FAQs: Human data annotation for generative and agentic AI
What is human data annotation in generative AI? Human data annotation is the process of labeling AI training data with meaning, tone, intent, or accuracy checks, using expert human reviewers. In generative AI, this helps models learn to produce outputs that are truthful, emotionally appropriate, localized to be culturally relevant, and aligned with user intent. […]
Sigma AI defines new standards for quality in generative AI
As enterprises face increased risk from hallucinations and misinformation, Sigma Truth evolves benchmarks beyond accuracy PRESS RELEASE: MIAMI – September 2, 2025 – Sigma AI, The Human Context Company and a global leader in human‑in‑the‑loop data annotation, today announced new standards for evaluating and improving the quality of generative AI outputs. As enterprises rapidly adopt […]
Generative AI glossary for human data annotation
Agent evaluation The process of assessing how well an AI agent performs its tasks, focusing on its effectiveness, efficiency, reliability, and ethical considerations. Example: An annotator reviews a human-agent AI interaction, determining whether the person’s needs were met, and whether there was any frustration or difficulty. Attribution annotation Labeling where facts or statements originated, such […]
Feedback loops: Enhancing AI data quality with human expertise
How feedback loops in AI work In AI and machine learning, a feedback loop is a continuous, iterative process designed to improve the performance of an AI model and make it more reliable and accurate over time. During data annotation, a team of expert annotators will label, enriche, and expand on an initial dataset to […]
Gen AI in healthcare: Improving patient care and efficiency
Why healthcare is betting big on generative AI The power of gen AI to analyze vast datasets, from patient records and medical imaging to clinical trial results and research literature, is fueling its fast-growing adoption in healthcare. According to a McKinsey survey, 85% of healthcare organizations are either exploring or have already adopted gen AI […]
Why inter‑annotator agreement is critical to best‑in‑class gen AI training
What is inter‑annotator agreement (IAA) and why is it important? IAA measures how consistently multiple annotators label the same content. It helps quantify whether annotation guidelines are clear and whether annotators share a reliable understanding. Common metrics: Even seasoned experts often show α = 0.12–0.43 in high‑subjectivity tasks like emotional attribute scoring, especially before refining […]
Why gen AI quality requires rethinking human annotation standards
From accuracy to agreement: A new lens on quality Traditional AI annotation tasks (e.g. labeling a cat in an image) tend to yield high human agreement and low error rates. Annotators working with clear guidelines often achieve over 98% accuracy — sometimes even 99.99% — especially when backed by tech-assisted workflows. But these standards don’t […]
Precision in data annotation: What’s needed for gen AI models
Precision in gen AI data annotation Gen AI models learn to create novel content. However, for these models to be reliable and useful, their content should be grounded in accurate information and logical structures. In gen AI data annotation, precision extends beyond accurate facts; it also encompasses creativity and nuance. Precise outputs should be factually […]
Human touch in gen AI: Training models to capture nuance
Humanity in gen AI data annotation Data annotation is not just about accuracy and precision. It requires human expertise and careful oversight to ensure AI models interact with the world in a meaningful, relevant, and responsible way. Drawing from our most recent whitepaper, “Beyond accuracy: The new standards for quality in human data annotation for […]