Queen's University at Kingston

Nuenergy.ai

Evan W Steeg

Jorge Deligiannis

Niraj Bhargava

Gaston Gonnet

Fred Speckeen

The present disclosure provides a system and method for use in evaluating the trustworthiness of an artificial intelligence (AI) process. The trustworthiness evaluation may include both automated and manual evaluation of the AI process. Further, the system is provided with functionality for automatically evaluating and modifying the criteria used in evaluating the AI process.

Methods and systems for the measurement of relative trustworthiness for technology enhanced with AI learning algorithms

A method and system for detecting coincidences in a data set of objects, where each object has a number of attributes. Iteratively, equally-sized subsets of the data set of sampled, and coincidences (co-occurrences of a plurality of attribute values in one or more objects in the subset) are recorded. For each coincidence of interest, the expected coincidence count is determined and compared with the observed coincidence count; this comparison is used to determine a measure of correlation for the plurality of attributes for the coincidence. The resulting set of k-tuples of correlated attributes is reported, a k-tuple of correlated attributes being a plurality of attributes for which the measure of correlation is above a predetermined threshold. The method and system (implemented on an array of processing nodes) is suitable for protein structure analysis, e.g. in HIV research.

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Kingston, Canada

Evan W Steeg