The benefits of comprehensive machine learning


You may learn to take advantage of machine learningor ML, collects accurate data and develops algorithms that can analyze it quickly and efficiently.

But there is another imperative for machine learning that companies often overlook: ensuring that machine learning models are fair and ethical by taking a “holistic” approach to machine learning.

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

What is comprehensive machine learning?

Holistic machine learning is an approach to machine learning that prioritizes fair decision making. It is called comprehensive because it aims to remove biases that may lead to unfair decisions by ML models about certain demographic groups.

For example, comprehensive ML can help companies avoid ML . facial recognition Tools that disproportionately Fails to recognize people of certain races accurately. Or it could help develop chatbots capable of handling queries in non-standard dialogs for a particular language.

The benefits of comprehensive machine learning

Perhaps the most obvious reason to embrace mass machine learning is that it is simply the right thing to do in an ethical sense. Companies don’t want their employees to make biased decisions when decision-making is done manually, so they should strive to avoid bias in ML-driven automated decision-making as well.

But even if you put ethical considerations aside, there are business-centric benefits to holistic machine learning:

  • Reach more users: The more fair and accurate your models are, the better positioned you will be to serve the widest possible group of users.
  • Creating happier users: You will achieve a better user experience, and generate happier users, when your ML models make accurate decisions about everyone.
  • Reducing complaints and requests for support: Unfair ML can lead to problems such as failure to log in using facial recognition. These issues turn into support requests that your IT team has to deal with. However, with end-to-end ML, you can avoid these demands – and reduce the burden on your IT team.
  • Get more out of ML: When you adopt comprehensive ML and design models that are fair and accurate, you can take advantage of ML in parts of your business that you may not be able to, due to the risks of inaccurate decision making.

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

How does comprehensive machine learning work?

Inclusive machine learning requires two main components: fair models and fair training data.

Fair ML Models

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

How you build fair ML models depends on the type of model you’re creating and the data you need to analyze. In general, however, you should strive to identify metrics and categories of analyzes that avoid over- or under-representation of a particular group.

As a simple example, consider an algorithm that analyzes faces and assigns a gender classification to each one. To make your model inclusive, you may want to avoid making “male” or “female” the only gender categories you specify.

fair training data

Training data is the data that you feed to ML models to help them learn to make decisions. For example, a model designed to categorize images of faces on the basis of gender can be trained using a data set of images previously categorized on the basis of gender identity.

To be fair and unbiased, your training data should represent all potential users that might end up making decisions once your model is deployed, rather than just a subset.

The classic example of biased training data is the dataset made up of images of people’s faces from only one ethnic group. A model trained with such data would likely not be able to accurately interpret the faces of people from other demographics, even if the model itself was not biased.

How to get started with comprehensive ML

At the moment, there is no easy solution for end-to-end machine learning. There are no tools you can buy or download to ensure that your forms and training data are fair.

Instead, end-to-end machine learning requires a thoughtful decision to prioritize fairness and accuracy when designing models and acquiring training data. You should also carefully evaluate the decisions your ML models make to identify instances of bias or unfairness. These practices require effort, but offer benefits in the form of happier users and more efficient work.

About the author

Christopher Tosi with a bullet to the headChristopher Tosi He is a technical 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 for Profit: A History of the Free and Open Source Software Revolution, is published by MIT Press.


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