Overcoming the Challenges of Crowdsourcing AI Training Data
Crowdsourcing AI Training Data Can Be Difficult—But It Doesn’t Have to Be.
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!