RAIRC03-BP10 Measure transparency quality
Consider situations where system documentation is insufficient, users do not understand the probabilistic nature of a system output, or where users are unaware of AI system presence. Transparency deficits might conceal or amplify potential harms while evaluating impacts on different stakeholder groups. The goal is finding the right transparency level for your situation by balancing enough openness to build trust and meet requirements without creating new vulnerabilities or unintended consequences.
Level of risk exposed if this best practice is not established: High
Implementation considerations
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Plan how you'll test transparency effectiveness before building your disclosure features by identifying which stakeholders from your RAIBR02 risk assessment need what level of transparency about AI system presence, capabilities, and limitations. Create simple tests that check whether users understand when they're interacting with AI, grasp the probabilistic nature of outputs, and recognize potential biases. This upfront planning assists you to build transparency features that inform users without overwhelming them.
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Design tests that measure whether your transparency disclosures assist users to make better decisions or accidentally create new problems. Build tests that track decision quality when users have different levels of system information and measure whether transparency improves outcomes or leads to misinterpretation. Test across different user expertise levels to see where more transparency assists versus where it might create confusion.
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Build measurement approaches that capture both positive transparency outcomes like increased trust alongside potential negative effects like exposure or security risks. Create simple metrics that track user confidence, stakeholder satisfaction, and compliance-aligned measures while also checking for unintended information leakage or misuse. This balanced approach assists you to spot where transparency creates value and where it might cause harm.
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Test transparency calibration by creating scenarios where users need to understand system confidence levels, limitations, and appropriate use cases for high-stakes decisions like financial or health recommendations. Build measurement tools that check whether users correctly interpret uncertainty indicators and make appropriately cautious decisions when system confidence is low. This testing catches cases where transparency gaps might lead to harmful over-reliance on uncertain outputs.
