role through a theoretical lens. It concludes that future implementation must prioritize explainable AI to balance computational efficiency with ethical responsibility.