This is how people like machines to explain themselves -- Sonder Scheme

Core to human-centered AI is explainability. If a machine cannot explain its reasoning in a way that humans understand and on human terms, the AI isn't working for people. Researchers from Georgia Institute of Technology, Cornell University and the University of Kentucky recently published the results of teaching a machine to generate conversational explanations of its model's internal state and action data representations in real-time. They tested whether people like the machine to tell them how it made decisions, and what characteristics of explanations drove people's perceptions of explainability. Relatability is key to understandability – when an AI uses natural language to explain itself, people put themselves in the AI's shoes and evaluate understandability based on whether the AI gives the same reasons they would.