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Social service demand prediction

Introduction

ML and AI have already started to change a number of industries and the public sector is not an exception. Due to the pressure of the social service demands, the public service organizations are now applying ML AI to forecast the future requirements and manage the resources. Using predictive analytics, these organizations can therefore be able to forecast the demand, improve on the flow of their activities and offer better services. This approach is especially useful in the areas of health care, safety and welfare, where the precision of demand forecasting is crucial to the quality of services delivered.

Challenges

It is, however, important to note that there are a number of challenges that can hinder the effective implementation of ML AI in social service demand prediction even though it has a lot of potential. The first challenge that is likely to be encountered is the issue of data quality and data availability. It has been a challenge to manage data privacy especially in the public domain and at the same time try to leverage on the data for predictive analysis. Secondly, the lack of technical competence especially in the public service institutions hampers the integration and proper utilization of ML AI. Fourth, there might be push back from employees as they may be anxious about losing their jobs or do not trust the use of AI. Finally, the dynamic nature of the social service demand which depends on the variation of the policy, the economy, and the society makes it hard to make accurate predictions.

AI Solutions

ML AI provides answers to these challenges in many ways. There are predictive analytics algorithms that can work with historical and real-time data to make intelligent guesses about the social service requirements. For instance, ML models can help in determining the chances of patient’s re-admission or the need for emergency services by using variables such as age, gender, number of admissions in the past, and the crime rate in the area. Also, ML AI can be used to mitigate data privacy issues through the use of different techniques such as differential privacy where information about specific data can be derived without compromising the privacy of individuals. This can be done by engaging the organizations with the AI firms or by providing training to the employees. It is also possible to implement AI in a way that complements instead of substituting humans, which can help reduce anxiety and opposition that is often associated with AI adoption.

Benefits

There are numerous advantages of applying ML AI in estimating the demand for social services. It can also help in improving the efficiency of the services as the number of the services required can be predicted accordingly. This can result to reduction in costs and increased quality of services. Also, it can assist in the analysis of trends and seasonality of service demand which is useful for policy makers. Also, ML AI can enhance equity in service delivery as it will be easier to pinpoint areas of want or redundancy in different groups of the population. Last but not the least, predictive analytics can enable proactive instead of reactive approach to social services thus combating social problems at the source.

Return on Investment

There is a high return on investment of ML AI in social service demand prediction, however, it depends on various factors. These are accuracy of the predictions, the level of dependency of the decisions made on the predictions and the resources that are required to develop and sustain the ML AI systems. Although it requires a lot of investment to set up, the benefits for cost and service can generate a high ROI. For example, a research done by University of Chicago and City of Cincinnati revealed that application of ML to predict sanitation service demand could lower the costs by 12-15%.

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