Abstract
The following quote was obtained by the news editors from the background informa tion supplied by theinventors: “Machine learning models, such as neural network s, may be used in critical applications suchas in the healthcare, manufacturing , transportation, financial, information technology industries, amongothers. In these and other applications, explanations to a user related to why the model g enerated aspecific prediction for a particular input, what data, models, and pr ocessing have been applied to generatethat prediction, and/or the like can be u seful, and in some instances, required. However, conventionalexplainable machin e learning methods inefficiently and/or inaccurately demonstrate the properties of thehidden units of the models as they fail to address the multi-modal nature of unconstrained latent featureactivation. As a result, explainability methods on conventional models provide inconsistent and unreliableexplanation associat ed with the model output.”
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NSTL