Definition
Transparent AI Reasoning
The design requirement that an AI system articulate its reasoning — what it observed, what that implies, and what published evidence supports the inference — before its output reaches a human decision-maker. Axenya's implementation: every clinical inference surfaces with a reasoning chain and supporting literature citations; inferences the model cannot explain are suppressed entirely. Addresses two risks simultaneously: clinician liability (the physician can see and evaluate the reasoning before acting on it) and AI error rates (unexplained outputs may reflect spurious correlations rather than valid clinical patterns).
From the episode “AI Can Predict Disease Years Early”Hear it on the podcast1 episode
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Related terms in AI Fundamentals
Traditional AIEarlier AI built on explicitly programmed rules, logic, and human-written knowledge rather than learning from data.Training dataThe examples, like text, images, or audio, used to teach a machine learning model how to do its task.Transfer learningReusing a model trained on one task as the starting point for a related task, which saves data and time.TransformerThe neural network architecture behind modern AI. It uses attention to look at all parts of the input at once instead of one piece at a time.Turing testA test proposed by Alan Turing in 1950: can a machine hold a conversation well enough that a person can't tell it apart from a human?Uncanny ValleyThe creepy zone where an AI feels almost human and people recoil once they realize it is not.Unsupervised learningTraining a model on unlabeled data so it finds patterns and groupings on its own.VectorA numerical representation of any data object. Vectors allow machine learning models to understand and compare information mathematically — the building block of semantic search.