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.
