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A collection of over 250 uses for artificial intelligence

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Child welfare prediction

Introduction

The application of Machine Learning (ML) and Artificial Intelligence (AI) in the public domain particularly in child welfare has brought about positive change in the working of child protective services. Through application of these technologies, it is possible to analyze data and make predictions on vulnerability levels, outcomes and risks for children in order to enable practitioners to take preventive and protective measures. However, there are some drawbacks of using AI in child welfare systems such as data privacy issues, bias in algorithms and lack of transparency. Nevertheless, the implementation of AI in child welfare has the possibility of making a positive change in the delivery of services, results and spending.

Challenges

The process of integrating AI into child welfare prediction has not been without its difficulties, however. Some of these challenges are the issues of data privacy and security, bias and discrimination, the issue of making AI decision making transparent and the challenge of integrating AI technologies into the current structures. Also, there is a low level of understanding of AI among the social workers as well as the shortage of training opportunities to enhance the skills of the social workers in this area. Lastly, there is a issue of lack of data consistency and interoperability of data between various child welfare systems making it difficult to implement AI effectively.

AI Solutions

There are however challenges that hinder the implementation of these technologies in the child welfare systems. Predictive risk modelling tools like Eckerd Connects' Rapid Safety Feedback and Allegheny County's Family Screening Tool employ Machine Learning algorithms to assess a massive volume of data and identify the risk of harm to a child. There are other ways through which social workers can get answers to queries, this includes chatbots and virtual assistants that are able to respond to queries instantly. Also, NLP can help to identify significant data from case notes. It is also possible to employ AI to perform repetitive tasks thus enabling the social workers to concentrate on important and strategic activities.

Benefits

There are a lot of benefits of using AI in child welfare prediction. First, it may assist in detecting high risk cases which could have been missed by a human being or due to limited resources. Second, it may improve decision making since it offers quantitative analysis. Third, it may improve efficiency by managing and performing monotonous tasks and thereby lessening the burden. Fourth, it may also assist in improving on the management of resources by forecasting the future needs that will be required in the services. Finally, it may enhance outcomes through supporting timely intervention and prevention.

Return on Investment

It is important to note that calculating the ROI of AI in child welfare prediction in terms of money can be challenging since many of the benefits are intangible. However, there are some quantitative metrics that can be used to measure the cost savings from increased efficiency and decreased workloads as well as the value gained from enhanced decision making and risk management. For instance, in Allegheny County, the Family Screening Tool helped in identifying the right families for investigation thereby minimizing on the number of cases and saving resources. It also has a long term effect on children and the society which could also form part of the ROI.

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