UseCasesFor.ai

AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

Sign up

to receive a PDF containing all the use cases and stay updated with the latest AI trends and news (you can always unsubscribe)

Predicting demand for welfare assistance

Introduction

There is a rising application of the use of ML and AI in estimating the demand for social services for the last few years. This is mainly because there has been an increasing demand for improved efficiency in the public services sector. These technologies have the capacity of changing the face of welfare assistance by means of demand forecasting, enhanced service delivery, reduced costs and better decision making. The predictive models that are formulated with the help of ML and AI can work through large sets of data and identify relationships that a human analyst may not be able to identify easily and within the right time frame, hence providing a better view of the demand for welfare assistance.

Challenges

There are however some challenges that can hinder the integration of AI and ML in demand prediction even with the promising prospects. First, there is the problem of data adequacy for instance whether the data that is being presented is of quality and sufficient in quantity. This type of predictive modeling needs access to a lot of good quality data and at times such data may not be easily available. Second, there is a likelihood of prejudiced suggestions in the predictions since the data used is also prejudiced. If the data set is biased then the model learned will also be biased and will continue to manifest these biases. Third, AI and ML integration is capital intensive and needs a strong infrastructure and skilled personnel. Some of the public service providers may not have the proper infrastructure or skills set to accommodate and manage such technologies. Finally, there are issues to do with privacy and data protection. Such application of AI and ML raises issues of data sensitivity since personal data is involved, hence the need to adhere to strict measures on data management and use to meet the set legal requirements.

AI Solutions

There have been several AI solutions designed to overcome these obstacles. For example, data cleaning techniques and algorithms can be applied to enhance the quality of data that goes into the predictive models. In order to address the issue of bias, there are techniques like fairness-aware algorithms and diversity promotion. Further, the use of cloud-based AI solutions can also minimize the cost and effort needed for deploying AI and ML. These solutions are provided by third party and therefore, the responsibility of management and maintenance is not borne by the organization. There are other AI solutions like differential privacy and federated learning that can be used to ensure that data is utilized in a way that does not infringe on an individual’s privacy.

Benefits

There are many advantages of applying AI and ML in estimating the demand for welfare assistance. They include better predictions, enhanced service delivery, reduced costs, and improved decision making. This way, public service providers can manage the resources in the right manner depending on the demand, hence enhancing service delivery. In addition, there is a potential of reducing costs in the process of prediction since there will be no need for human analysts to be involved in the process and the allocation of resources will be effective. The two also enable improved decision making since they offer quantitative analysis and forecasts.

Return on Investment

It is possible to realize a high ROI when AI and ML are deployed in predicting the demand for welfare assistance. Although significant funds are required to set up the system, the returns in the long run and the benefits that come with it can be immense. For example, a research done by Deloitte revealed that AI can decrease the workload of the public sector by 30% by 2025 thus reducing costs. In addition, through enhancement of service delivery and management of resources, AI and ML can generate positive social impacts which are difficult to measure though may have tremendous positive impacts.

Choose Topic