Explainable AI: Making Quality Decisions Transparent

Most translation quality assessment tools provide scores without explaining the reasoning behind them. This leaves translators and project managers uncertain about why particular segments received low ratings or what specific issues need attention. We've developed innovative approaches that leverage LLMs to deliver transparent, actionable feedback through new Intelligent Score and Language Guard features, which provide clear explanations using established quality frameworks.

This session will explore how explainable AI transforms quality assurance workflows by providing detailed reasoning for quality assessments. We'll demonstrate how these intelligent features not only help users focus their efforts on segments that need attention, but also remove the guesswork with immediate correction guidance. 

Additionally we'll explore the technical challenges of making AI decisions interpretable and demonstrate the prompt engineering approaches we use to extract meaningful explanations from LLMs.

Takeaways for the audience:

- Transparent AI explanations help translators understand quality issues and improve their skills over time

- Confidence scoring with detailed reasoning allows teams to prioritize review efforts more effectively

- Robust QA checks not only prevent inappropriate content from reaching clients, but also provide safer, more inclusive alternative language.