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ICTs for a Sustainable World #ICT4SDG

AI Breakthrough Tracks

​​​​Track 1: AI + Satellite Imagery​
Team Lead: Stuart Russell (Berkeley)
Members: Lars Bromley, Einar Bjørgo (UNOSAT); Miguel Luengo-Oroz, Marguerite Nyhan (UN Global Pulse); Andrew Zolli (Planet), James Crawford (Orbital Insight)

Track Overview
This track will help address how satellite imagery together with artificial intelligence and machine learning can help meet the Sustainable Development Goals (SDGs). The focus of the track will be identifying challenges to large-scale automated analyses of satellite imagery libraries that inform models and knowledge systems on sustainable development. At the same time the track will serve to create partnerships among the artificial intelligence community, satellite imagery providers, research labs, analysts, sustainable development implementers in Member States and the United Nations system, and others. As implementing the SDGs presents enormous policy challenges this track will be ambitious in its scope, discussing artificial intelligence methods for rapid and accurate analysis of satellite imagery that can feed into decision-making processes at national levels. Informing national decision-making for sustainable development requires effective knowledge systems, linking data and information to policy across almost all sectors. Satellite imagery is particularly potent for such purposes as it can potentially measure multiple indicators repeatedly over time and across large areas. Such information allows analysis of underlying issues affecting multiple SDGs and can inform knowledge systems addressing key policy issues. Operational and digital knowledge systems of this type may be relatively far off but the elements involving satellite imagery can already be actively explored by the global research and policy communities. To that end, this track will seek to create a framework for ‘challenges’ whereby specific satellite imagery analytical tasks are posed to the machine learning community to solve. To help focus discussions three crosscutting themes have been chosen:



Track 2: AI + Health: Artificial Intelligence – a game changer for Universal Health Coverage?
Team Lead: Marcel Salathé (EPFL); Ramesh Krishnamurthy, Senior Advisor, Department of Information, Evidence and Research, World Health Organization (WHO); Sameer Pujari, “Be Healthy, Be Mobile” Project Manager, World Health Organization (WHO)​​

Track Overview
The Sustainable Development Goals (SDGs) are the 17 global priority goals to be achieved by 2030, agreed upon by the UN member states in 2015. The 17 goals include a more detailed 169 targets and indicators. Achieving the SDGs within the timeframe is an enormous challenge across the scientific and economic community.

Universal Health Coverage (UHC) is one of the SDG targets that aims at ensuring that all people can access quality health services, to safeguard all people from public health risks, and to protect all people from impoverishment due to illness, whether from out-of-pocket payments for health care or loss of income when a household member falls sick.

Artificial Intelligence applications can be a game changer to achieve Universal Health Coverage goals by empowering frontline health workers to enable early stages of diagnostic like Malaria or Cervical Cancer detection or to identify population at risk of developing non-communicable diseases like detecting diabetes/cardiac-risk from the iris, etc. Currently much of this work is done manually, limiting the frequency and scale of coverage. AI can be used also during health and natural disaster emergencies that can significantly increase the efficiency of disaster response and save more lives.

Aim of the track Potential domains for AI quick-wins for Public Health (workstreams)




Track 4: Trust in AI
Team Lead: Huw Price, Francesca Rossi, Zoubin Ghahramani, Claire Craig
Members: Stephen Cave, Kanta Dihal, Adrian Weller, Seán Ó hÉigeartaigh, Jess Whittlestone, Charlotte Stix, Susan Gowans, Jessica Montgomery
Theme Managers: Ezinne Nwankwo, Yang Liu, Jess Montgomery

Meet the team: ​trustfactory.ai​

Track Overview
Artificial intelligence (AI) has the potential to dramatically accelerate the pace at which the United Nations’ Sustainable Development Goals (SDGs) can be achieved. Maximising AI’s potential for good will strongly depend on building trust in AI, in several dimensions. This track will focus on three dimensions of trust. Developers of AI solutions must earn the trust of communities to which such solutions are offered. AI developers and others working for beneficial AI must trust each other, across cultural, national and corporate boundaries. And AI systems themselves must be demonstrably trustworthy.

This track will explore these three dimensions of trust under three Themes:
We will present three projects under each Theme. Participants will be invited to refine the projects, to propose potential research, impact, and funding partners, and to join projects as collaborators.

Theme A: Building trust for beneficial AI – stakeholder communities Theme B: Building trust for beneficial AI – developer communities Theme C: Building trust for beneficial AI – trustworthy systems