When a recommendation needs an explanation
An AI system recommends cutting the marketing budget for a high-performing product but gives no reason. Clients question the recommendation, see the system as a black box, and become less willing to use it. An explanation of the relevant factors would give them a basis for judging the advice rather than simply accepting or rejecting it. section 11.4
Transparency means communicating what an AI system can and cannot do, how to understand its outputs, and what users should consider before acting on them. It is both a governance concern and a user-experience choice: clear explanations support informed decisions, while honest limits reduce overreliance when a probabilistic system fails. section 11.4 section 12.3
Make transparency useful at the point of decision:
- Explain the factors behind consequential outputs so users can assess and discuss them. Treat explainability, interpretability, and accountability as parts of transparency. section 11.4
- Signal when AI is processing information, show uncertainty where appropriate, and let users modify or reject outputs. section 10.4
- Disclose relevant data practices and limitations. Address privacy, bias, and safeguards directly with customers and compliance stakeholders. section 11.2 section 12.2.4
- Keep communication two-way: collect feedback after launch to find failures and situations the team did not anticipate. section 10.4 section 12.3
A forecast a customer can challenge
If a holiday-inventory forecast conflicts with historical trends, a customer success team can point to factors such as weather or industry trends and explain that the prediction is an input to the client’s decision, not the final decision. Confidence intervals, the ability to adjust predictions, and documentation of data sources and algorithms give users further ways to assess it. section 12.2.3 section 12.3.3
