In our efforts to create robust and unbiased AI solutions, it is pertinent that we focus on training models on an unbiased, dynamic and representative assortment of data. Our data collection process is extremely important for developing credible AI solutions. In this regard, bringing together AI training data using crowd workers becomes an essential aspect of the data collection strategy.
In this article, let's explore the role of crowd workers, their impact on the development of AI. learning algorithms and ML models, and the necessity and benefits they bring to the entire process.
Why are crowdworkers needed to create AI models?
As humans, we generate tons of data, but only a fraction of that data generated and collected has value. Due to the lack of data benchmarking standards, most of the data collected is either biased, riddled with quality issues, or unrepresentative of the environment. Since more and more machine learning and deep learning models are developed that draw on huge amounts of data, there is a growing need for better, newer, and more diverse datasets.
This is where crowd workers come into play.
Crowdsourced data constitutes a dataset with the participation of large groups of people. Crowdworkers infuse human intelligence into artificial intelligence.
Crowdsourcing platforms assign microtasks of data collection and annotation to a large and diverse group of people. Crowdsourcing allows businesses to access a massive, dynamic, cost-effective and scalable workforce.
The most popular crowdsourcing platform – Amazon Mechanical Turk, was able to generate 11,000 human-to-human dialogues in 15 hours and paid the workers. $0.35 for each successful dialogue. Crowdworkers are hired for such a meager amount, which highlights the importance of establishing ethical standards in data sourcing.
Theoretically, this seems like a smart plan, but it's not an easy strategy to implement. The anonymity of crowd workers has given rise to issues of low wages, disregard for workers' rights, and poor quality work impacting the performance of the AI model.
Benefits of having crowdworkers search for data
By engaging a diverse group of crowdworkers, developers of AI-based solutions can distribute micro-tasks and collect varied and extensive observations quickly and at relatively low cost.
Some of the main benefits of employing crowdworkers for AI projects are
Faster time to market: According to a Cognilytica study, nearly 80% of artificial intelligence Project time is spent on data collection activities such as data cleaning, labeling, and aggregation. Only 20% of time is spent on development and training. Traditional barriers to data generation are eliminated since a large number of contributors can be recruited in a short time.
Cost effective solution: Participatory data collection reduces time and energy spent on training, recruiting and onboarding. This eliminates the cost, time and resources required since labor is employed on a piece rate basis.
Increases diversity in the dataset: Data diversity is essential to all training on AI solutions. For a model to produce unbiased results, it must be trained on a diverse dataset. Through data crowdsourcing, it is possible to generate diverse datasets (geographic, linguistic, dialects) with little effort and cost.
Improves scalability: When you recruit reliable crowd workers, you can ensure high quality data collection that can be adapted according to the needs of your project.