- The 17 UN SDGs
- the interconnectedness of the sdgs
- The need for technology
- Using AI Innovation to Achieve the SDGs
- Environmental Protection
- AI Energy Solutions
- Addressing Health Issues
- How to Improve Agriculture and Fight Hunger
- How to Improve Infrastructure Management
- Increasing Equality and AI Emotions
- Bringing AI Solutions to Life
During the past few years, we keep coming across headlines that alert us how Artificial Intelligence (AI) will take over our jobs and that robots will rule the world. However, in the sea of this dystopian information, we can also encounter the good impact of AI, helping us make the world a better place. In the global attempts of achieving sustainability, and more notably, meeting the UN Sustainable Development Goals (SDGs), AI can become a powerful weapon towards a circular economy and more sustainable living.
In fact, the McKinsey Global Institute has collected about 160 cases of AI’s real or potential uses that could benefit society non-commercially, with technologies like natural language processing (NLP), deep learning, computer vision, machine learning, and others, making a significant difference.
They propose that current capabilities could help solve cases across all 17 of the UN SDGs, eventually benefiting hundreds of millions of people in both advanced and developing countries. Helping blind people navigate their environment, identifying victims of sexual exploitation online, and supporting disaster relief efforts, such as the flooding that followed Hurricane Harvey in 2017, are just some examples where embracing new technologies and innovation can help. Moreover, AI-powered wearables can help detect the early stages of diabetes and help detect skin cancer through image scanning.
the 17 UN SDGs
The SDGs cover 17 integrated priorities that identify global quantifiable targets through social, economic, and environmental factors of development. They are meant to be a universal collection of metrics and a reference structure to be utilized by the international society in order to motivate initiatives and adoption by 2030.
In 2015, the United Nations set these targets as a follow-up to the Millennium Development Goals, which established a basis for a collective collaboration to eradicate extreme poverty. A long-term shift towards more sustainable growth is facilitated by this new paradigm. It encourages transparency while facilitating global cooperation at the same time. It represents a method to direct decision-making, but not a normative and actionable guide by itself. As a result, according to their own background, capabilities, and accessible research data, Member States and organizations are free to implement strategies and laws.
the interconnectedness of the sdgs
The main characteristic of the 17 global goals is their interconnectedness. It is likely that the rise in poverty will lead to a decline in sanitation habits and an increase in health issues. Furthermore, with poverty, access to schooling is more difficult and it will be easier for violence to occur. Another example is that climate change is affecting biodiversity and would worsen current inequality between individuals. That said, any commitment to improving one of these goals may have a positive impact on another, or even several others.
Just like the human body, the parts of sustainability are connected and can’t function one without the other. The economy impacts the state of society, which affects the way we take care of our planet. The point of the goals is to prove that the economy can’t advance without impacting society, which strongly depends on the environment.
The need for technology
The 169 targets within the 17 goals, however, aren’t enough to create a long-lasting impact. They have been set to inspire global leaders and governments to come up with concrete actions that will create positive, long-term changes. The 17 global partnership goal reflects this, requiring cooperation between stakeholders to achieve better results and make data exchange easier.
A significant amount of expertise, ideas, and innovative technologies are produced on a daily basis from a group of scholars, experts, entrepreneurs, and technologists, in order to achieve the SDGs. However, a lot has to be done to obtain facts and expertise to make this knowledge and information actionable.
This is where technology comes in. New technologies like AI, machine learning, big data, computer vision, and others, can help stakeholders get more concrete guidance on how to act towards achieving the SDGs.
As one of the most emerging technologies, AI will have a great impact on our future actions. According to Grand View Research, the global AI market was estimated at $39.9 billion in 2019 and is projected to grow at a compound annual growth rate (CAGR) of 42.2% from 2020 to 2027. This rapid growth means that the technology will enter many segments of how the world works, including achieving sustainability.
using ai innovation to achieve the sdgs
Today's age of digital technology drives remarkable advancements at phenomenal speed. In areas such as healthcare, agriculture, schooling, and transport, AI would enable humans to leverage vast quantities of data and make groundbreaking advances. We are also seeing how AI-enhanced computing can enable physicians to eliminate medical errors, increase yields for growers, tailor training for students, and unlocate researchers.
AI and climate change has been an area that has been gaining a lot of traction in recent years. For example, AI can be used to manage environmental changes and impacts in many different economic fields and conditions. Renewable distributed electricity grids integrated with AI, safer supply chains, environmental control and regulation, and weather condition forecasting, are only some of the possible applications.
According to research by PwC UK and Microsoft, by 2030, the use of AI for environmental technologies could add up to USD $5.2 trillion to the world economy, an improvement of 4.4% compared to business as usual.
In addition, the use of AI levers could decrease global greenhouse gas (GHG) emissions by 4% in 2030, an amount equal to 2.4 Gt CO2 equivalent to Australia, Canada and Japan's total annual emissions in 2030. As part of this change, while improving competitiveness, AI could generate 38.2 million new jobs across the global economy, creating more openings for skilled professionals.
Here are just a few of the many applications of AI in the world’s journey to achieving the 17 UN SDGs:
The 14th and 15th SDG goals refer to the protection of life in water and on land. In these cases, AI-powered technologies can be used to create automated processes that collect remote sensing data about biodiversity. This data can help stakeholders identify patterns in the species behavior and act accordingly when they notice something unusual.
Therefore, AI-based technologies can play a meaningful role in environmental protection. The management of protected areas can be made more effective with a clearer understanding of the specific behavior patterns. This can be especially useful for the protection of water and its fragile habitats. But, there has to be controlled and transparent use of technologies. Otherwise, AI can also be applied to environmental exploitation, which we certainly don’t want.
AI Energy Solutions
There is a strong opportunity for AI to reduce energy consumption and encourage renewable and affordable energy. For example, smart power grids have the power to coordinate the demand for electricity against multiple sources of renewable energy. However, we should be careful here too. As digital technologies are rising, so is the demand for ICT solutions. The growing production of ICT solutions, therefore, increases the electricity usage, which goes against the established goals.
Addressing Health Issues
The McKinsey report mentions a disease identification AI system that was developed by researchers at the University of Heidelberg and Stanford University, using visual assessment of natural images, such as images of skin conditions, to determine whether they are cancerous. The AI-based identification system was found to outperform professional dermatologists.
Another example of using AI for health is wearable devices that have the ability to detect early symptoms of diabetes using heart rate sensor data. These devices have the potential to help over 400 million people affected by diabetes worldwide if they are to be made affordable.
Human errors are also a major issue when it comes to healthcare. Incomplete medical records and massive patient loads can lead to human mistakes that are fatal. AI, immune to these causes, can anticipate and detect illness at a higher pace than most medical practitioners. As an example, an AI model using algorithms and deep learning detected breast cancer at a higher rate than 11 pathologists in one study.
Drug development is another aspect of healthcare that can be addressed by AI. In 2007, one of the greatest AI breakthroughs in drug production came when researchers delegated yeast testing roles to a robot called Adam. In public libraries, Adam searched massive amounts of data to hypothesize the roles of 19 genes inside yeast, forecasting 9 new and precise hypotheses. Adam's robot mate, Eve, discovered that triclosan, a natural ingredient found in toothpaste, can kill malaria-based parasites.
How to Improve Agriculture and Fight Hunger
According to the United Nations, in order to feed the population of the world by 2050, we would need to raise the world's food supply by 70%. Since time is important, AI programs can be deployed to speed up the exploration of potential solutions. The aim is for our food production to become smarter and more cost-effective.
An incredible number of the world's food, up to half, ends up in the garbage today. The TOMRA Sorting Solutions machine, which is operated by AI, analyzes and can divide food into "good" and "bad." For example, if the algorithm has to analyze a tomato, it could decide whether it’s suitable for a salad or not. If the fruit isn’t good enough for a salad, it could be useful for a tomato juice instead.
Big data and machine learning are also used in the Nutrition Early Warning System (NEWS) to classify places at heightened risk of food shortages, droughts, and floods, rising food costs, and soil degradation. It has already been deployed in Colombia to alert producers that there was a flood on the horizon and recommend that they miss the planting season. When the drought happened, the 170 farmers who obeyed the advice and missed the planting season saved a lot of planting costs.
Other use cases involve the integration of different forms of alternate sources of data, such as geospatial data, social media data, telecommunications data, internet search data, and vaccine data to better forecast patterns of transmission of viruses and diseases, or the use of an AI approach to optimize food delivery networks in areas facing natural catastrophes.
How to Improve Infrastructure Management
Improvements related to infrastructure could deliver public goods in the areas of electricity, water and waste control, transport, real estate, and urban planning. In order to increase vehicle throughput, for instance, traffic light systems can be configured using real-time traffic camera data and Internet of Things sensors. In order to recognize presumably malfunctioning elements, AI could also be used for condition monitoring of public transport systems such as railways and public infrastructure, including bridges.
Increasing equality and AI emotions
Addressing challenges of equality, diversity, and self-determination, such as reducing or suppressing stigma based on race, sexual identity, ethnicity, religion, and disabilities, are crucial to creating a better society for all world citizens.
In its report, McKinsey mentions Affectiva, a spin-out of the MIT Media Lab, and Autism Glass, a Stanford research project, whose work leverages AI in automating the detection of emotions to provide social signals to help people communicate in social settings along the autism spectrum. Another example is the introduction of an alternate method of identity authentication, such as driver's licenses, for persons without conventional means of ID.
Bringing AI solutions to life
Governments, NGOs, charities, technology firms, and organizations that gather or produce large volumes of data will have to take some concrete actions to achieve more significant advancements. Regarding the use of AI in achieving the UN SDGs, two important obstacles appear:
1. The problem with access to valuable information.
2. The lack of AI-skilled talents to develop, improve, and implement algorithms and machines.
What is more, the implementation of AI will bring a lot of risks. Authorities and other stakeholders can misuse their instruments and techniques or be exposed to unintended misuse, often damaging the same individuals they are supposed to help.
Here at Valuer, we believe in the importance of using a data-driven approach when trying to tackle innovation and sustainability head-on. It is definitely no easy approach, but with Valuer's AI algorithms, we can help you explore the vast ecosystem of business models and opportunities that were previously off your radar.
For all of humanity's challenges, AI is not a magical cure. But within the variety of its uses, it has the capacity to be a powerful tool. In order to use the benefits, we need to facilitate the production of applications, but also drive them to be used responsibly and thoughtfully at every level.
“At its essence, sustainability means ensuring prosperity and environmental protection without compromising the ability of future generations to meet their needs. A sustainable world is one where people can escape poverty and enjoy decent work without harming the earth’s essential ecosystems and resources; where people can stay healthy and get the food and water they need; where everyone can access clean energy that doesn’t contribute to climate change; where women and girls are afforded equal rights and equal opportunities.” - Former UN Secretary-General Ban-Ki Moon