# Using Shapley Values to Explain Machine Learning Predictions in Business Operations

DevFeed: [Using Shapley Values to Explain Machine Learning Predictions in Business Operations](<https://devfeed.tech/articles/unlocking-the-ml-black-box-shapley-values-in-the-world-of-machine-learning-for-business-operations-22595.md>)

Original publisher: [Read original article](<https://medium.com/amex-gbt-technology/unlocking-the-ml-black-box-shapley-values-in-the-world-of-machine-learning-for-business-operations-ca3866fa60bb?source=rss----60a0578f4096---4>)

Author: Mahad Mohamed

Published: 2026-05-11T14:20:57Z

Content type: tutorial

Language: en

Sources: [Amex GBT Technology](<https://devfeed.tech/sources/amex-gbt-technology.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [data](<https://devfeed.tech/topics/data.md>), [context](<https://devfeed.tech/topics/context.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [context](<https://devfeed.tech/tags/context.md>), [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [deep-dive](<https://devfeed.tech/tags/deep-dive.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [operations](<https://devfeed.tech/tags/operations.md>)

## AI overview

This article explains how Shapley values, a concept from cooperative game theory, can make machine learning predictions more interpretable. It describes how they measure each feature's contribution to a specific prediction, helping business stakeholders understand model outputs such as fraud-risk ratings.

## Source excerpt

In the dynamic landscape of machine learning, businesses are constantly seeking ways to optimize processes, enhance decision-making, and drive efficiency in their operations. One powerful concept to drive data-driven efficiency is Shapley values. Originating from cooperative game theory, Shapley values offer a nuanced perspective on the contribution of each player in a collaborative setting. In the realm of machine learning, this concept is proving to be a game-changer for businesses striving to balance accuracy, transparency, and equity. But there's a problem... Currently, when businesses use ML models, the outputs are directly only related to a specific outcome and can be seen as a black-box when being related to overall business outcomes. For example, when predicting Fraud Risk, the output of a model would be the risk rating of each customer without much context of the reasons why a specific customer would be high versus low risk. Solution Shapley values directly help to solve this problem by calculating the impact of each factor when predicting any outcome. This context helps decision-makers decide the best next steps. In this article, we do a deep dive on the background and application of Shapley values to solve real-world problems. Understanding Shapley values At its core, Shapley values provide a framework for fairly distributing a value among a group of contributors based on their marginal contributions. This idea was first introduced by Lloyd Shapley in 1953 and has since found applications in various fields -- including economics, political science, and now, machine learning. In the context of machine learning models, Shapley values assign a value to each feature, indicating its contribution to the prediction for a specific instance. It answers the fundamental question: How much does each feature contribute to the model's output, considering all possible combinations? Decoding the features in machine learning Imagine a machine learning model as a collaborativ