So How To Ensure Data Quality While CrowdsourcingThere will be times your team will be pushed to collect data within stringent timelines. In such cases, crowdsourcing techniques do help significantly. However, does this mean crowdsourcing high-quality data can always be a plausible outcome?If you’re willing to take these measures, your crowdsourced data quality would amplify to a certain extent that you could use them for quick AI training purposes.Crisp and Unambiguous GuidelinesCrowdsourcing means that you will be approaching crowd-sourced workers over the internet to contribute to your requirements with relevant information.There are instances where genuine people fail to provide correct and relevant details because your requirements were ambiguous. To avoid this, publish a set of clear guidelines on what the process is all about, how their contributions would help, how they could contribute, and more. To minimize the learning curve, introduce screenshots of how to submit details or have short videos on the procedure.Data Diversity And Removing Bias
Bias can be prevented from getting introduced into your data pool when dealt with at foundational levels. Bias only stems when a major volume of data is inclined towards a particular factor such as race, gender, demographics, and more. To avoid this, make your crowd as diverse as possible.Publish your crowdsourcing campaign across different market segments, audience personas, ethnicities, age groups, economical backgrounds, and more. This will help you compile a rich data pool you could use for unbiased outcomes.Multiple QA ProcessesIdeally, your QA procedure should involve two major processes:A process led by machine learning modelsAnd a process led by a team of professional quality assurance associatesMachine Learning QAThis could be your preliminary validation process, where machine learning models assess if all the required fields are filled, necessary documents or details are uploaded, if the entries are relevant to the fields published, diversity of datasets, and more. For complex data types such as audio, images, or videos, machine learning models could also be trained to validate necessary factors such as duration, audio quality, format, and more.Manual QAThis would be an ideal second-layer quality check process, where your team of professionals conducts rapid audits of random datasets to check if the required quality metrics and standards are met.If there is a pattern in outcomes, the model could be optimized for better results. The reason why manual QA wouldn’t be an ideal preliminary process is because of the volume of datasets you would eventually get.So, What’s Your Plan?So, these were the most practical best practices to optimize crowdsourced data quality. The process is tedious but measures like these make it less cumbersome. Implement them and track your outcomes to see if they are in line with your vision.