Artificial intelligence (AI) systems present unprecedented challenges for socio-technical systems (STS) that fundamentally reshape our understanding of technology-society interactions. These multifaceted challenges span organizational, ethical, and governance dimensions, requiring comprehensive analytical frameworks to address their complexity. This paper examines the intricate interplay between AI technologies and social structures through a systematic analysis of their mutual constitution and evolution. We employ an interdisciplinary approach, integrating perspectives from computer science, sociology, organizational studies, and ethics to develop a holistic understanding of AI's socio-technical implications. Through critical examination of algorithmic bias, accountability frameworks, and organizational integration challenges, we identify key patterns in AI-society interactions that demand new theoretical and practical approaches. Our analysis reveals that algorithmic bias emerges from multiple interconnected sources including training data, design choices, and deployment contexts, while accountability mechanisms designed for human decision-makers prove inadequate for distributed AI systems. Organizational integration requires fundamental transformation beyond technical implementation, encompassing structural changes, capability development, and cultural shifts. The research synthesizes current literature on AI governance and implementation to develop a comprehensive understanding of the socio-technical landscape, identifying critical gaps between theoretical frameworks and practical implementation. Building on this foundation, we propose integrated frameworks for addressing socio-technical challenges that balance technical innovation with social considerations. Our findings highlight the critical need for interdisciplinary approaches to AI integration that transcend traditional disciplinary boundaries, adaptive governance mechanisms that can evolve with technological change, and participatory approaches that engage diverse stakeholders. This work contributes to understanding AI's transformative impact on socio-technical systems while providing actionable insights for practitioners and policymakers navigating this complex terrain.
K Arvapally Ravi (2025) studied this question.