Explainable AI (XAI)

Many AI models, especially deep learning systems, operate as complex “black boxes” that are difficult to interpret. Explainable AI techniques reveal the factors that influence a model’s output, showing why it made a particular prediction or recommendation. These explanations help stakeholders validate results, uncover hidden biases and improve model performance.

XAI is increasingly essential in regulated sectors where decisions must be transparent and defensible. Clear explanations also help data scientists identify errors, refine training data and collaborate more effectively with domain experts. Explainability goes beyond model behavior – it improves trust. When organizations understand how an AI system works, they can deploy it responsibly and meet evolving expectations around ethics, fairness and regulatory compliance.

RWS supports explainable AI development by helping organizations build high-quality, diverse and well-structured training datasets. This Human + Technology approach ensures AI systems learn from accurate information and produce outputs that are easier to interpret, validate and govern.

RWS strengthens explainable AI initiatives by improving the data that models learn from. Through expert annotation, high-quality multilingual datasets and structured content strategies, RWS helps organizations build AI systems that are more transparent, traceable and aligned with domain knowledge. Our Human + Technology approach supports the development of AI that teams can understand, validate and trust across industries with regulatory or ethical requirements.