Here is a map of current UN activities in AI that I compiled from published UN reports. Everyone in AI has a responsibility to understand the opportunities to positively impact the world. If we don’t build responsible technology and solutions for our future generations, we will leave the world in shambles. We need to collaborate among ourselves to democratize technology, make it simple and build tools for solving technology adoption in areas where it is needed.

As you are reading this, think about what kind of tools can we build that makes adoption easy. More importantly, how can we collaborate to make progress on our efforts? There is no reason we cannot make progress as a community towards these goals; technology is mature in several of these areas.

Map of AI efforts by United Nations — https://www.saranyan.com/blog-pics/2019/8/19/un-ai-efforts

1. UN Department of Economic and Social Affairs (UNDESA)

1.1. AI areas

1.1.1. AI in eGovernment

  • Administrative (Filling forms, answering questions, routing requests, translation, drafting documents) tasks. Allows time for citizen engagement efforts.

  • Opportunities — Not to exacerbate issues around privacy, service delivery and ethics. Improve resource allocation and learn from historical data, which tends to mostly structured.

1.2. HLPF (High level political forum on sustainable development)

1.3. STI forum (Science, Technology and Innovation forum)

  • Forum focusses on SDG goals to connect technologists, stakeholders and identify technology gaps.

1.4. UN big data and official statistics

  • Working group that investigates the challenges and opportunities in Big Data including issues around data privacy, ethics, availability, etc.

1.5. Big data for public good

1.5.1. ECOSOC partnership — The Economic and Social Council (ECOSOC) is the United Nations’ central platform for reflection, debate, and innovative thinking on sustainable development.

2. United Nations Office for Disaster Risk Reduction (UNISDR)

  • Scanning the frontier technology horizon, what technological innovations are effectively ensuring resilience and DRR today?

  • How are frontier technologies being applied in vulnerable country contexts to ensure resilience?

  • What are the key barriers to scaling these technologies, noting typical challenges from capacity development and digital skills, finance and property rights areas?

  • How can governments address these barriers and accelerate the use of these solutions for DRR and resilience?

3. United Nations Conference on Trade and Development (UNCTAD)

3.1. Sustainable development

3.1.1. Using AI to accelerate SDG goals. Refer to my previous article on AI for social good.

3.2. Frontier technologies

3.2.1. Policy areas. Refer to UNCTAD policy hub.

3.2.2. International Tech and Trade Initiative (ITTI)

  • Businesses to trade (B2T) and leveling the playing field for SME. Example — Banks can use AI to make loan decisions to SMEs to reduce processing time. SMEs can integrate AI into their business processes for audit capabilities to stay compliant. AI products/startups have to stay on top of various government and international regulations.

  • Countries to trade (C2T): Allow nations to expand their competitive advantages. Using data to promote a given sector of the national economy both nationally and internationally. AI can speed up the process of information gathering. AI machines can can analyze data across WTO (World Trade Organization) rules to understand the pros and cons of different strategies, even suggesting the steps to be taken.

  • Negotiator to trade (N2T): Structured access to cloud-based resources to make life easier for trade negotiators. AI Negotiation support systems are able to suggest win-win outcomes. This can be adapted for trade negotiations.

  • Multilateral to trade (M2T): Multilateral trade officers will be able to weigh pros and cons of alternative scenarios through predictive AI. Automated analysis of trade, market, campaign and negotiation data to understand the impact of tariffs, investments, etc on the global market.

  • AI can help infrastructure (energy, networks, etc) to be shared among countries, communities or individuals. New business models for infrastructure are possible.

3.3. Focus on bottom billion

3.3.1. Understand the opportunities and challenges because of Frontier tech

3.3.2. Understand the impact on economy and society

3.4. Raise awareness of AI

4. UN programme on HIV/AIDS (UNAIDS)

4.1. AI areas

4.1.1. Information Source — Marlo Chatbot.

4.1.2. Understanding population trends and prediction of population growth.

5. United Nations Development Programme (UNDP)

5.1. AI Areas

5.1.1. Automation of Rapid Integrated assessment (RIA) (Evaluates national development priorities aligning to SDG targets)

5.1.2. AI to help the work of experts in development policy

5.1.3. Predict proxy levels of poverty (Use covariates from call details records to determine poverty levels)

5.2. Portfolio of frontier technology experiments

5.2.1. Drones and ML for environmental protection

5.2.2. Disaster preparedness

5.2.3. Mapping of refugee settlement (Develop infrastructure for refugees)

5.2.4. MapX: Catalog of best available spatial data and tools to map and monitor sustainable use of natural resources.

  • Partnership between UNDP, World bank, UNEP, Global information DB

  • Cloud based geospatial solution

5.3. UN Biodiversity lab

5.3.1. Partnership between UNEP and UNDP (Spatial analysis platform to enhance decision making on conservation)

5.3.2. Goal — Accelerate biodiversity targets (Aichi biodiversity targets)

6. United Nations Economic Commission for Europe (UNECE)

6.1. AI areas

6.1.1. WP.29

  • Regulations for autonomous vehicles

  • Vehicle management

  • Limiting wrong use of AI

6.2. Future networked car event

6.2.1. UNECE and ITU partnership

6.2.2. Status and future of vehicle communications and automated driving

6.2.3. Ethical considerations in AI and autonomous systems

6.3. Urban KPIs for smart cities

6.3.1. Circular cities: Green infrastructure that ensures mitigation of waste. Green infrastructure is a strategically planned network of natural and semi-natural areas that feeds into a circular utilization of resources and reduction of waste. AI is useful for planning.

6.3.2. Financing smart sustainable cities

6.3.3. Blockchain and AI in cities

6.3.4. Sensing technologies and IoT in cities: Connected communities through smart sensors and decision making. Through AI, it is possible to understand how cities are being used.

7. United Nations Interregional Crime and Justice (UNICRI)

7.1. Crime prevention

7.2. AI areas

7.2.1. Threat detection and landscape: Smarter, autonomous security systems that learn without human intervention and keep pace with the amount of data the security systems produce.

7.2.2. Behavioral patterns of terrorist networks: Detect pattern anomalies. Monitor new technology landscape to ensure preparedness. Advance understanding of and prepare for the risk of malicious use of AI by criminal and terrorist groups.

7.2.3. Predictive policing

7.3. Trust in AI and robotics

7.3.1. Ethics and Legal issues: Law enforcement using AI should take steps to ensure fairness, accountability and transparency. The use should be communicated to communities.

7.3.2. Algorithmic bias

7.3.3. Explainability (non black-box)

7.4. Criminal justice

7.4.1. Public safety video and image analysis.

7.4.2 . Scene understanding: Ability to develop text that describes the relationships between objects in a series of images to provide context.

7.5. Law enforcement AI use cases

7.5.1. Autonomously research, analyze and respond to requests for international mutual legal assistance.

7.5.2. Advanced virtual autopsy systems

7.5.3. Autonomous robotic patrol systems

7.5.4. Predictive policing and crime hotspot analytics for optimizing law enforcement resources

7.5.5. Computer vision to identify stolen cars

7.5.6. Tools to identify vulnerable and exploited children

7.5.7. Behavior detection tools to identify shoplifters

7.5.8. Fully autonomous tools to identify and fine online scammers

7.5.9. Crypto based packet tracing tools enabling law enforcement to tackle security without invading privacy.

8. United Nations Environment Programme (UNEP)

8.1. AI areas

8.1.1. Fintech for sustainable development: Less vulnerable financial systems, Creation of new markets, minimizing the risks and maximizing opportunities because of robotic automation.

8.1.2. Animation of the physical world: Using IoT and AI to connect the physical and natural assets, machines and infrastructures together will allow them sensing and responding to each other.

8.1.3. Planetary data governance

  • World environment situation room, a new data platform, but also a new way of accessing data, via a worldwide partnership model with multiple data-centers

8.1.4. Planetary dashboard for Surface water monitoring.

  • Organizations partnering the efforts — NASA, ESA, Google Earth, JRC

9. United Nations Educational, Social and Cultural Orgnaization (UNESCO)

9.1. Ethics

9.1.1. Ethical norms and standards: Using ROAM frameworks (Rights, Openness, Accessibility, Multi-stakeholder governance) to evaluate AI.

9.2. Policy

9.2.1. Freedom of expression, privacy and inequality

9.2.2. Safe and beneficial use of AI

9.3. Capacity Building

9.3.1. Counter the knowledge divide and marginalization of people

9.3.2. Create awareness around AI science and technologies

9.4. Platform for ethical dimensions on AI

9.4.1. Reflect on how AI could transform societies

9.4.2. Risks and benefits of transformations

10. United Nations Population Fund (UNPF)

10.1. GRID (Geo-referenced Infrastructure and Demographic Data for Development)

10.1.1. Access to spatial datasets for evidence-based and humanitarian decision making

10.1.2. High resolution spatial reference data

  • Population

  • Settlements

  • Infrastructure

  • Boundaries

10.1.3. Spatial modeling to generate accurate high-res population maps

10.2. Partnerships

10.2.1. UNFPA

10.2.2. Bill and Melinda Gates Foundation

10.2.3. DFID

10.2.4. Flowminder/Worldpop

10.2.5. Oak ridge national lab

10.2.6. Center for international earth science network

11. Comprehensive Nuclear-Test-Ban Treaty (CTBT)

11.1. International Monitoring System (IMS)

11.1.1. 337 facilities worldwide (monitor the planet for signs of nuclear explosions)

11.2. International Data Centre (IDC)

11.2.1. acquires data from the IMS global monitoring stations

11.2.2. Data distribution

11.3. On-site inspections (OSI)

11.4. AI efforts

11.4.1. Seismic phase

  • Data processing on Seismic signals

  • Classifiers to determine nuclear activity

  • Improve performance of classifiers

  • DNN architectures

  • Different data like waveforms

11.4.2. Event detection

11.4.3. Satellite monitoring (Change detection in inspected areas)

11.4.4. Seismic aftershock monitoring (Changes in the geological structures caused by a possible nuclear explosion)

11.4.5. Operations for sustainment of IMS

12. United Nations Children’s Fund (UNICEF)

12.1. AI Areas

12.1.1. Magic box (Open source platform that combines new sources of data in computational modeling to generate insights like spread of epidemic)

12.1.2. Project connect (Satellite imagery and DL for infrastructure mapping)

  • 130000 Schools mapped in 9 countries

12.1.3. Deep empathy (Use AI to increase empathy for victims in far-away disasters)

12.1.4. Generating equitable data sets

  • Training equitable AI algorithms

  • Compilation of symbols from different languages and cultures for children with disabilities

12.1.5. Venture fund investments

  • Capacity building

12.1.6. Building internal knowledge and capacity

12.1.7. Drone imagery and AI (Improve response during outbreaks)

12.2. Policy change

12.2.1. Use AI and NLP to understand and analyze constitutions from 194 countries

  • Advocate for human and environmental rights

12.2.2. Research on impact of AI on economy

12.3. Future work

12.3.1. Chat bot, social messaging platforms

12.3.2. Analyze implications of child labor in supply chain

12.3.3. Optimizing transactions and communication flows in organizations (Train on internal datasets)

13. United Nations High Commissioner for Refugees (UNHCR)

13.1. Predictive analytics

13.1.1. Project Jetson (Predict population movements in the horn of Africa, ex. Somalia)

13.2. AI in HR

13.2.1. Screening candidates to the talent team

14. United Nations Global Pulse

14.1. Achieve critical mass of high potential applications in AI and big data

14.1.1. Refer to UN Global pulse project page

14.2. Lower systematic barriers to innovation

14.3. Strengthen the data innovation ecosystem

14.4. AI areas

14.4.1. Using speech recognition technology to inform on SDG related topics in Africa

  • Convert public radio discussions to local languages

14.4.2. AI to detect structures in Satellite images to mark humanitarian efforts

14.4.3. Haze Gazer — a crisis analysis tool

  • Use satellite images and population data to enhance disaster management efforts

  • DL to determine air quality by fusing meteorological data, satellite imagery and social media pics

15. International Civil Aviation Organization (ICAO)

15.1. AI areas

15.1.1. Analysis of aviation infrastructure in terms of readiness for responding to disasters

15.1.2. Predictive model for preventing the spread of communicable diseases through aviation

15.1.3. Natural Language interface for decision-makers to interact with safety information

15.1.4. Neural Network application to classify Notice to Airmen (NOTAM) to reduce the noise

16. International labour organization (ILO)

16.1. AI areas

16.1.1. Impact on jobs and inequality

  • Risks, trends, analysis

  • Governance and infrastructure gaps

  • Working conditions in micro task platforms

16.1.2. Big data analytics

  • Capabilities of middle-income and emerging economies

  • Opportunities for economic diversification and new technology adoption

  • Equal access to job opportunities

16.1.3. Skill development

  • AI tools to measure current and potential skill demand

  • Diagnotsic tools

  • Understanding evolution and composition of job tasks

  • Understanding skills and knowledge content of occupations

  • Job matching policies

16.1.4. AI to improve learning delivery

16.1.5. Framework for public employment creation policy

16.1.6. Monitor child labour

  • Combating child labour and human trafficking

  • assess situation of the child and provide recommendations

17. International Telecommunications Union (ITU)

17.1. AI for good global summit

17.1.1. Accelerate development of AI solutions

17.1.2. Democratize AI development

  • Empower people to address social problems

  • Poverty

  • Hunger

  • Health

17.2. ML infra for 5G

17.2.1. Functional network architectures: 5G networks are far more complex than previous generation networks. Higher frequency radio technology, complex antenna configurations, sophisticated connectivity mechanisms like beamforming, dynamic and elastic network resources, etc require machine learning approaches. AI will play a significant factor in the design, deployment and monitoring of 5G network infrastructure.

17.2.2. Interfaces: Data and connectivity, Reducing gaps between edge and cloud computing.

17.2.3. Protocols and algorithms: Self-healing and resilience, Algorithms for streaming quality improvements, Detection of leakage from HFC networks, etc.

17.3. AI for health

17.3.1. ITU

  • ITU focus group on AI health

  • Standardize evaluation and validation of ML algorithms

  • Develop benchmarks

  • Standards frameworks

  • AI for health use cases

  • Mobile diagnostics

  • 6B smartphone deployments by 2021

  • ITU briefings on AI

  • Publication on AI

  • Global AI repository

17.3.2. WHO efforts

  • Access to wellness

  • Tracking outbreak of infectious diseases

  • Modeling and treatment of chronic diseases

  • Collection and storage and sharing of large datasets

18. United Nations Institute for Disarmament Research (UNIDIR)

18.1. Impact of AI on international Security

18.2. Weaponization of AI risks

18.2.1. Functional concerns like accidents in deployment

18.2.2. Manipulation and weaponization

  • Vulnerabilities

  • Datasets

  • Algorithms

  • Algorithm-driven weaponization of information

  • Conflict and stability

18.2.3. Commercial development

18.3. Mitigating harm from AI weaponization/security

19. United Nations Industrial Development Organization (UNIDO)

19.1. SAP digital boardroom

19.1.1. Monitor progress on SDG 9 (Sustainable industrialization)

19.2. Knowledge sharing thorugh Global Forum events

19.3. Understand role of AI in convergence of technologies

19.4. Industrial 4.0 efforts, opportunities and impact of AI

19.4.1. Industry 4.0 center in South Africa

19.4.2. Realtime monitoring of energy efficiency

19.4.3. Framework for industrial parks, eco parks

19.4.4. Bridge for City

References

  1. UN efforts on AI.

  2. AI for citizen services and Government — Harvard

  3. Webinar on Government Innovation and Disaster Risk Reduction

  4. McKinsey notes on AI frontier

  5. New Frontier of competitiveness in emerging technologies

  6. ITTI Trade initiatives

  7. Risks and benefits of AI and Robotics — UNICRI report

  8. AI and Robotics for Law Enforcement — UNICRI report

  9. UNEP — Fintech and sustainable development

  10. UNESCO — Steering AI for knowledge societies

  11. UN Global Pulse projects

  12. Machine learning for 5G future