AI Data Annotation & Evaluation (Awarri Project)
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I annotated and labeled datasets for AI and machine learning model training, focusing on accuracy and consistency. My responsibilities included evaluating AI-generated responses for hallucinations, bias, and quality of reasoning. I applied structured evaluation frameworks emphasizing clarity, accuracy, and usefulness. • Performed pairwise comparisons and answer ranking with justifications
• Detected unsupported claims, vague authority references, and harmful outputs
• Used standardized criteria to maintain high annotation quality
• Supported model improvement through feedback and precise labeling.
I annotated and labeled datasets for AI and machine learning model training, focusing on accuracy and consistency. My responsibilities included evaluating AI-generated responses for hallucinations, bias, and quality of reasoning. I applied structured evaluation frameworks emphasizing clarity, accuracy, and usefulness. • Performed pairwise comparisons and answer ranking with justifications • Detected unsupported claims, vague authority references, and harmful outputs • Used standardized criteria to maintain high annotation quality • Supported model improvement through feedback and precise labeling.
2024 - 2024