The integration of Artificial Intelligence (AI) into various facets of society has significantly impacted concepts related to social responsibility (SR). This chapter explores these impacts through a comprehensive lens, using the concept of Interactions as an explanation tool to delve into the dynamic interactions and relationships facilitated by AI, aiming to propose ways to enhance social responsibility (SR) levels on a personal, organisational and the society level. For the AI technologies development, SR can provide a requisitely holistic—systemic understanding of the interconnectedness of ethical, economic, social, and environmental dimensions, ensuring that AI instances are developed and deployed in ways that benefit society as a whole. This perspective is crucial for addressing the multifaceted impacts of AI on individuals, organisations, and society. AI encompasses a broad effects of AI technologies, which affect and will affect humankind in a multitude of ways ranging from significant improvements to life-threating risks. It will result in unifying data sources, enabling a more holistic situational understanding and instant cross-humanity learning. This will enhance diagnostic tools and personalised interactions, increase efficiency by automating routine tasks, enhance decision-making with data-driven insights, and provide personalised customer experiences. However, these advancements come with significant challenges, including job displacement, privacy concerns, biases in AI algorithms, economic disruptions, and physical violence. The range of AI aspects’ impacts necessitate sophisticated and dynamic governance mechanisms. In this chapter, we include the concept of Interactions as one of the examination approaches, focusing on the dynamic exchanges, communication, and influences between AI systems and human stakeholders. By examining the interactions influenced by AI, we aim to uncover patterns and structures that govern behaviour within AI-driven systems, leading to more effective and responsible AI deployment. For instance, we explore how AI-driven SR-based governance can guide all participants to co-design sustainable interactions. The results of this study propose invoking AI in the double loop learning to gradually introduce SR concepts to the management processes to align them with societal values and priorities, promoting fairness, transparency, inclusivity and building relations. The focus of SR-related concepts should be built upon the long-term implications of interactions. These may, if interpreted correctly, display the need to follow SR concepts and provide the methods on how to integrate them into the managerial processes. Limitations: The methodology of interaction observations is new. Thereby, it may be prone to conceptual and applicative misinterpretations.