Data labeling
Description
AI systems learn by example. Data labeling provides those examples by pairing input data with descriptive tags or metadata that indicate what each element represents – for instance, identifying objects in an image, classifying emotions in speech or marking entities in text.
The process can be manual, semi-automated or supported by AI-assisted tools. Regardless of the method, human oversight remains critical to ensure accuracy, remove bias and validate complex decisions. Data labeling is used across every stage of the AI lifecycle: model training, fine-tuning, evaluation and continuous improvement. Well-labeled data improves model reliability, fairness and generalization – while poor labeling can introduce bias or inaccuracies that undermine performance.
Example use cases
- Computer vision: Tag objects, boundaries and actions in images or videos for recognition models.
- Natural language processing (NLP): Annotate text for sentiment, entities, intent or translation quality.
- Speech AI: Label audio files for transcription, emotion detection and Automatic speech recognition (ASR).
- Large language models (LLMs): Curate and label multilingual data to improve reasoning and factual accuracy.
- Healthcare: Mark medical imagery or clinical text to train diagnostic and compliance systems.
Key benefits
RWS perspective
At RWS, data labeling is where human expertise ensures AI learns responsibly. Through our TrainAI Data Services, we combine global linguistic talent, domain specialists and intelligent automation to deliver accurate, ethically sourced data for training and evaluation.
Our Human-in-the-Loop workflows guarantee quality across languages, dialects and domains. From text classification to image segmentation and speech tagging, we manage large-scale, multilingual datasets that power LLMs and enterprise AI. RWS’s Human + Technology model ensures each dataset is diverse, bias-checked and contextually rich. Supported by secure infrastructure and ISO-certified processes, we provide assurance that labeled data meets the highest standards of privacy, compliance and quality. It’s how we help the world’s leading organizations build AI that understands – not just processes – human language.