For artificial intelligence (AI) to function as envisaged, it needs to be fueled by high-quality, representative data. However, this is easier said than done as getting one’s hands on high-quality data is one of the biggest barriers to adopting and implementing AI.

Crowdsourcing was long ago identified as a solution to the problem of collecting massive amounts of data, but ensuring that data’s quality can extremely difficult. This is a particularly sticky issue with most popular open-source datasets, many of which have led to innovative AI implementations marred by the questionable quality of the data they were trained on.

To build a language model that won’t get you in hot water with the very people you’re building it to serve, the questions we must ask are:

• How do you ensure data contributors are really native speakers of a specific language?
• How do you ensure contributors are completing collection tasks properly?
• How can you test the quality of data collected?
• How do you find the right contributors necessary for a specific data collection?

In this white paper, we’ll examine the challenges of crowdsourcing training data for AI and how to effectively overcome them. Download it here!