Today machine learning (ML) and artificial intelligence (AI) are often discussed in terms of business improvement and product innovation. But, these technologies can also address pressing global issues like climate change, healthcare disparities, and resource constraints. This approach, known as ‘machine learning for social good,’ uses AI’s predictive power, data analysis, and pattern recognition to tackle these challenges.
In this blog, we will discuss how machine learning for social good is a practical reality, with its implementations in critical domains such as healthcare, environmental conservation, and humanitarian aid.
What Is ML And How To Use Machine Learning For Social Good?
Machine learning is a subset of artificial intelligence where machine learning algorithms are trained to identify patterns from huge datasets and make predictions based on the data. With the massive amounts of data that we have today, the ability to utilize machine learning for analysis and predictions can solve a lot of societal issues.
Machine learning can be a tool used to fulfill the United Nations’ Sustainable Development Goals. Machine learning can use huge datasets related to health, disaster relief, and climate change to help make critical observations and insights. This can help organizations be better prepared and design more effective solutions.
Key Areas Where Machine Learning is Making an Impact
Here are some key areas and industries where machine learning can make an immense impact.
1. Machine Learning for Social Good in Healthcare
Machine learning can have the biggest impact on healthcare, especially when it comes to early diagnosis and highly personalized treatment plans. DeepMind by Google is a great example of using machine learning for social good. It can analyze retinal scans and diagnose up to 50 different forms of diseases in the eye. This helps doctors to cut short on time spent for diagnosis and potentially save the vision from getting lost for millions.
An even more stunning example is the AI system detecting diabetic retinopathy, a leading cause of blindness, using medical images. Several countries have already begun using this system, bringing eye care to areas where there are no specialists available.
Also read: AI in Healthcare: Examples, Benefits, and Challenges
2. Climate Change and Environment
Source: ITU
Machine learning has an important role to play in tackling climate change. AI models can estimate deforestation, keep track of animal population, or optimize renewable energy sources. Accodring to Google DeepMind, the use of AI technology to optimize wind power has increased energy output prediction accuracy by 20%. By enhancing our ability to forecast wind energy, we can make the best use of renewable energy in grid operations.
Another example of using machine learning for social good is the Rainforest Connection. Rainforest Connection uses AI-powered sound sensors to continuously monitor sounds in the forests to detect any chainsaw sounds in real time. This helps the forest rangers to immediately act upon any illegal logging in the forest.
3. Disaster Response
Natural disasters often lead to huge losses of property and human lives. Natural disasters are inevitable but we can proactively detect them and minimize the loss. With machine learning, disaster response can be improved significantly.
Vast datasets can be used to predict the movement and course of natural disasters like hurricanes, cyclones, and wildfires, with machine learning models. This can significantly improve how we react and plan for disasters. For instance, drones equipped with AI were used to survey damaged areas and send off real-time imagery during a disaster like those wildfires scorching through Australia.
Another major feather in the cap for Google is its Flood Forecasting Initiative. In India and Bangladesh, this AI system is used to predict floods and enable early warnings. Such initiatives can save lives and minimize property damage substantially.
Real-World Examples of Machine Learning for Social Good
ML is changing the world & it can solve some of the critical global problems with tangible real-world applications. Here are some of the prominent fields where machine learning is applied in an impressive way towards social good.
1. Education: Helping Every Student
Source: Carnegie Learning
When it comes to education, machine learning is not just assisting mainstream students. It is also making education available to those who are disabled and face difficulty in getting a traditional style of teaching.
A good example of this is the Carnegie Learning MATHia Platform. Designed to serve students with math learning disabilities, also called dyscalculia. MATHia is powered by machine learning algorithms that allow it to provide a personalized study plan for each student.
The system uses reinforcement learning to adapt how it teaches students depending on the learner’s performance and speed. MATHia continuously calibrates itself by tracking how students respond to new tasks and what problem-solving strategies they use. This data is used to suggest alternative explanations or provide more practice on a related task.
This technology has shown to be especially useful with students who struggle in mathematics because of dyscalculia or other learning challenges.
Likewise, Curipod is an AI-driven educational platform that uses Natural Language Processing (NLP) to assist teachers in guiding customized lesson plans among diverse learning profiles. Based on student feedback and engagement, it can recommend changes in the curriculum, making learning accessible to every student.
2. Wildlife Conservation: Combating Poaching with AI
Thanks to machine learning, wildlife conservation is seeing significant changes with improvements in tracking and protecting vulnerable species. Scientists at USC created PAWS (Protection Assistant for Wildlife Security) that utilizes predictive modeling to estimate probable poaching places based on historical data.
Using game theory with machine learning, PAWS suggests the best patrol routes that rangers should follow for surveillance patrols in conservation areas. Data input into the model involves information on past poaching behavior, geography, and animal activity.
For example, in the case of pervasive poaching issues amongst elephants, spatial data becomes very useful in predicting where and when poachers are going to target. As more data is collected over time with ranger patrols and sensors, it can predict poaching risks with greater accuracy. PAWS pilot programs in Africa and Southeast Asia have significantly enhanced patrols by helping rangers detect illegal activities that would have gone unnoticed.
Similarly, Global Fishing Watch, a joint venture between Oceana, Google, and SkyTruth uses satellite technologies and machine learning to track illegal fishing worldwide. With the mapping out of water bodies using satellite data along with tracking fish movements, this system can identify ships illegally involved in fishing activity.
3. Agriculture: Precision Farming for Food Security
Food security is a huge concern in many countries and this can be solved with machine learning. Smallholder farmers are using machine learning to increase resource efficiency, crop yield, and food security in agriculture.
The Easiest and most glaring example is the Omdena Crop Yield Prediction Tool for Farmers in Senegal. The tool is built by using Satellite Images from Google Earth Engine (GEE) and Convolution Neural Networks (CNNs) to predict the crop yield based on the weather, soil type, etc.
Here, CNNs are especially utilized for pixel-level examination in satellite images investigating the pressure of crops due to water starvation or disease. Using historical and real-time satellite data, processed in Google Earth Engine (another application by the same team), this tool creates precise predictions of future crop yields. The tools predict when the planting, irrigation, and harvesting will most likely deliver maximum productivity.
For example, John Deere uses convolutional deep learning models to develop autonomous tractors capable of plowing and planting crops on their own with little human involvement. These devices run on computer vision and sensor fusion techniques to study soil health which helps agriculture processes in real time.
Also read: ML in Agriculture: Revolutionizing Farming Practices
4. Earthquake Detection: ConvNetQuake
In an attempt to tackle disaster response, Thibaut Perol and collaborators developed ConvNetQuake, a method for almost instantaneous earthquake detection from seismograms using convolutional neural networks (CNNs). The neural network can distinguish ground shaking caused by real earthquakes from background noise, offering the potential to provide vital early warnings​.
ConvNetQuake processes a large amount of data from seismographs, pinpointing patterns a regular monitoring system wouldn’t be able to. In tests, ConvNetQuake was able to identify more than 17 times as many earthquakes compared with existing detectors within Oklahoma, which is home to low-level seismic activity. This depth of insight and real-time data can be benefited by emergency response teams, where they can use them for quick response in times of crisis.
5. Mental Health: AI-powered Chatbots for Therapy
Mental health is another area where machine learning can really improve things. A key evolution is employing artificial intelligence (AI) chatbots, such as Woebot, an AI conversational agent modeled on CBT that delivers mental health support through Facebook Messenger.
Woebot leverages natural language processing (NLP) and sentiment analysis to gauge how users are feeling at any given moment so that they can serve evidence-based therapeutic solutions.
Woebot uses conversational data to train a Recurrent Neural Network (RNN) so that it can detect patterns in users’ responses and offer personalized support. With the integration of emotional AI, it can identify even a slight change in users’ moods and treat them accordingly. This is the sort of AI-driven therapy that can be truly valuable for people who do not have access to traditional mental health services.
Also read: ML in Healthcare: Transforming Diagnostics and Treatment
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FAQs: Machine Learning For Social Good
1. Is it Possible to Use Machine Learning for Social Good?
Yes, absolutely. Machine learning is an incredible “tool†that be used to predict events or derive solutions for societal problems like disaster management, healthcare, and food security.
2. How can one use Machine Learning for Social Good in Healthcare?
In healthcare, machine learning can help achieve sophisticated diagnostics, and optimize treatment plans. Most importantly, it can predict disease outbreaks using historical data. The possibilities are endless.
3. Will Machine Learning be used to Tackle Climate Change?
Machine learning is already being used to optimize the energy output from renewable energy sources, predict deforestation, and manage environmental data to minimize the impact of natural disasters.
4. Are there some Real Life Examples of Applications of Machine Learning for Social Good?
Scientists at USC created PAWS (Protection Assistant for Wildlife Security) that utilizes predictive modeling to estimate probable poaching places based on historical data. Another example of this is the Carnegie Learning MATHia Platform, which is designed to teach students with math learning disabilities, also called dyscalculia.
5. Why is Collaboration so Crucial to Machine Learning for Social Good?
Governments and NGOs working together with companies in the tech sector will be key to scaling machine learning across regions so that AI innovations can positively affect marginalized populations around the world.
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