In our efforts to build robust, unbiased AI solutions, it is appropriate to focus on training models on unbiased, dynamic, and representative data classification. Our data collection process is critical to developing trustworthy AI solutions. meeting about this AI training data through crowd workers This becomes an important aspect of your data collection strategy.
In this article, we will look at the role of crowd workers and their impact on AI development. learning algorithm ML models, and the needs and benefits they bring to the entire process.
Why do we need crowd workers to build AI models?
As humans, we generate enormous amounts of data, but only a small portion of this generated and collected data is valuable. Due to a lack of data benchmarking standards, much of the data collected is biased, riddled with quality issues, or is not representative of the environment. a little bit more machine learning And as deep learning models are developed based on massive amounts of data, the need for better, newer, and more diverse data sets is growing.
This is where crowd workers come into play.
Crowdsourcing data involves large groups of people participating to build a data set. Crowdworkers inject human intelligence into artificial intelligence.
Crowdsourcing Platform Provide data collection and annotation micro-tasks to large and diverse groups of people. Crowdsourcing gives businesses access to a large, dynamic, cost-effective, and scalable workforce.
Amazon Mechanical Turk, the most popular crowdsourcing platform, was able to source 11,000 human-to-human conversations in less than 15 hours, paying workers $0.35 for each successful conversation. Crowdworkers are participating for such a small amount, highlighting the importance of establishing ethical data sourcing standards.
It sounds like a brilliant plan in theory, but it’s not an easy strategy to execute. The anonymity of crowd workers has led to low wages, disregard for worker rights, and poor work quality that affects AI model performance.
Benefits of having data sources for crowd workers
By engaging a diverse group of crowd workers, developers of AI-based solutions can distribute micro-tasks and collect diverse and broad observations quickly and at relatively low cost.
Some of the key benefits of hiring crowd workers for your AI projects include:
Shorten time to market: According to a study by Cognilytica, almost 80% A.I 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. Large numbers of contributors can be recruited in a short period of time, eliminating traditional barriers to data generation.
Cost-effective solution: Crowdsourced data collection Reduce time and energy spent on training, hiring, and onboarding. This eliminates the cost, time and resources required because the workforce is hired on a pay-per-job basis.
Increase the diversity of your data sets: Data diversity is critical to training overall AI solutions. For a model to produce unbiased results, it must be trained on a variety of datasets. Crowdsourcing of data allows the creation of diverse (geographic, language, dialect) datasets with little effort and cost.
Improved scalability: Hiring trusted crowd workers ensures: high quality Data collection that can scale with project requirements.