Artificial Intelligence (AI) is expanding to meet global challenges and streamline global solutions. The market will grow by 18.8% in 2022 alone, reaching a $500 billion market value by 2024 (IDC 2021).
Organisations like the International Research Centre in Artificial Intelligence (IRCAI) are applying this new frontier of technology to sustainable development challenges.
They asked the question - how are developments in AI going to help us achieve regulatory milestones such as the UN's Sustainable Development Goals (SDGs)?
As part of this work, the IRCAI and UNESCO have created a list of 100 projects worldwide that use AI to support the achievement of SDGs.
In considering the ethical applications of AI, the projects are classified by impact and implementation ability. Overall, they found that the use of AI in achieving these goals is 'thriving' (IRCAI 2021).
The range included in the top 100 list shows the growth and innovation in the AI space and the technologies' capacity to aid sustainable development.
NatureAlpha's science-based, AI and machine-leaning powered biodiversity analytics was one of ten in the world to be rated 'outstanding' by UNESCO and the IRCAI.
This article will discuss the future applications of AI in achieving SDGs by exploring five of the 'outstanding' projects.
NatureAlpha
Sustainable development goals addressed:
Goal 6: Clean Water and Sanitation
Goal 13: Climate Action
Goal 14: Life Below Water
Goal 15: Life on Land
Goal 17: Partnerships to Achieve the Goal
NatureAlpha's analytics platform provides biodiversity data at scale for asset managers.
The problem:
NatureAlpha's solution directly addresses five goals. However, the decline in biodiversity threatens over 80% of SDG targets which could have widespread systematic and financial implications (UN 2019).
The World Economic Forum (WEF) ranks natural degradation as one of the top five global risks by likelihood and impact (WEF 2021).
Moreover, $44 trillion of economic value generation (over half of the world's GDP) is moderately or highly dependent on nature (WEF 2021).
Fundamentally, biodiversity loss is a significant threat to global financial and systematic stability.
However, there is no credible biodiversity data at scale to inform investment decisions.
This lack of data means that over $100 trillion (BCG 2021) of global assets are managed without reliable biodiversity risk and impact information.
The solution:
NatureAlpha's solution allows asset managers to assess the risk of a decision more accurately by factoring in one of the top five biggest upcoming threats - biodiversity loss (WEF 2021).
It also gives investors the ability to positively impact nature with their choices by providing them with nature-footprint information for the first time.
On the AI:
NatureAlpha's data is powered by the combination of a unique R&D partnership with Oxford University and leading geospatial and machine learning technology.
The integration of AI gives the platform a competitive advantage over non-AI based metrics due to the volume of data analysis possible.
NatureAlpha delivers user-friendly analytics via API or platform to global financial institutions.
Get in touch to learn more about how you can factor biodiversity into your investment decision making.
Fair Forward (German Development Cooperation Initiative)
SDGs addressed:
Goal 3: Good Health and Wellbeing
Goal 5: Gender Equality
Goal 7: Affordable and Clean Energy
Goal 9: Industry, Innovation and Infrastructure
Goal 10: Reduced Inequality
Goal 13: Climate Action
Goal 17: Partnerships to Achieve the Goal
The problem:
Despite the boom in AI, developing and emerging economies are at risk of being left behind.
For example, only 31 out of 54 African countries had data protection legislation in 2020 (the United States International Trade Commission 2021). Moreover, only a few developing countries (such as India and Kenya) have launched strategies to support the use of AI (GDCI 2021).
The solution:
The project is working with six partner countries (Ghana, Rwanda, Kenya, South Africa, Uganda and India) to achieve a more inclusive approach to AI at an international level.
It aims to strengthen local technical knowledge, build open AI training sets and develop policy frameworks to support AI use.
On the AI:
The first phase of this project involves conversational AI systems in local languages. It will use machine learning and natural language processing (NLP) techniques to achieve this.
NASA harvest - NASA & the University of Maryland
SDGs addressed:
Goal 1: No poverty
Goal 2: Zero Hunger
Goal 10: Reduced Inequality
Goal 13: Climate Action
Goal 15: Life on Land
Goal 17: Partnerships to Achieve the Goal
The problem:
According to the UN, between 720 and 811 million people faced hunger in 2020 worldwide (FAO 2021).
Moreover, there are limitations on the availability and accuracy of global crop monitoring data.
The solution:
NASA Harvest uses satellite earth observations to provide more timely and accurate data on global crops.
This technology can help warn of early crop failures and production shortfalls to mobilise industry, government or NGO action and prevent shortages.
The project also includes extensive capacity-building to help developing countries integrate this data with their existing systems.
On the AI:
NASA Harvest combines machine learning and earth observation data.
Flag by Rewire Online
SDGs addressed:
Goal 5: Gender Equality
Goal 9: Industry, Innovation and Infrastructure
Goal 16: Peace and Justice Strong Institutions
The problem:
With the rise in social media, there has been an increasing amount of 'hate speech' online.
The solution:
Flag is a tool for online safety. It automatically detects whether a piece of content contains hateful language and can categorise messages.
On the AI:
Flag uses a unique human-and-model-in-the-loop approach to training.
Rewire Online's process includes NLP with the addition of iterative-human driven testing.
The method allows the software to rapidly learn and fix holes in its categorisation process. This ability allows it to adjust to new forms of online hate speech swiftly.
INPS (Social Security Administration of the Republic of Italy) & Accenture
SDGs addressed:
Goal 8: Decent Work and Economic Growth
Goal 9: Industry, innovation and infrastructure
The problem:
The Italian Public Administration currently classifies citizen emails manually. This inefficient system delays responses to citizens' concerns and requests.
The solution:
The INPS and Accenture have created an AI classification system to efficiently and automatically manage email responses to citizens.
On the AI:
The project uses Deep Learning' Transformers', a method that expands the scope of NLP capabilities. The technology can even analyse thread and embedded attachments and unstructured emails.
Other projects
The other five projects rated 'outstanding' by the IRCAI include:
Logically Intelligence by Logically
MedCheX by National Cheng Kung University
Novel Application of Advanced Manufacturing Approaches to High-Quality Protein by Aspire Food Group & Darwin AI
SkillLab by SkillLab B.V.
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