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](https://www.defined.ai/blog/how-to-ensure-quality-when-crowdsourcing-ai-training-data/)** 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](https://www.technologyreview.com/2021/01/29/1017065/ai-image-generation-is-racist-sexist/)**.

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!

**[Download the White Paper](https://prdstrapimediastorage.blob.core.windows.net/prdstrapimediastorage/assets/1083_Defined_AI_White_Paper_Overcoming_the_Challenges_of_Crowdsourcing_AI_Training_Data_d715c2cb46.pdf)**

Downoad White Paper

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Speech

Arabic Scripted Monologue

Portuguese (Brazil) Spontaneous Dialogue

Aspect-Based Sentiment Analysis

Case Studies

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Mastering Linguistic Diversity: Sourcing French Speech Training Data for Many Dialects