ML Security - the ML algorithm must be healthy and explainable in the face of malicious or non-malicious attacks - i.e. efforts to change or manipulate its behavior.
ML Explainability: it must be possible to determine why the ML algorithm behaved the way that it did for any particular prediction and what factors led to the prediction.
Industry vendors and institutions are also defining MLOps, best practices for ML algorithm deployment, testing, monitoring and lifecycle management that can help organizations scale their ML production initiatives while maintaining ML Integrity [1,23,24].
% We use the ML algorithm to estimate the PDF from the samples.
In this paper, I introduce a machine learning (ML) algorithm that evaluates urban appearance and change from time-series Google Street View images.
However, carcass profile information was considered non-relevant by the ML algorithm in earlier stages of the analysis.
Abstract: We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach.
By choosing the appropriate "incomplete data" we replace the high dimensional search, associated with the ML algorithm, with several sub-problems that require only one dimensional search.
Furthermore, when carcass weight was taken into account, the ML algorithm used only easy-to-measure attributes to clone the classifiers decisions.
In other words, humans can only trust that SGB is a suitable ML algorithm to correctly "learn" the clinical features of narcolepsy from the EU-NN database if it can successfully reveal the "ground truth" (i.e., classifying NT1 and NT2 correctly).
All the above areas can affect the quality of an ML algorithm output - ie whether the ML algorithm generates "acceptably good" predictions.
