# Adding Explainability to Machine Learning Routines via Runtime Execution of Labeling Functions

DevFeed: [Adding Explainability to Machine Learning Routines via Runtime Execution of Labeling Functions](<https://devfeed.tech/articles/adding-explainability-to-machine-learning-routines-via-runtime-execution-of-labeling-functions-28193.md>)

Original publisher: [Read original article](<http://fuzzyblog.io/blog/machine_learning/2020/01/10/adding-explainability-to-machine-learning-routines-via-re-execution-of-labeling-functions.html>)

Author: Fuzzygroup

Published: 2020-01-10T00:00:00Z

Content type: article

Language: en

Sources: [Scott Johnson](<https://devfeed.tech/sources/scott-johnson.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [data](<https://devfeed.tech/topics/data.md>), [Kafka](<https://devfeed.tech/topics/kafka.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [explainability](<https://devfeed.tech/tags/explainability.md>), [kafka](<https://devfeed.tech/tags/kafka.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [python](<https://devfeed.tech/tags/python.md>), [snorkel](<https://devfeed.tech/tags/snorkel.md>), [supervised-learning](<https://devfeed.tech/tags/supervised-learning.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

The article discusses the black-box nature of machine-learning outputs and introduces runtime execution of labeling functions as an approach to adding explainability. It also explains basic concepts including supervised learning, labeled datasets, neural networks, training programs, and models.

## Source excerpt

I am spending much of my time these days in the world of machine learning and I'm finding that it is profoundly interesting. One of the most interesting aspects of machine learning, for me, is the black box nature of machine learning's output. What we typically get from a machine learning model what the model thinks but not how it arrived at that result. And this lack of explainability is true for machine learning whether the underlying technology is Tensor Flow for image segmentation or Bert / Roberta / Albert for text processing. Note: Bert / Roberta / Albert are machine learning tools that generate linguistically savvy models that understand English language content. As a side note, given how hard it is for most people to understand why they think what they think, is the black box nature of machine learning actually all that surprising? Machine Learning Basics for the Newbie The world of machine learning, which appears intimidating as hell to the newbie, actually isn't that hard but the terms can be terrifying. Someone recently asked me what I was doing and this was my answer: "I am working on a weekly supervised learning model for an anti Semitic classifier that will be used in a Kafka data pipeline. " Here are the very basics of machine learning: Machine Learning is just that - a way for a machine to learn from data. The underlying idea of machine learning is pretty much just pattern recognition based on data fed through a neural network. To make machine learning work you "label" a data set which means you tell the machine that for Example X, the answer you want is Foo and for Example Y, the answer is Bar. And you do this over and over and over (sometimes you have to label 50,000 examples; that's what my current project uses). You give a program, generally called a training program, this labeled data and then it thinks on this for a while and creates what is called a model. The model is a trained neural network which understands how to process data like what yo