# The Democratization of Artificial Intelligence: Theoretical Framework
## 2 Context and Current Trends in AI Democratization  
The origins of AI date back to the mid-20 th century, with pioneering work by researchers like [PERSON] [7] and [PERSON] [8]. Initially, AI research was confined to academic institutions and characterized by limited computational resources and theoretical exploration. The advent of micro-computers in the 1980s [9] led to the democratization of computing. Significant advancements in machine learning and the availability of big data in the late 20 th and early 21 st centuries marked a shift in AI development. These advancements enabled the creation of more sophisticated algorithms and practical applications, sparking increased interest from industry and governments. The open-source movement has played a crucial role in AI democratization [10]. Platforms like TensorFlow [11], PyTorch [12], and scikit-learn [13] have made powerful AI tools available to anyone with an internet connection, fostering innovation and collaboration globally. Current trends in AI democratization [14; 15; 16] unveil considerable topics that must be addressed. Some belong to the application fields of AI, others to the fastest technological evolution, and the challenges that need to be addressed when AI is adopted in the various fields of society and economy [17]. The current AI democratization trends may be considered at the following levels: governance and decision-making, accessibility and affordability, distributed learning systems, social and ethical considerations, and public data trust. Democratization must include governance reforms to ensure public deliberation and stakeholder involvement in AI research and development (R&D). Barriers include deterministic AI development narratives, opaque decision-making processes, and the dominance of technical experts and business executives [18]. Effective democratization requires a framework for the democratic governance of AI [19]. Democratizing AI includes making technologies accessible and affordable, ensuring portability, explainability, credibility, and fairness. These aspects require advancements in science, technology, and policy [20]. Large-scale distributed AI systems, characterized by self-organizing, hierarchical structures, can contribute to democratizing AI by enabling collaborative learning across various devices and environments. These systems aim to surpass traditional federated learning by enhancing generalization and specialization capabilities [21]. Addressing social and ethical challenges is crucial for democratizing AI. This includes ensuring AI transparency, privacy, and fairness, particularly in data-centric sectors like energy management. A framework for AI and data democratization can help mitigate biases and enhance decision-making processes [22]. Establishing public data trusts to control training data for AI models can ensure that the economic value generated by AI is redistributed to address its negative externalities. This approach emphasizes collective decision-making and the regulation of data use [23]. Other AI democratization trends, such as cloud-based AI services, educational initiatives, and AI in the public sector, may be considered. Cloud computing has been instrumental in democratizing AI by providing scalable resources that can be accessedon demand. Amazon Web Services (AWS), Google Cloud, and Microsoft Azure offer user-friendly and cost-effective AI and machine learning tools, enabling small businesses and individual developers to leverage AI technologies [24; 25; 26; 27]. Regarding the recent trend of educational institutions, universities and massive open online courses (MOOCs) on platforms increasingly offer courses and certifications in AI and machine learning. Programs from organizations such as Coursera, edX, and Khan Academy [28; 29; 30] have made high-quality AI education accessible to a global audience worldwide, empowering a new generation of AI practitioners and users. Governments worldwide recognize AI's potential to drive economic growth and improve public services [31; 32].