Virtual Assistant Data Collection for a Global Technology Company
Virtual Assistant Data Collection for a Global Technology Company
Ensuring accuracy, fairness, and integrity in data collection and artificial intelligence training requires maintaining a diverse and inclusive dataset. The potency and reliability of a machine learning model significantly increase when trained on a diverse and representative dataset. Creating and optimizing such datasets is crucial to gaining a competitive edge in today’s rapidly evolving technological landscape.
The Challenge
Our Client, a global technology company, partnered with Us to develop a representative dataset from their virtual assistant’s phone query logs to fine-tune their machine learning models. This initiative was critical for achieving unbiased, precise, and fair results in model training.
The Answer is Us
Our Client trusted our approach to maximize workforce engagement and ensure the highest application of data quality and security standards:
- Enhanced Crowd Recruitment: We strategically employed myriad acquisition channels, incentivized referrals, and utilized personalized, targeted communication to recruit from 17 different demographic categories.
- Streamlined Crowdsourced Data Collection: Both manual and automated tools were leveraged to gather unique queries.
- Quality Control: Regular manual and automated evaluations were conducted on all submitted data to uphold premium data quality.
- Data and Security Management: Every piece of captured data underwent stringent privacy and security protocols to ensure compliance.
The Results
The project’s success resulted from our capacity to gather data from many users within a strict timeline, all the while upholding superior quality. We achieved this through a comprehensive mobilization strategy involving existing Taskers along with new recruits. Additionally, our custom operational framework and tooling enabled enhanced consistency and minimized bias for our Client’s machine learning models.
- 17 distinct demographic groups
- 78.5% participant approval rate
- 9,700 TaskVerse sign-ups resulting from organic and marketing efforts
- Implementation of a custom-built PII detection tool for enhanced security
Improve consistency and reduce bias in your machine learning models.
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