Evaluating machine learning models presents significant opportunities for technology companies to enhance user experience and ensure fairness in their algorithms. With the evolution of technology and social media platforms, having a diverse and balanced dataset is crucial for gaining a competitive edge. A robust and representative training dataset is critical in significantly improving model accuracy.
Our Client, a major social media entity, approached Us to evaluate the fairness and robustness of their machine learning algorithm. Their goal was to create an open-source dataset, which required the collection of an extensive range of audio and video samples from diverse participant groups.
Our Client entrusted Us to design and implement a strategic plan for acquiring and managing the required data. We performed the following to ensure consistency and avoid bias in our Client’s machine learning models:
Collecting data from a wide range of users within a limited timeframe while maintaining high-quality standards is crucial for the project’s success. Employing dynamic, innovative, and quality-driven strategies led to the following results:
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