Jan 20, 2019 · Here we propose a quantitative measure for the quality of interpretability methods. Based on that we derive a quantitative measure of trust in ML decisions.
In this work we propose a metric to quantify and com- pare the quality of interpretability methods. Another equally challenging to measure concept that is.
A trust metric is derived that identifies when human decisions are overly biased towards ML predictions and demonstrates the value of interpretability for ...
Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them.
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The more complex machine learning models are, the harder it gets for humans to interpret them. Read this interview with Serg Masis, Data Scientist at ...
This repository contains scripts and reports on explaining Deep Learning applications for Remaining Useful Life and Machine's health Estimation.
Dec 28, 2023 · In this paper, we introduce a new method for measuring explainability with reference to an approximated human model.
In this paper we review the recent literature to provide guidance to researchers and practitioners on the design of explainable AI systems for the health-care ...
These consumers need to understand and trust the model and have enough information about machine predictions to combine with their own inclinations to produce.
A Two Sigma engineer outlines several approaches for understanding how machine learning models arrive at the answers they do.