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Diversity and Inclusion
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How Inclusive Machine Learning Can Benefit Your Organization

While ethical considerations are the primary reason to pursue inclusive machine learning, there are business-centric benefits as well.

You probably know that taking advantage of machine learning, or ML, requires collecting accurate data and developing algorithms that can analyze it quickly and efficiently.

But here's another imperative for machine learning that businesses often overlook: ensuring that machine learning models are fair and ethical by taking an "inclusive" approach to ML.

Increasingly, businesses are turning to inclusive machine learning to mitigate biases and inaccuracies that can result from poorly designed ML models. Keep reading for a look at how inclusive machine learning works, why it matters, and how to put its principles into practice.

What Is Inclusive Machine Learning?

Inclusive machine learning is an approach to ML that prioritizes fair decision-making. It's called inclusive because it aims to remove the biases that could lead to unfair decisions by ML models about certain demographic groups.

For example, inclusive ML can help businesses avoid ML-powered facial recognition tools that disproportionately fail to recognize people of certain ethnicities accurately. Or, it could help develop chatbots that are able to handle queries in non-standard dialogues of a given language.

The Benefits of Inclusive Machine Learning

Perhaps the most obvious reason to embrace inclusive machine learning is that it's simply the right thing to do in an ethical sense. Businesses don't want their employees to make biased decisions when the decision-making process takes place manually, so they should seek to avoid bias in automated, ML-driven decision-making, too.

But even if you set ethical considerations aside, there are business-centric benefits to inclusive ML:

  • Reach more users: The more fair and accurate your models, the better positioned you'll be to serve as broad a set of users as possible.
  • Create happier users: You'll achieve a better user experience, and generate happier users, when your ML models make accurate decisions about everyone.
  • Reduce complaints and support requests: Unfair ML can lead to problems like failure to log in using facial recognition. Those problems turn into support requests that your IT team has to handle. With inclusive ML, however, you can avoid these requests — and reduce the burden placed on your IT team.
  • Make more use of ML: When you embrace inclusive ML and design models that are fair and accurate, you can make use of ML in parts of your business where you otherwise may not be able to, due to the risk of inaccurate decision-making.

You don't need to have an MBA to read between the lines here: Inclusive machine learning translates to happier users, greater operational efficiency, and — ultimately — more profit for your business. So, even if you couldn't care less about ethics, it's smart from a business perspective to implement inclusive ML.

How Does Inclusive ML Work?

Inclusive machine learning requires two key ingredients: fair models and fair training data.

Fair ML models

ML models are the code that interprets data and draws conclusions based on it.

The way that you build fair ML models will depend on which type of model you are creating and which data it needs to analyze. In general, however, you should strive to define metrics and analytics categories that avoid over- or underrepresenting a given group.

As a simple example, consider an algorithm that analyzes faces and assigns a gender label to each one. To make your model inclusive, you'd want to avoid having "male" or "female" be the only gender categories you define.

Fair training data

Training data is the data that you feed to ML models to help them learn to make decisions. For instance, a model designed to categorize pictures of faces based on gender could be trained with a data set of images that are prelabeled based on gender identity.

To be fair and unbiased, your training data should represent all possible users about whom your model may end up making decisions once it is deployed, rather than only a subset.

A classic example of biased training data is a data set made up of pictures of faces of people from only one ethnic group. A model trained with data like this would likely not be able to interpret the faces of people of other demographics accurately, even if the model itself was not biased.

How to Get Started with Inclusive ML

Currently, there's no easy solution to inclusive machine learning. There are no tools that you can buy or download to ensure that your models and training data are fair.

Instead, inclusive machine learning requires making a deliberate decision to prioritize fairness and accuracy when designing models and obtaining training data. You should also carefully evaluate the decisions that your ML models are making to identify instances of bias or unfairness. These practices require effort, but they deliver benefits in the form of happier users and a more effective business.

About the author

Christopher Tozzi headshotChristopher Tozzi is a technology analyst with subject matter expertise in cloud computing, application development, open source software, virtualization, containers and more. He also lectures at a major university in the Albany, New York, area. His book, “For Fun and Profit: A History of the Free and Open Source Software Revolution,” was published by MIT Press.
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