AI@UN

Brainstorming Call for Action

We, the peoples, the first line of the UN Charter, should guide the development of artificial intelligence (AI) at the UN. Contributions of countries, companies, and communities worldwide to AI@UN could bolster the high potential of AI to support the UN’s missions of upholding global peace, advancing development, and protecting human rights.

An inclusive approach to AI development is key for upholding the principle of impartiality, a pillar of the UN. 

The AI@UN has two main goals:

• support policy discussions on the sustainable AI transformation of the UN ecosystem; and
• inspire the contributions of AI models and agents by member states and other actors.

As a starting point, we would like to propose the following guiding principles for the development and deployment of AI models, modules, and agents at the UN: 

1. Open Source: Abiding by the open-source community’s principles, traditions, and practices. Openness and transparency should apply to all phases and aspects of the AI life-cycle, including curating data and knowledge for AI systems, selecting parameters and assigning weights to develop foundational models, vector databases, knowledge graphs, and other segments of AI systems. 

2. Modular: Developing self-contained modules according to shared standards and parameters. AI@UN should start with AI agents and modules for core UN activities and operations.

3. Public Good: ‘Walking the talk’ of public good by using AI to codify UN knowledge as a public good to be used by countries, communities, and citizens worldwide. By doing so, the UN would inspire the AI-enabled codification of various knowledge sources, including ancient texts and oral culture, as the common heritage of humankind.   

4. Inclusive: Enabling member states, companies, and academia to contribute, by their capacities and resources, to the technical, knowledge, and usability aspects of AI@UN. A modular approach to AI@UN would require a wide range of knowledge contributions beyond technical ones.  

5. Multilingual: Representing a wide range of linguistic and cultural traditions. The special focus will be on harvesting the knowledge and wisdom available in oral traditions not available in the written corpus of books and publications.

6. Diverse: Ensuring inputs from a wide range of professional, generational, cultural, and religious perspectives. While AI@UN should aim to identify convergences between different views and approaches, diversities should not be suppressed by the ‘least common denominator’ approach inferred in AI. Diversity should be built in through the transparent traceability of sources behind AI-generated outputs. 

7. Accessible: Adhering to the highest standards for accessibility, in particular for people with disabilities. AI@UN must increase the participation of people with disabilities in UN activities, from meetings to practical projects. Simple solutions and low-bandwidth demand should make the system affordable for all. 

8. Interoperable: Addressing a problem of organisational silos in managing knowledge and data within the UN system. Interoperability should be facilitated by knowledge ontologies and taxonomies, data curation, and shared technical standards.

9. Professional: Following the highest industry and ethical standards of planning, coding, and deploying software applications. This will be achieved by testing, evaluating, and submitting AI solutions to a peer-review process. The main focus will be maximising the reliable development of AI solutions to directly impact human lives and well-being. 

10. Explainable: Tracing every AI-generated artefact, such as a report or analysis, to sources used by AI inference, including texts, images and sound recording. Explainability and traceability would ensure transparency and impartiality of AI@UN systems.

11. Protecting Data and Knowledge: Achieving the highest level in protecting data, knowledge and other inputs into AI systems. Explainability and traceability will be critical for protecting data and knowledge used by the AI@UN system.  

12. Secure: Guaranting the highest possible level of security and reliability of AI@UN. Open source, red-teaming, and other approaches will ensure that the systems are protected by having as many ‘eyes’ as possible to test and evaluate AI code and algorithms. AI communities will be encouraged to contribute to red-teaming and other tests of the AI@UN system.

13. Sustainable: Realisation of SDGs and Agenda 2030 through three main approaches: firstly, ensuring that SDGs receive higher weights in developing AI models and tools; secondly, the AI systems themselves should be sustainable through, for example, sharing the code, building resources, and providing proper documentations and development trails; thirdly, AI solutions should be developed and deployed with environmental sustainability in mind.

14. Capacity: By developing an AI system, the UN should develop its own and wider AI capacities. Capacity development should be: a) holistic, involving the UN Secretariat, representatives of Member States, and other communities involved in UN activities; and b) comprehensive, covering a wide range of AI capacities from a basic understanding of AI to high-end technical skills. 

15. Future-Proofing: Planning and deploying systems dealing with future technological trends. Experience and expertise gathered around AI@UN should be used to deal with other emerging technologies, such as augmented/virtual reality and quantum computing. 

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The call to action is clear: by leveraging the transformative power of AI, the UN can turn an ongoing challenge into a watershed moment for global governance. The UN’s commitment to innovation and adaptability will test its resilience and underscore its enduring relevance and leadership in charting the course of human progress.