Spektrum Iran

Abstract Abstract
The emergence of artificial intelligence as a semi-autonomous agent has challenged the classical paradigms of knowledge production in the humanities. Employing a comparative-analytical approach and qualitative methodology, this study addresses the central question: How are the legal and ethical components of responsibility for works produced with AI participation redefined within the frameworks of Imami jurisprudence and the German legal system? Data were collected through the study of authoritative jurisprudential sources, German codified laws, and scholarly articles, and analyzed using inductive reasoning. The findings reveal that the shift from a purely instrumental paradigm to one recognizing AI's "relative agency" has intensified three structural crises: the crisis of attribution (ambiguity regarding authorship and ownership); the crisis of originality (erosion of human creativity and scientific credibility); and the crisis of accountability (conflict in determining final responsibility). In response, this research proposes the innovative "Proportional Responsibility" model as an interdisciplinary solution. This model distributes ethical and legal responsibility equitably among three key actors: the researcher (active supervision and final verification of content); the developer (transparency regarding system limitations and potential errors); and academic institutions (formulating clear standards and implementing oversight mechanisms). By integrating foundational jurisprudential principles from Imami jurisprudence—such as the rules of lā ḍarar, ḍamān, and tasbīb—with the advanced doctrines of German civil liability, particularly § 280 BGB and producer liability principles, the model establishes a coherent framework designed to preserve scientific integrity in humanities research in the age of artificial intelligence.
 Keywords: Artificial Intelligence; Imami Jurisprudence; German Law; Algorithm and Text; Proportional Responsibility

Abstract This study investigates how Persian-speaking users on the social media platform X engage with generative artificial intelligence as a sociotechnical and discursive phenomenon. Drawing on a dataset of 24,215 Persian-language posts, we employ a multi-label topic modeling framework and affective profiling to analyze public discourse surrounding AI tools, their perceived implications, and normative judgments about their use. Rather than treating sentiment as a static indicator of opinion, we interpret affective expression as a communicative act shaped by platform incentives and cultural context. Our findings show that AI is positioned not only as a technical artifact but as a boundary object entangled with debates over expertise, ethics, and institutional legitimacy. The discourse is anchored in practical concerns—especially labor, education, and tool comparisons—but frequently extends into culturally specific narratives about risk, fairness, and epistemic authority. Emotionally, the conversation is marked by pragmatic positivity, critical intensity, and a sizable neutral band reflecting orientation rather than evaluation. This study contributes to ongoing debates in communication, AI ethics, and platform studies by offering a non-Anglophone, culturally grounded analysis of how publics perform vernacular governance over emerging technologies. Emotionally, the conversation is marked by pragmatic positivity, critical intensity, and a sizable neutral band reflecting orientation rather than evaluation. This study contributes to ongoing debates in communication, AI ethics, and platform studies by offering a non-Anglophone, culturally grounded analysis of how publics perform vernacular governance over emerging technologies. Drawing on a dataset of 24,215 Persian-language posts, we employ a multi-label topic modeling framework and affective profiling to analyze public discourse surrounding AI tools, their perceived implications, and normative judgments about their use.

Abstract This study examines the multifaceted impact of artificial intelligence (AI) on interpersonal relationships within Iranian society, highlighting the cultural, social, and psychological challenges emerging from the rapid adoption of AI technologies. As tools such as virtual assistants, social media algorithms, and AI-driven communication platforms become embedded in daily life, they are reshaping patterns of interaction, emotional engagement, and cultural norms. Drawing on twelve semi-structured interviews analyzed through a qualitative-dominant mixed-methods approach, including thematic analysis, intercoder reliability checks, and cross-case comparison, the research identifies a dual narrative: AI enhances communication, productivity, and daily convenience, yet simultaneously undermines face-to-face engagement, emotional bonds, and traditional social practices central to Iranian culture. Findings reveal growing concerns about weakened family and community ties, reduced social skills, dependency on intelligent systems, and generational gaps in digital adaptation. Participants also noted broader cultural shifts, including the rise of virtual lifestyles, threats to cultural identity, and increased social inequality driven by uneven access to AI tools. The study further identifies psychological risks such as loneliness, superficial online connections, diminished empathy, and the perceived decline of emotional intelligence as individuals increasingly interact with algorithmic systems. At the societal level, privacy, data governance, and ethical challenges create additional pressures that shape public trust and relational dynamics. The study contributes to national and international debates on human–AI interaction by demonstrating how global technologies interact with local cultural contexts. It argues that balancing technological innovation with the preservation of Iranian social values is essential to ensuring that AI strengthens rather than erodes the foundations of meaningful human relationships.

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.