Why human skills are the secret ingredient in generative AI
Rethinking AI development — from code to human intelligence When most people think of artificial intelligence, they imagine complex algorithms and machine logic. But Sigma is proving that the most powerful AI systems begin with people. The company specializes in training individuals to perform generative AI data annotation — the behind-the-scenes work that fuels model […]
How red teaming AI reveals gaps in global model safety
Red teaming goes global Red teaming — intentionally probing AI models for weaknesses — has long been a key practice in AI safety. But most efforts focus on English, text-based interactions. Sigma AI decided to take things further. In our latest study, they pushed top models to their limits, examining how they behave in different […]
Bias detection in generative AI: Practical ways to find and fix it
Protection: Adversarial testing surfaces unfair behavior Common bias patterns: Prompt-induced harms (e.g., stereotyping a profession by gender), jailbreaks that elicit unsafe content about protected classes, or unequal refusal behaviors by demographic term. How to combat it: Run red-teaming at scale with targeted attack sets: protected-class substitutions, counterfactual prompts (“they/them” → “he/him”), and policy stress tests […]
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 […]