Incorporating AI and Machine Learning into the Social Sector

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Posted by: Kultar Singh
Category: AI and ML
Incorporating AI and Machine Learning into the Social Sector

In computer science, artificial intelligence (AI) is the development of intelligent machines that mimic the capabilities of humans and perform tasks that would typically require human cooperation.

Machine learning (ML) is a sub-area of artificial intelligence research (AI). It focuses on the capability of computers to accept data and learn independently without being programmed with rules. With ML, you may teach your program through examples rather than a set of instructions. ML employs learning algorithms instead of programming instructions or rules that are altered and improved.

This blog will explore social issues and problems using AI, machine learning, and statistical learning. Let’s take a closer look at how these technologies can enhance the capabilities of the social sector and improve efficiency while reducing costs.

Use Cases For AI & ML Mapped to Social Impact

Machine learning employs many techniques, including Bayesian modeling and elementary regression models. In addition, advanced machine learning techniques, including neural networks, support vector machines, decision trees, ensemble models, and model averaging, can be used for social good.

AI can’t solve all of the world’s most complicated social and environmental problems, but it may help solve some urgent issues and improve things. Collaboration is the key. Several businesses and tech companies have begun partnering with non-profit groups to address social, economic, and environmental concerns.

Though it is nascent, there is existing collaboration on the projects that use AI to increase social diversity, equality, inclusion, and sustainability.

There are several emerging fields wherein AI and ML can make a qualitative difference in the lives of the poor.

Research and Evaluation

In research and evaluation, we can all benefit from learning how to use ML strategies to evaluate how well our datasets can be categorized, how to choose variables, how well our models are constructed, and how to improve them. Further, in impact evaluation, ML methodology simulates fairness using causal inference techniques.

Health

Healthcare is one area wherein AI and ML have been hugely deployed. Machine learning models can be customized for each illness, but they may be tailored to the data and often have greater predicted accuracy.

From increasing medication research and manufacturing to providing virtual medical assistance and improving diagnostics, AI and ML are used in numerous healthcare use cases.

Crisis Response

AI and ML may also be helpful in various emergencies, including search and rescue operations in the wake of natural and man-made catastrophes. A wide range of AI applications can be used in crisis response, from predictive modeling to set up a helpline to conducting search and rescue operations.

Climate change

By decoupling effects and resources, AI and ML may reduce environmental damage, including CO2 emission per unit of economic production.

AI in transportation may provide more precise traffic predictions and freight transportation optimization to estimate demand and shared mobility options.

It can assist in improving climate change projections to manage trash and pollutants that harm human and animal health and damage biodiversity.

Social justice, Inclusion, Diversity, and Sustainability

We all know that many of our problems are caused by prejudices wherein our past decisions are unfairly biased against certain groups, like people of a certain race, gender, or sexual orientation. One can use AI and ML models to look at and assess the prevalent biases and help find ways to improve inclusivity by tracking decisions.

Conversational AI

Conversational AI is a use case wherein one can use a conversational chatbot to bridge the communication gap. The community has made progress towards improving Conversational AI and how current technology may support good social projects from various perspectives that are particular to Conversational AI.

One example is Unicef’s #KidsTakeOver app, developed to enable kids worldwide to engage in positions spanning media, sports, entertainment, politics, etc. It went live last year across 11 nations and in 5 languages. From health information to educational learning for kids, conversational AI is now being used to provide information in vernacular languages.

The Bottom Line

For AI and ML systems and models to help us achieve these objectives, addressing the obstacles and challenges currently facing them in the real world is imperative for building on learned knowledge and further improving them.

Kultar Singh – Chief Executive Officer, Sambodhi



Author: Kultar Singh

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