How Machine Learning, Classification Models Impact Marketing Ethics Posted on : Feb 19 - 2018

Machine learning classification needs accurate DATA TO avoid bad prediction that places marketing efforts IN ethical peril. Here's an overview to help explain how data can brands at risk.

People seek convenience in their experiences with brands. Brands have begun to use machine learning classification to know who, where, and when to direct resources to provide that convenience.

But in relying on algorithms to provide customer convenience, managers must understand classification to protect brands from making unethical societal choices when delivering outcomes to customers.

It's not new for businesses to be proactive in an effort to influence. History is filled with intriguing stories of worthy trials such as constructing homes near plants for workers, to failed efforts, too, such as those that led to the 2009 global financial crisis. When businesses use technology to influence, an important question arises. What qualities become associated with algorithms? It starts with how data is classified.

Classification algorithms define rules to place observational data into a category or group. Various classification algorithms based on precise statistics are obtained through programming. Models such as regressions, Latent Dirichlet Allocation (LDA), or cluster analysis (I explain clustering basics in this All Analytics post), can be recreated using Python, R Programming, or SAS.

The ethical challenge lies in creating the data for algorithm models. Modeling includes training datasets to train a given model while a validation dataset is used to choose the algorithm that accurately represents the environment.

Training datasets are increasingly incorporating real-world conditions.

For example, big data often includes data created from images. Digital images that were once useful as website elements can now be mapped out against location coordinates or aid in facial recognition. These associations can provide scientific value in the form of environmental impact, such as image recognition being used to map global warming impact on the coral reef. However, the variety of data types can also lead to models that incorporate societal risks or historical cultural bias associated with the data used in training. View More