AI, LLM Training, and Operations Efficiency

Learn how to leverage AI, technology and human capital for the future.

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Annotation fatigue: Why human data quality declines over time

Learn how prolonged annotation tasks lead to fatigue, reduced data quality, and slower output, and discover research-backed strategies Pareto AI uses to keep annotators engaged.

The consequences of ambiguity in data annotation rubrics

Ambiguous data annotation rubrics introduce noise, bias, and inconsistencies in AI training data. Learn expert-driven best practices to ensure high-quality labels.

The micro-decisions made by AI trainers that define data quality

Discover how micro-decisions by AI trainers shape data quality, safety, and alignment in LLMs.

The false dichotomy of "synthetic data vs. human data"

We provide actionable strategies on how AI companies can effectively combine synthetic and human data to enhance model performance

Behind the Data: Zachary Neeley

Last month, we had a candid conversation with Zachary Neeley, a political science tutor who transformed from a skeptical outsider to a passionate AI trainer.

Designing Robust Human Studies for AI Safety Evaluations

A comprehensive guide to identifying vulnerabilities in AI models through systematic jailbreaking research, exploring methodologies, challenges, and potential defenses.

Data Annotation's Role in Shaping Ethical AI Governance Post-AGI

As we move closer to AGI, the standards we establish today in data annotation—fair wages, worker rights, diversity, and transparent practices—will influence the ethical boundaries of tomorrow’s AI systems.

Data Annotation's Growing Appeal to PhDs and Scientists

It's time for academia to be recognized and compensated fairly for the invaluable knowledge and expertise these professionals bring to the table.

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