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)
HLPF follows up the UN-SDG goals.
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
Regulations for autonomous vehicles
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)
9.1.1. Ethical norms and standards: Using ROAM frameworks (Rights, Openness, Accessibility, Multi-stakeholder governance) to evaluate AI.
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
10.1.3. Spatial modeling to generate accurate high-res population maps
10.2.2. Bill and Melinda Gates Foundation
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
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
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
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
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
ITU focus group on AI health
Standardize evaluation and validation of ML algorithms
AI for health use cases
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
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