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     Artificial intelligence (AI) is fundamentally reshaping the landscape of social sciences by providing novel tools and methodologies for data analysis, predictive modeling, and understanding complex social phenomena (Sharma, 2025)(Dritsas & Trigka, 2025)(Hoca & Nuredin, 2024). This integration marks a significant transformation, moving beyond traditional interpretive depth and qualitative nuance to engage with large-scale, complex datasets efficiently (Sharma, 2025). The application of AI in social sciences encompasses various sub-fields including machine learning (ML), natural language processing (NLP), network science, and explainable AI (XAI) (Dritsas & Trigka, 2025). This interdisciplinary approach allows for the study of emerging fields that link technology and humankind, fostering change across sociology, psychology, economics, political science, anthropology, geography, history, education, communication studies, cultural studies, finance, marketing, human resource management, supply chain management, and information technology (Farooq et al., 2023)(Tr, 2022).

    One of the primary benefits of AI in social science research is its ability to handle and analyze vast amounts of data, often referred to as big data (Ranjan & Foropon, 2021)(Hoca & Nuredin, 2024)(Yavuz et al., 2022)(Bircan & Salah, 2022). Traditional methods can struggle with the scale and complexity of contemporary social data, but AI-driven tools, such as predictive algorithms and ML systems, introduce new paradigms for analysis (Hoca & Nuredin, 2024). Machine learning, for instance, provides a powerful toolbox for extracting information and knowledge from diverse datasets, from text and images to characterizing population heterogeneity and improving causal inference (Molina & Garip, 2019). It amplifies researcher coding, summarizes complex data, relaxes statistical assumptions, and targets researcher attention to further social science inquiries (Lundberg et al., 2022). For example, AI has been used to monitor social media data during crises like COVID-19 to understand public knowledge and behavior through machine learning and natural language processing (Flores & Young, 2022). This ability to process non-reactive data, like social media content, allows for insights into social dynamics without direct intervention (Xu et al., 2024).

    [Natural Language](@brm/pedia/kw:271464) Processing (NLP) stands out as a particularly impactful AI technique for social scientists (Dritsas & Trigka, 2025)(Girju, 2023). NLP techniques have been increasingly applied in various sectors, including the social sciences, since the advent of models like Word2Vec (Li, 2020). It enables researchers to analyze textual data, which is a rich source of information about human communication and societal trends (Card, 2020). Applications include generating metadata to enhance thematic transparency in interview collections, facilitating large-scale research on archival data, and accelerating "text-as-data" research in computational social science (Gárdos et al., 2023)(Card, 2020). Generative AI, including large language models (LLMs) such as ChatGPT, has lowered barriers to computational social sciences by automating code generation, annotation, and debugging, thereby enhancing researcher productivity (Rigin et al., 2025)(Bail & Bail, 2024)(Cappelli et al., 2024)(Zhang, 2023). Such tools can also improve survey research, online experiments, and automated content analyses (Bail & Bail, 2024). Recent research indicates that existing LLMs can closely align with human expert assessments in specialized social science surveys (Cappelli et al., 2024).

    The interdisciplinary application of AI extends to various fields within social sciences. In political science, AI enhances the analysis of complex political phenomena and voter behavior (Dritsas & Trigka, 2025). For instance, sentiment analysis, a component of NLP, can be used in backchannel systems to gauge audience morale and understanding during presentations, allowing for real-time adjustments (Wyeld et al., 2021). In economics, AI supports predictive modeling and economic forecasting (Dritsas & Trigka, 2025). In psychology, AI can model cognitive processes and analyze behavioral patterns (Dritsas & Trigka, 2025). Social work benefits from AI and ML through enhanced case management, predictive analytics for identifying at-risk populations, and optimized resource allocation in areas like child welfare and mental health (Nuwasiima et al., 2024). Computer vision, another significant AI technique, has advanced beyond foundational data capture to interpret and analyze digital images in social applications, influencing research in psychology, sociology, and economics, particularly in studying human behavior (He & Jatel, 2025). For example, the study of rodent social behavior has shifted from direct human observation to computational methods integrating AI and ML, providing multifaceted insights into behavior (Chindemi et al., 2025).

    Despite the transformative potential, the integration of AI into social science research introduces critical ethical dilemmas and challenges (Runcan et al., 2025)(Hoca & Nuredin, 2024)(Wen, 2025). Key ethical concerns include governance, bias, accountability, transparency, privacy, and fairness (Runcan et al., 2025)(Ivanov, 2025)(Oguru, 2025)(Angga, 2025). Algorithmic bias, for instance, has emerged as a significant challenge, necessitating increased engagement from psychological and social science research to understand and mitigate its antecedents and consequences (Ukanwa, 2024)(Angga, 2025). The reliance of AI technologies on vast datasets can risk infringing on privacy and human rights (Ortega-Bolaños et al., 2024)(Oguru, 2025). Furthermore, the complexity of AI systems often leads to a lack of transparency and explainability, which can erode public trust and compromise fairness in algorithmic decision-making (Ivanov, 2025)(Angga, 2025). Therefore, researchers emphasize the responsible use of AI, outlining principles such as transparency, traceability, explainability, fairness, privacy and data protection, human oversight, quality control, and accountability (Ivanov, 2025).

    The ethical development of AI systems is crucial and can be conceptualized through frameworks that integrate virtue ethics, deontological ethics, and the ethics of applied AI (Ortega-Bolaños et al., 2024).


*Source: (Ortega-Bolaños et al., 2024)*

    This figure illustrates the "Ethical Development of AI Systems" as a structure supported by three pillars: Virtue Ethics (focusing on basic AI virtues and active responsibility), Deontological Ethics (centering on AI ethical principles), and Ethics of Applied AI (involving technical and non-technical tools and operationalization) (Ortega-Bolaños et al., 2024). This framework underscores the need for a holistic approach to address ethical concerns, ensuring that AI systems are developed and deployed ethically (Ortega-Bolaños et al., 2024). The rapid pace of AI development also raises questions about the "implicit intelligence" of AI models and their cognitive dissonance, demanding critical examination of accountability and validity in AI-driven research (Rigin et al., 2025). The debate between quantitative and qualitative methods for evaluating AI-driven research is also pertinent (Rigin et al., 2025).

    Future directions in AI-enhanced social sciences involve building socially intelligent AI agents (Social-AI) that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (Mathur et al., 2024). This multidisciplinary goal spans natural language processing, machine learning, robotics, human-machine interaction, computer vision, and speech (Mathur et al., 2024). The continuous advancements in AI, including neural networks and deep learning, represent a paradigm shift in information processing, machine learning, and AI itself, with profound implications for computational social science (Górriz et al., 2020)(Fitz & Romero, 2021). The increasing human-like capabilities of AI, particularly with the emergence of LLMs, are prompting a rethinking of artificial general intelligence possibilities and fostering new studies at the intersection of AI and social science (Xu et al., 2024). This intersection can be broadly categorized into "AI for social science" and "social science of AI" (Xu et al., 2024).


*Source: (Xu et al., 2024)*

    This mind-map illustrates the intersection of AI and social science, branching into "AI for social science" (hypothesis generation and verification through experiments, surveys, and nonreactive research) and "Social science of AI" (exploring the psychology, sociology, economics, politics, and linguistics of AI), alongside public tools and resources (Xu et al., 2024).

    The ongoing transformation necessitates that social scientists critically evaluate the role of AI, acting as both gatekeepers to ensure ethical use and revolutionaries embracing its transformative potential (Runcan et al., 2025). This includes developing new teaching methodologies for social research using generative AI tools like ChatGPT-4, which can address challenges in empirical datasets, data analysis techniques, and scenario-based learning in higher education (Arosio, 2025). The ethical implications of AI applications, especially in monitoring social media for public health data, demand careful consideration to balance innovation with privacy, fairness, and accountability (Flores & Young, 2022)(Oguru, 2025).