Problems Statement

What ELNA is trying to solve

Problems of Centralized AI

  • Trouble in Governance: Centralized AI systems often face governance issues due to the concentration of decision-making power within a single entity or a limited group of entities. This centralization can lead to biases in AI development and deployment, lack of accountability, and challenges in ensuring ethical standards. Moreover, the interests of the governing body may not always align with those of the broader user base, leading to decisions that favor profit over public good or innovation.

  • Lack of Explainability: Many AI systems operate as "black boxes," where the decision-making process is opaque. The public won't be privy to the internal agenda and roadmap of the AI’s centralising entity. This lack of transparency can obscure how personal data is being used, making it difficult for individuals to understand or contest decisions made by AI that affect them.

  • Ehical AI Development: Encouraging the ethical development of AI, including considerations of fairness, privacy, and human rights, is critical. This includes ethical AI guidelines and practices that prioritize user privacy and data security

  • Lack of security and privacy for user data and usage, with barely any incorporation of practices like encryption, access control, and regular security audits to protect against data breaches and unauthorized access.

  • Censorship: Centralized control over AI platforms raises significant concerns regarding censorship. Entities that control these platforms have the power to restrict or manipulate the flow of information, suppress dissenting voices, or promote certain viewpoints over others. This control can undermine freedom of expression and access to information, which are crucial for democratic societies and the ethical development of AI technologies.

Challenges in Web3

  • Lack of Accessible Tools in Blockchain for AI Development: Blockchain technology, while promising for decentralizing AI, currently faces challenges due to a lack of sophisticated tools and frameworks specifically designed for AI development. This scarcity hinders the efficient creation, testing, and deployment of AI models on blockchain platforms, slowing innovation and adoption rates compared to more mature, centralized AI development ecosystems.

  • Complex Deployment, Scaling, and Management of AI Applications in Blockchain: Deploying, scaling, and managing AI applications on blockchain platforms can be exceptionally complex due to the inherent limitations of blockchain technology, such as transaction throughput, latency, and the need for consensus. These challenges complicate the integration of AI with blockchain, making it difficult for developers to build scalable and efficient AI applications that can leverage the full potential of decentralization.

  • Need for the Combination of Multiple Talent Sets for Development: The development of decentralized AI applications requires a multifaceted skill set that spans blockchain technology, AI/machine learning, cybersecurity, and domain-specific knowledge. This multidisciplinary approach poses a significant barrier to entry, as it necessitates collaboration among experts from diverse fields to innovate, develop, and maintain sophisticated decentralized AI solutions.

  • Support and Monitoring: Ensuring ongoing support and monitoring of decentralized AI applications is challenging due to the distributed nature of blockchain. Unlike centralized systems, where a single entity can oversee operations, decentralized systems require collaborative efforts for maintenance, updates, and problem resolution. This dispersion of responsibility can lead to issues in efficiently identifying and addressing operational challenges, security vulnerabilities, and performance bottlenecks.

  • Monetization, Collaboration, Transparent Revenue Model, and Sharing: Developing a fair, transparent, and effective monetization and revenue-sharing model is a significant challenge in decentralized AI ecosystems. Ensuring that creators, developers, and users can collaborate and share the economic benefits equitably requires innovative approaches to value distribution. Traditional models often fall short in addressing the unique dynamics of decentralized networks, necessitating new frameworks that promote transparency, incentivize participation, and reward contributions fairly across the ecosystem.

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