Poverty is a worldwide concern that needs to be addressed on a large scale. With the enhancement of technology, new initiatives are taking place in order to curb the global poverty rate. With COVID-19, the poverty rate across the globe has taken a much larger hit. As per World Bank reports, during the first wave of COVID-19, the rate of poverty increased from 8.4% in 2019 to 9.4% by the end of 2020. The most horrifying situation is that in this data, almost 33% to 80% of the contributors are from India. Machine learning has emerged as an advanced technology in enhancing the productivity and overall accuracy of all the sectors into which it has been incorporated into. When we understand the non-traditional data including records and documents, machine learning has been a highly appreciated and reliable application for complementing the anti-poverty program initiated by governments across the globe. These records are packed with extremely important information such as communication and recharge patterns along with contacts networks and many more.
When we look at the ways to understand and evaluate the extremely poor areas, three major methods have to be highlighted. The first one involves developing a model backed by machine learning which is programmed on the data from the call detail records. Second, prepare an index that is based on the available assets. The third is to develop a consumption metric that is widely utilized as a medium in order to evaluate the poverty statistics in countries with low and middle-income groups. These algorithms are developed on the basis of 797 behavioral indicators which are adapted from the data received from the call detail records. These indicators have been developed and have inculcated patterns for better communication, dynamic contact building, and specific spatial as well as recharge patterns. This was developed with the assistance of a gradient boosting model which was equipped to provide an astonishing performance when compared with the existing applications of machine learning algorithms.
When we look at the aspect of the accuracy of these CDR methods in order to understand and evaluate the groups falling under the category of extremely poor, the method is directly compared with the two methods. These methods are consumption-based methods and asset-based methods. The CDR method was 42% which was considered highly promising when compared with the other two. The asset-based method had an accuracy of 49% whereas the consumption-based method was 45%.
The highly impractical way of collecting consumption data for large masses is now being solved with the help of machine learning. This can be achieved by implementing a hybrid method that uses assets data as well as the CDR method is a more practical approach and will be more effective. One of the best advantages of using the CDR method is its promising efficiency and accuracy which not only curbs the time but also reduces the marginal costs involved in the installation of a required program. This is much more advanced and reliable when compared with the current tests such as community-based targeting tests and proxy means tests. Apart from this, Machine learning has some astonishing advantages in curbing poverty. Let us have a look:
- A better Financial Inclusion: Machine learning can help financial institutions better understand the needs and behavior of low-income customers, allowing them to develop products and services that are tailored to their needs. This can promote financial inclusion and help lift people out of poverty.
- Targeted interventions: The algorithms which are backed by machine learning can analyze huge amounts of data in order to determine the low and middle-income groups and after this provide the necessary solutions for improvement. This can result in more effective resource usage and better results for those who need them the most.
- Availability of services to all: With machine learning on board, the loopholes in the services and the accessibility can be now minimized for the lower-income and middle-income groups. These services may include healthcare, education as well as day-to-day needs.
- Optimized solution for aid delivery: With the advancement that machine learning brings, lower and middle-income groups can be provided with efficient aid. This can be achieved by developing systems enabled by machine learning which can deploy distribution networks with the aim to minimize waste generation and also perform in the most efficient and accurate way to optimize the service systems.
However, when we are discussing the advantages of these systems, we must also take into consideration the limitations and barriers which come along the way. Let’s have a look:
- Natural bias: It is true that machine learning is capable of learning on its own. However, we must understand that the learning which takes place is on the basis of past experiences as well as the commands given by humans. Hence, the possibility of bias in the algorithms is quite possible.
- Installation Cost: We all agree that technology is expensive. The installation of the machine learning applications can incur huge expenses which is practically not possible for the lower income groups who do not have access to even the basic needs. Hence, companies will not be able to initiate such projects completely.
- Ethics and morality: It is highly important to keep the process of installation of applications transparent. While implementing the models for the low-income groups, it is highly important that the companies note that they are not exploiting these groups and developing programs towards the upliftment of their living conditions.
With the potential of machine learning, it is now possible that the barriers to poverty can now be demolished and a better lifestyle can be provided to the lower and middle-income groups in society. It is highly important that there is massive development in this sector and that technology is used to its full potential. With machine learning on board, it has the potential of revolutionizing the economic conditions of society and can uplift them dynamically. If the limitations are well addressed and resolved, machine learning can even minimize poverty and maybe even almost eliminate it! Hence, the steps need to be taken precisely and with the aim to make the processes efficient and fruitful.
Author- Toshank Bhardwaj, AI Content Creator
