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AI Technology Helps NGOs Provide Service to the World with Great Efficiency

In the past few years, artificial intelligence (AI) technology has gradually changed from an emerging technology used in various kinds of products and services to being utilized in various aspects of life. Given that AI technology is involved in big data training, sometimes people relate it to personal information exposure, thus people have different levels of acceptance regarding AI technology. However, it is hard to overlook the convenience and efficiency it brings about. Not only in common business behaviors, but also NGOs have also clearly benefitted from the development of AI technology.
Not long ago, Matter of Form, a well-known strategic marketing firm from London, pointed out the effects of AI technology in NGOs and also provided this explanation on commonly existing controversies.
Image Classification and Biometric Technology Helps Mountain Rescue by Reducing the Search Time by 50%
Not many people have noticed that application of AI technology in rescue actions, where every second counts, can highly enhance efficiency. Traditional rescue actions relies almost completely on manpower regarding identification, strategic stipulating, and action implementation. On the one hand, the success rate can only rely on the experience of decision makers, while on the other hand, its progress is slow. However, when we apply air camera drones installed with image classification and biometric technology at rescue sites, we can rapidly and accurately navigate through complicated landscapes. For example, after an avalanche accident, the mountain rescue organization responsible for that rescue pointed out that they used drones in that action and reduced searching time by 50%.
Machine Learning Technology Helped to Search for Noises Made by Illegal Logging and Prevented Carbon Dioxide Emissions by 6.5 Million Tons
Rainforest Connection, a start-up company for environment protection, has used microphones collected from old smart phones to record voices in various parts of the rain forest. Then the classic machine learning frame of Tensorflow was used to distinguish between the sounds of electronic saws and wild animals. Every time it recorded the noises of electronic saws, it automatically sends warning signals to rain forest protection authorities, enabling enforcers to stop illegal logging actions at once. According to data provided by Rainforest Connection, this model has monitored 26,000 hectares of forest, collected 4,629 days of data, and successfully prevented 6.5 million tons of carbon dioxide emissions due to the decrease of rain forest area, which is equivalent to the emissions of 1.3 million cars.
This was just one successful application of AI technology in the field of ecosystems. The co-founder of Rainforest Connection Topher White, who has an engineering background, possesses infinite imagination on combining AI with biological research. “Ecology can be observed from the perspective of big data. For example, we can analyze how the voices of birds change when a jaguar walks by.”
On a similar case, technological powerhouse Microsoft has collaborated with big data company Gramener in recent years. They have invested US$40 million to establish an NGO team with the purpose of researching the various possibilities of big data application for NGOs. For instance, they have traced the overall animal migration model, which included thousands of animals, and also the behaviors of individual animals. With this data, they seek to help animal conservation groups to understand how organisms are affected or even forced into extinction by human beings with better accuracy and efficiency in order to stipulate protection strategies.
Deciding Who Should Receive Resources with the Precise Calculations of AI? Moral Controversies and Limitations on the Application of AI
In an ideal situation, AI could accurately make decisions and maximize interests through a series of precise calculations when faced with dilemmic issues, such as “Who should we save first?” “Who is most suitable for distributing resources?” “Where should we relocate disaster-stricken residents?” However, up to today, we still dare not to do so in real life. The reason for this is that data used to train AI is biased and decisions made by AI will definitely include the blind spot or bias of its creator. For example, a technology company once attempted to find the most appropriate qualified personnel with AI technology and this attempt was declared a failure recently. The AI tool it used showed a systematic bias against women. This tool continued to make biased decisions, because they used past hiring records to train this AI tool. These past records only presented the personal perspectives of past human resource staff and not objective facts. The company soon learned its lesson.
The Next Step of NGOs
Although currently there remains many limitation on AI technology, it will definitely become more mature and its scope of usage will expand as time progresses. NGOs can ponder on how to further utilize AI technology to achieve more things from this perspective, instead of focusing on its deficits.
For instance, WeRobotics has noticed that developing countries have been excluded from AI development. Therefore, it installed incubators locally. In addition to using AI capacity to solve local aid and public health issues, they also provide AI services to local enterprises with the hope of establishing a local AI business system. This is what NGOs can do to promote the growth of the overall indusial ecosystem and a win-win-win sustainable model that increases employment opportunities, investment targets, and solves local issues.
NGOs Should Join the Discussion of AI Technology More Aggressively
AI technology is not only utilized by the field of technology. All fields should have the right to use it. However, NGOs are commonly absent in the current debates on AI related issues, indicating that NGOs have a common vigilance of AI technology. This may be mainly caused by their distrust of suggestions made by AI and their concerns on how AI may take over the NGO, especially when there were so many past cases of AI making wrong decisions.
However, because AI is created by humans, we can train it into the form we wish to utilize. It can reduce costs, enhance assistance quality, and benefit more people. If NGO teams can aggressively join in the discussion of AI technology and allow ones who are familiar with rescue sites to bring up AI application questions, there lie possibilities for AI to provide better services.
Microsoft president Brad Smith said, “If we put our whole heart into thinking about how to respond social needs with technology, each of us can achieve more goals.”
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