Technology, communications, and social media have changed emergency and disaster response networks. These developments enable affected citizens to generate georeferenced real-time data on important events, fueling this new landscape. Detecting and investigating such events requires crowdsourcing and machine learning. Crowdsourcing generates, aggregates, and filters data, while automatic tools analyze publicly available data using information retrieval techniques. Crowdsourcing encourages and coordinates large-scale participation in many fields. Crowdsourcing useful data and human computation interchangeable knowledge will help public health informatics soon. These efforts will lower any nation's disease burden and healthcare costs. It advances sustainable development goals and milestones. This chapter proposes crowd-sourcing modeling to improve public health surveillance for communicable and non-communicable diseases. These efforts will lower any nation's disease burden and also improve sustainable development goals.
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Crowdsourcing is used to increase the intelligence of artificial intelligence which is equally important for equity important for building the efficient Public Health care system. It achieves this by facilitating data collection from large groups of people and other sources for use by businesses and individuals. Large amounts of data and commentary are typical components of such information. This is typically achieved via the world wide web. With this information, machine learning models can be trained and improved for use in the creation of AI systems. Artificial intelligence (AI) systems can better represent the real world and are less likely to be biassed or limited by the mindsets of employees who adhere to conventional business thinking if they collect data from a large number of people.
Crowdsourcing's primary advantage is that it helps businesses gather massive amounts of information with minimal additional time and financial outlay. With the help of online surveys, this can be especially useful for organizations seeking information from marginalized populations like those with disabilities. When developing speech-based systems, it may be necessary to priorities the inclusion of under-represented speakers of that language, speakers of that language with a particular regional accent, or speakers of that language as a second language. These individuals may be native speakers of the target language with an accent, or they may be bilingual.
Moreover, AI is improved through crowdsourcing because it allows for the collection of feedback on the performance of AI systems, which in turn enables the identification and correction of errors and biases. Humans can be tasked with evaluating the AI system's output and providing feedback for further training and development. To achieve this goal, it is possible to have humans assess the AI's performance. This ensures the reliability and objectivity of AI systems, as well as their ability to fulfil user needs.
Some instances where crowdsourcing helped make an AI more capable.
Amazon Mechanical Turk is a crowdsourcing platform for small tasks that helps businesses and academics quickly gather data and feedback from a sizable group of people. Users of the platform can provide their data in exchange for payment whenever and wherever they have free time, allowing them to earn a supplementary income without sacrificing their primary responsibilities. In order to train machine learning models, such as image and text annotation, the platform is frequently used for data collection.
Using Google's AI Platform, programmers can create their own custom data sets to train the machine learning models with. Data annotation is another useful tool available on the platform [1;, and it can be used by anyone with a Google account. Work can be found for people of all ability levels. Using Groups of Amateur Scientists from the Internet to Complement Expert Studies
Citizens who participate in scientific research come in all shapes and sizes. Zooniverse is a citizen science platform that mobilizes a global community of over a million people to collect, organize, and analyze data in support of scientific discovery. Numerous scientific studies and inquiries have made use of the platform to amass data. The platform is equipped to equip citizen scientists with the tools they need to tackle a wide range of scientific challenges, from the investigation of galaxy formation to the tracking of climate change and the protection of endangered species. Volunteers have the opportunity to follow a personal interest while making a positive impact on society, and researchers can strike up conversations with people from more walks of life than they would with other researchers.
Mozilla's Common Voice is an effort to improve the accuracy of voice recognition software by compiling data from the general public. In essence, it aids in training machines to understand human speech by studying how people actually speak. By reading the provided sentences aloud, users can contribute voice samples to the project and help validate the samples contributed by others. Speech samples have been donated, but the donors have not been compensated.
Figure 1.
Representation of core phases needed for initiations of crowdsourcing