Byte by Byte: How AI is Revolutionising Poverty Alleviation Efforts
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Recent public discourse surrounding Artificial Intelligence (AI) has been punctuated by a cacophony of conflicting opinions and shocking stories. The news is brimming with examples of AI algorithms perpetuating discrimination, causing privacy concerns, deepfakes, cybersecurity risks and talk of job displacement to name a few. From policing models reinforcing systemic injustices to adversarial machine learning techniques being used to bypass traditional security measures, the negative impacts of AI are a very real concern. However, in the midst of the panic and uncertainty there is a lot of good this technology can do.
Eliminating poverty stands out as the most urgent global concern according to the United Nations, with more than 700 million individuals surviving on less than $2.15 daily. With its capacity to transform industries and streamline processes, AI is now being leveraged as a powerful tool in this fight against global poverty, proving to be instrumental in understanding and tackling its root causes.
One of the fundamental aspects of poverty alleviation is access to basic necessities such as electricity, clean water, education, and healthcare. In many developing countries, lack of access to electricity hinders economic growth and limits opportunities for advancement. However, AI driven solutions are revolutionising the way electricity is generated and distributed. By utilising data on wind patterns and solar radiation levels, AI can identify optimal locations for renewable energy sources such as wind turbines and solar panels. This not only provides communities with electricity, but also opens doors to economic and educational opportunities.
In regions where clean water is scarce, AI technology can be instrumental in detecting sources of water contamination and developing solutions to remove contaminants. By leveraging satellite imagery and machine learning algorithms, it can pinpoint areas in need of water infrastructure improvements, such as wells and pumps, thereby increasing access to safe drinking water. Governments and organisations can invest in AI-driven water infrastructure projects to ensure sustainable access to clean water for impoverished communities, improving health outcomes and economic opportunities.
Research indicates that allocating resources to agriculture can be up to four times more impactful in reducing poverty compared to investments in alternative economic sectors. For instance, in India AI has reduced farming expenditures and enhanced agricultural practices. AI therefore facilitates increased food production at a reduced cost to support poorer communities. AI-driven technologies such as computer vision and robotics paired with the analysis of satellite-collected data and the application of predictive analytics, enables farmers to enhance crop yields and efficiently utilise resources. Offering valuable insights into farming techniques such as irrigation and fertiliser application, this technology has the power to increase productivity. Projects like FarmView, developed by AI specialists at Carnegie Mellon University, use robotics and machine learning to optimise crop growth, particularly in staple food crops like sorghum. This technology can also aid in disaster management by predicting and mitigating the impact of natural disasters on water supplies and infrastructure, thereby safeguarding vulnerable populations from the adverse effects of climate change.
Improving access to education is paramount for poverty reduction, yet many regions lack resources and qualified teachers. AI-enabled virtual learning systems can bridge this gap, providing students with access to educational resources and skills necessary for employment. Additionally, AI tutoring systems could provide personalised learning experiences tailored to students' individual needs. These systems use machine learning algorithms to adapt to students' progress and provide targeted support. AI-powered analytics tools can analyse large datasets to identify trends in educational practices, both developing targeted interventions as well as providing personalised feedback to students. In areas where language barriers are an issue, there are natural language processing algorithms able to provide real-time translation, language instruction, and language assessment services.
Lastly, by assisting doctors in diagnosing and treating illnesses, AI is improving healthcare access. Remote patient monitoring alongside AI driven diagnosis tools such as medical imaging algorithms, are capable of enhancing diagnostic accuracy and efficiency. For instance, AI algorithms can analyse medical images like X-rays or MRIs to assist healthcare providers in making more timely diagnoses. This is crucial in regions where access to skilled radiologists or specialists is limited. By analysing patient information, including electronic health records and wearable device data, these algorithms can identify patterns and risk factors associated with various diseases, allowing healthcare providers to intervene earlier.
Ultimately, AI holds promise in contributing significantly to reducing poverty. Nonetheless, it is imperative to recognise that AI is not without its limitations and inherent biases. To harness the potential benefits of AI for poverty alleviation, it is essential to establish appropriate policies, regulations, and governance mechanisms. Through diligent oversight and thoughtful implementation, AI can serve as a valuable asset in the ongoing efforts to alleviate poverty worldwide.