AI-generated outputs are probabilistic. Communicating model confidence in Gen-AI applications is crucial for setting the right user expectations and ensuring trust.

For example, in a AI Medical chatbot, if the AI suggests a diagnosis with 60% confidence, it informs the user that it's a preliminary assessment and recommends consulting a doctor. Without this transparency, users might misinterpret uncertain responses as absolute truths, leading to potential risks. By clearly communicating confidence levels, Gen AI systems can enhance user decision-making and safety.

When to show Model Confidence

Showing model confidence depends on the application and its impact on user decision-making.

1. In high-stakes scenarios like healthcare, finance, or legal advice, displaying confidence scores are crucial weigh AI-generated insights appropriately. However, in creative applications like AI-generated art or storytelling, confidence may not add much value and could even introduce unnecessary confusion.
   

2. Additionally, if users are not familiar with probabilistic reasoning, confidence scores might be misinterpreted, leading to confusion rather than clarity. If the confidence level could be

   misleading for less-tech savvy users, reconsider how it’s displayed, or whether to display

   it at all.

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.

1. 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.
   

2. 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.
   

3. 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.
   

4. Uncertainty Warnings – Display messages like “This answer may not be fully accurate” for low-confidence outputs.
   

5. Color-Coded Indicators – Use colors like green (high confidence), yellow (moderate confidence), and red (low confidence) for intuitive understanding.
   

6. Graphical Representations – Use bar charts or progress indicators to visually represent confidence levels.