AI-generated outputs are probabilistic and can vary in accuracy. Showing confidence scores communicates how certain the model is about its output. This helps users assess reliability and make better-informed decisions.
How to use this pattern
Assess context and decision stakes: Showing model confidence depends on the context and its impact on user decision-making. In high-stakes scenarios like healthcare, finance or legal advice, displaying confidence scores are crucial. However, in low stake scenarios like AI-generated art or storytelling confidence may not add much value and could even introduce unnecessary confusion.
Choose the right visualization: If design research shows that displaying model confidence aids decision-making, the next step is to select the right visualization method. Percentages, progress bars or verbal qualifiers (“likely,” “uncertain”) can communicate confidence effectively. The apt visualisation method depends on the application’s use-case and user familiarity. E.g., Grammarly uses verbal qualifiers like “likely” to the content it generated along with the user
Guide user action during low confidence scenarios: Offer paths forward such as asking clarifying questions or offering alternative options.
*How to show Model Confidence
There are several ways to show Model confidence. The apt method depends on the application’s use case, user familiarity with AI and the importance of accuracy in decision-making.
Numerical Confidence Scores – Show a percentage (e.g., “Confidence: 85%”) to indicate the AI’s certainty in its response. This is crucial in high-stakes scenarios like medical decisions.
Categorical Labels – Categorize responses as “Highly Certain,” “Likely,” or “Exploratory” instead of raw percentages is more user-friendly in low-stake scenarios and offers clarity without overwhelming with numeric details.
N-best alternatives - Rather than providing an explicit indicator of confidence, show
multiple options (e.g this photo might be of Apple, Peaches or Plums), to help the user to rely on their own judgement.
Uncertainty Warnings – Display messages like “This answer may not be fully accurate” for low-confidence outputs.
Color-Coded Indicators – Use colors like green (high confidence), yellow (moderate confidence), and red (low confidence) for intuitive understanding.
Graphical Representations – Use bar charts or progress indicators to visually represent confidence levels.