A Blueprint for Intelligent Economies 2025

Robust ethical and regulatory frameworks for AI are essential to ensuring that technology benefits society while reducing risks. Establishing standards prevents misuse, bias and ethical breaches, strengthening trust in AI and promoting responsible development and use.2.3 Establish guardrails for ethics, safety and security Key challenges in establishing guardrails for responsible AI TABLE 3 Establishing guardrails for ethics, safety and security requires a coordinated action plan involving many stakeholders. A preliminary set of five capabilities frames how this strategic objective can be delivered: Ethical guardrails Ethical guardrails are essential for building societal trust and ensuring AI systems align with both global and local values. AI systems predominantly trained on Western-centric data risk perpetuating cultural biases when deployed globally, thereby creating ethical dilemmas in culturally diverse settings. Efforts like the Organisation for Economic Co- operation and Development’s (OECD) International Standards and regional initiatives such as the African Union’s Continental AI Strategy play crucial roles in reflecting diverse values. However, the lack of universally accepted ethical standards significantly complicates the implementation of ethical principles in AI systems.As AI ethics governance evolves, there is a growing recognition of the need for culturally sensitive approaches. In 2024, eight global tech companies announced their intention to align with the United Nations Educational, Scientific and Cultural Organization’s (UNESCO) Recommendation on the Ethics of AI,22 which emphasizes cultural sensitivity in AI development and deployment.23 Initiatives in Australia, Canada and New Zealand focus on integrating indigenous knowledge and perspectives, such as those of the Māori, into AI systems.24 Similarly, frameworks developed by the Council of Europe25 and Singapore reflect their unique societal values and risk tolerances. However, the UN’s 2024 report, Governing AI for Humanity, highlights a critical current gap: “whole parts of the world have been left out of international AI governance conversations … primarily in the Global South”.26 This homogenization of AI ethics, dominated by perspectives from the Global North, risks excluding diverse cultural philosophies and interests worldwide. Addressing this issue requires stakeholders to commit to comprehensive approaches to ethical AI development and deployment.Key challenges Examples of successful initiatives Mitigating bias, ensuring equity and inclusionConsensus on ethical AI: Consensus can be reached through collaborations between international and regional bodies, along with industry and civil society engagement. Awareness campaigns: Initiatives that solicit cultural and regional feedback to inform policy development are being prioritized within historically underrepresented groups and communities that do not fully trust AI. Navigating evolving regulatory landscapesEnhanced data and technology regulations: These are helping organizations to consider the changing landscape within the context of AI while assigning responsibilities and ownership of AI’s regulatory challenges. Risk-based regulatory approaches: These are being used to ensure regulation remains in line with the fast-paced advancement of AI, addressing the balance between supporting innovation within defined AI safety and security considerations. Securing AI against emerging risksAI safety bodies: These bodies are contributing to the development of global and regional AI safety standards. Agreement to “red lines”: Defining the highest-risk use cases through the continuous dialogue between all stakeholders in the AI value chain Implementing accountable AI practicesAdaptation of existing AI regulatory frameworks: Governments and industry are using regulation and self- regulation to encourage operationalization of self-governance. AI intellectual property (IP) rights and legal uncertaintyAlignment on global IP standards: Collaboration between international IP boards and industry groups is ensuring that emerging AI technology definitions are common within rights and legal frameworks. International AI IP sharing platforms: These are being developed to enable cross-boundary commercial partnerships and alignment around R&D outcomes. Blueprint for Intelligent Economies 13