CLUSTERING, EXPLAINABILITY, AND AUTOMATED DECISIONS IN COMPUTER-BASED REASONING SYSTEMS

摘要

The techniques herein include using an input context to determine a suggested action and / or cluster. Explanations may also be determined and returned along with the suggested action. The explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and / or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; and / or other measures such as the ones discussed herein, including certainty. The explanation data may be used to determine whether to perform a suggested action.