Introduction of AI for Social Good
In AI for social good, Artificial intelligence has become an important aspect in addressing the challenges globally and gives positive change. Global issues have an impact on AI. Certainly, the technologies leverages and address challenges such as health, environment sustainability, education, and more. To achieve a positive impact by using AI in the enhancement of society by participation in the development and implementation of policies. To address the challenges such as social, humanitarian, and environmental challenges AI creates beneficial solutions to enhance society by improving the health of the public (Patel, 2024). It has a positive impact on society and well-being. The report provides an overview of AI for social good, roles, challenges, case studies, ethical considerations, and future trends of AI for social good.
Role of AI in Social Good
Economic empowerment
AI uses open access to economic resources. Also, provide opportunities for people and opportunities to develop skills.
Educational challenges
As there will be many challenges in improving productivity. The AI tools provides learning opportunities for the lecturers as well as students. Also, it gives opportunities for growth in the field of education.
Environmental challenges
As there are many challenges related to climate change, natural resources, pollution, and wildlife protection (Chui, et al., 2018). With the help of AI, it detects climate change, manages resources, and responds to disasters. Furthermore, the usage in the agricultural field to maintain secure food and manage wastage. to promote sustainability.
Healthcare
AI is helpful for any early detection of diseases and providing a treatment plan using the discovery method. Moreover, supports for remote patient monitoring (Roy & Sunder, 2024). It also suggests treatments with personalized medicine based on the disease and lifestyle factors.
AI in Healthcare
Poverty Alleviation
AI gives a promising response regarding global challenges regarding the management of finances and optimization of improved solutions. Also, it provides hope for low-income communities to find analysis regarding people affected due to poverty and enable job marketing strategies for more employment opportunities and enhance economic empowerment.
Humanitarian aid
Generally, AI logistics are useful in managing supply chain management and planning routes and timely deliveries. Therefore, it enhances the targeting reach of demographic data and organizes the delivery patterns (Shree & Patidar, 2020). Additionally, communication tools are used to gather information and translate languages.
Benefits of AI for Social Good
It assists in faster decision-making during the situation of a crisis. The algorithms provides real-time identification of emergencies and respond to the situations. Moreover, this helps in the effective management of patterns and waste management are maintained. In addition, this has the potential to spread and address the issues globally. It monitors health conditions and pattern analysis with advanced predictions. Also, it helps in scaling up and elimination of poverty in the rural areas.
It helps in a financial crisis where there are payments, credit payments, and banking (Hasas, Hakimi, Shahidzay, & Fazil, 2024). Moreover, it enables faster transaction processes which makes faster access to finances. It has enhanced prediction capability regarding historical analysis and managing weather patterns. Various situations are analyzed with cost-effective solutions whereas it saves time and money. Also, maintains its impact in supply chain, logistics, and human resource management. Also, enhances socio-economic development and entrepreneurship it gives protection rights to the human by making effective decisions.
AI for Social Good
Case Studies of AI for Social Good
Case Study 1: Access to Health Services
Companies such as Google Health and IBM Watson have developed AI-powered diagnostic tools. Hence, these are useful in detecting diseases quickly. The use of machine learning algorithms in image predictions, progression of disease, and recommended solutions were provided. For example, To detect the diabetes there is usage of AI Likewise from the images of retinol and earlier access to progression and help in the prevention of vision loss.
Case Study 2: Protection of the environment and maintainability of sustainability
AI is used in managing the conservation organization to protect the species. An example is mentioned that while book which is an AI and machine learning platform where it has recognized distinct animals when the images were shared and uploaded by scientists and various researchers. It helped in monitoring the movements of distinct species and contributed to the conservation of the forest.
Case Study 3: Disaster management and response
Certainly, the AI system has developed machine learning algorithms for the prediction of disaster management and recovery processes. For example, in a natural disaster the algorithms used through image satellites. The metadata for image recognition and predicting the areas affected and provide rescue operations in regarding the help. Hence, the virtual assistants use real-time information in AI and assist the communities where they are affected.
Case Study 4: Education
There are personalized AI platforms such as Duolingo and Khan Academy where they have provided different styles of learning for the students and professionals and personalized experiences and learning experiences were given.
Ethical challenges
Bias and fairness
Based on the training data it could provide discriminatory outcome consequently, it brings social inequalities might affect some communities.
Privacy and data security
As there is a huge amount of data, the use of sensitive and private information violates the privacy concerns of individuals. Therefore, data anonymization and protection measures should be taken to protect the safety and privacy of individuals.
Decision-making and autonomy
It is important to make decisions in healthcare and criminal justice. Consequently, this leads to an impact on human autonomy.
Corporate and multi-stakeholder engagement
The challenges need collaboration across various governments, stakeholders, technological companies, communities, and NGOs. Therefore, there should be partnership creation in collaboration and sharing of knowledge which successfully maintain AI for social good.
Future trends and recommendations of AI for Social Good
The ethical AI enhancement, where should we prioritize the considerations of the ethical process, which especially ensures transparency, accountability, and fairness. Furthermore, the guidelines need to be established, and it reduces the bias in the technology of AI. The AI technologies should be marginalized and communities should be seen. For creating AI solutions with various aspects of languages, socioeconomic cultures, and backgrounds, the solutions should provide the risk of eliminating inequalities. Also, there should be collaboration between the public and private sectors. Collaboration between non-profit organizations, policy government, and academia to enhance society. Additionally, making partnerships with multiple stakeholders enhances the resources and innovative solutions for social challenges (Singh, 2024). The local issues should be taken care of and empower the communities with AI literacy programs, development of skills, and strategies for addressing the community challenges.
Conclusion
Artificial intelligence is handled with large amounts of data and it gives a deeper understanding regarding various aspects and provide recommendations. With the help of predictive modeling and various innovative solutions, it gives an approach with enhancements. Furthermore, AI develops with all the ethical considerations without having any inequalities and biases and serves all people with equal effort. In conclusion, making collaboration with all the sectors creates advancements and enhancement in social responsibility. Despite being a tool, it has the most important aspect in solving the humanitarian problems and being sustainable.
References
Chui, M., Harrysson, M., Manyika, J., Roberts, R., Chung, R., Nel, P., & Heteren, A. v. (2018, Nov 28). Applying artificial intelligence for social good. Retrieved from McKinsey Global Institute: https://www.mckinsey.com/featured-insights/artificial-intelligence/applying-artificial-intelligence-for-social-good
Hasas, A., Hakimi, M., Shahidzay, A. K., & Fazil, A. W. (2024). AI for Social Good: Leveraging Artificial Intelligence for Community Development. Journal of Community Service and Society Empowerment, 2(2), 196-210. Retrieved from https://www.researchgate.net/publication/378043917_AI_for_Social_Good_Leveraging_Artificial_Intelligence_for_Community_Development
Patel, S. (2024). AI for Social Good: Addressing Global Challenges with Artificial Intelligence. International Journal of Holistic Management Perspectives, 5(5), 1-10. Retrieved from https://injmr.com/index.php/IJHMP/index
Roy, A., & Sunder, V. (2024, Feb 13). AI for Social Good: Revolutionising lives through operational excellence. Retrieved from Forbes: https://www.forbesindia.com/article/isbinsight/ai-for-social-good-revolutionising-lives-through-operational-excellence/91413/1
Shree, C., & Patidar, D. (2020). AI for Social Good: Addressing Global Challenges and Empowering Communities. Turkish Journal of Computer and Mathematics Education, 11(1), 1095-1099. Retrieved from https://turcomat.org/index.php/turkbilmat/article/download/14407/10424/26131
Singh, R. P. (2024, Apr 10). Artificial Intelligence for Social Good : Addressing Global Challenges. Retrieved from Linkedin: https://www.linkedin.com/pulse/artificial-intelligence-social-good-addressing-global-singh-cpfxc
Keywords
AI for Social Good, Poverty Alleviation, Privacy and data security, Artificial Intelligence
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AI for Social Good: Leveraging Artificial Intelligence for Community Development
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