# The Democratization of Artificial Intelligence: Theoretical Framework
## 5 Proposing an AI Democratization Theoretical Framework  
This section presents an AI democratization framework based on the literature review and analysis presented in Sections 2-4. The framework is composed of four underlying components that must be present and considered in any scenario: accessibility (critical for democratic operationalization), affordability (ensuring generalized access to AI technologies), usability (determinant to AI adoption), and ethical regulations (support the ethical development and usage of AI technology). The framework comprises three main intertwined dimensions: drivers, challenges, and strategies. Each dimension is further divided into sub-components, as shown in Figure 1. The dimensions represent the different aspects of AI democratization, such as access, participation, and governance. The drivers are the factors that enable or facilitate AI democratization, such as technological advancements, social movements, and ethical principles. The barriers are the challenges or obstacles that hinder or prevent AI democratization, such as technical complexity, data privacy, and ethical dilemmas. The impacts are the potential consequences or outcomes of AI democratization, both positive and negative, such as economic growth, innovation, job displacement, and global disparities. The framework identifies collaboration, education and training, and ethical practices as strategies that should be followed for AI Democratization. The framework aims to provide a comprehensive and holistic view of the phenomenon of AI democratization and to guide further research and practice in this field. The key components of AI democratization include accessibility, affordability, usability, and ethical and regulatory considerations (Table 1). Accessibility is divided into technological and educational aspects. Technological accessibility focuses on developing user-friendly AI tools, open-source AI platforms and libraries such as TensorFlow and PyTorch, and cloud-based AI services provided by companies like Google Cloud AI and AWS AI. Educational accessibility aims to make AI learning resources widely available through online courses and tutorials on platforms like Coursera and edX, university programs and certifications, as well as workshops, seminars, and hackathons. Affordability is crucial for widespread AI adoption and is achieved through cost reduction and incentive structures. Cost reduction involves providing affordable computational resources, subscription models for AI services, and securing government and private funding for AI research and development. Incentive structures include grants and scholarships for AI education and public-private partnerships to subsidize AI projects. Usability ensures that AI technologies are user-friendly and adaptable. This involves focusing on user experience design by creating intuitive interfaces for AI tools, simplifying programming languages and frameworks, and offering comprehensive documentation and support communities. Customization and flexibility are also essential, as they provide tools that can be tailored to specific needs and ensure interoperability with existing systems and data. Figure 1: AI democratization framework. Ethical and regulatory considerations are vital for responsible AI deployment. Ethical AI practices include transparency in AI algorithms and data usage, bias detection and mitigation, and privacy-preserving AI techniques. Regulatory frameworks involve developing policies that promote responsible AI usage, setting standards for data protection and AI safety, and fostering international cooperation on AI governance. The drivers of AI democratization encompass technological advancements and socio-economic factors (Table 2). Technological advancements are fueled by innovations in AI research, including breakthroughs in machine learning, natural language processing, and computer vision, as well as the development of more efficient and scalable algorithms.