Machine Learning

What Is Bias in Machine Learning? What You Should Know

By Gowtham Raj on February 18th, 2021

The artificial intelligence market expects $110 billion in 2024, which is double its revenue from 2019.

Needless to say, artificial intelligence has transformed 21st-century modern technologies. We depend on machine learning in our everyday life, whether we use it for streaming services or medical diagnostic systems.

But its programming isn’t perfect. Not yet, anyway.

Human biases create machine learning biases that hinder the progress of AI models. Machine learning biases have severe effects, but they can be rectified to advance AI’s effectiveness. But what is bias in machine learning?

By reading on, you’ll learn what a machine learning bias is, the different kinds of biases, and how to correct them.

What Is Machine Learning?

Artificial intelligence uses machine learning as a mechanic. Machine learning learns through experience even without direct programming. Through “training data”, machine learning algorithms learn to make accurate predictions.

Training data is the first and most essential dataset programmed into machine learning. It sets precedent for the system’s evolution. But training data is also collected by humans, rendering it susceptible to human error.

Machine learning algorithms will deftly use available training data. But human prejudice in the data will affect how informed or blindsided the system will become.

What Is Bias in Machine Learning?

Biases in machine learning begin with biased data, which stem from human bias. Human bias happens long before data collection and can affect every step leading to the AI’s programming.

Biased data in machine learning can lead to consequences more severe than minor inconvenience. Inaccurate predictions from AI technology can come at the expense of real people.

There are quite a few different types of machine learning biases. We’ve listed some that originate from human bias and have actually resulted in critical repercussions.

Sample Bias

Sample bias occurs when the data used is lacking in size, representation, or both. By underrepresenting certain demographics, the AI’s dataset could teach it to only serve a limited demographic.

Confirmation Bias

Confirmation bias produces data collection that confirms data collectors’ existing beliefs. Data collectors avoid collecting data that deviate from their beliefs, even if the contrary data would better inform their dataset. When this data is inputted into machine learning models, the algorithms make ill-informed predictions in order to confirm the data collector’s beliefs.

Prejudice Bias

Prejudice bias may use large, holistic data, but the data reflects historical human prejudice. Human discrimination creates unquestioned, discriminatory data. When used for predictive algorithms, machine learning models make decisions that simply continue that prejudice.

Association Bias

Association bias falsely attributes inherent characteristics to certain groups. In one example dataset, all women have long hair and all men have short hair. If this data is implemented in machine learning models, then the algorithm will believe that only women can have long hair and that only men can have short hair.

How Can We Fix Bias in Machine Learning?

Prejudice has existed long before machine learning programming. But there are ways to prevent these biases from affecting your AI. Use the following methods to improve your model and better serve your users.

1. Understand Your Algorithm’s Weaknesses

A thorough understanding of your algorithm and data will enable you to detect their weaknesses. You’ll be able to use your knowledge in future research to improve and strengthen those weak points.

2. Research Your Users

By researching and understanding your users, you can research specifically for your users’ needs. But don’t just cater to your main demographic. Consider outliers in your user database to strengthen their attention to your service. Doing so will diversify your userbase.

3. Incorporate Rigorous Bias Testing

Incorporate bias testing into your developmental cycle. By doing so, you’ll ensure that your technology’s standards are being met. You won’t be blindsided by problems that arise from imperfect, biased programming.

4. Use Experts, Both Technical and Ethical

Using only software engineers won’t enhance your chances of fixing machine learning biases. You need different experts to create a machine learning model free of biases. Besides engineers and data label experts, use ethicists. Diversify your team to gain different perspectives that are crucial to your technology’s growth.

5. Know When You Need Machine or Human Intervention

Instead of relying on AI automation, exercise careful judgment on whether you actually need it. If your intended results are too spontaneous for machine learning, then you’ll need human involvement to address those needs.

6. Continue Your Research

These tips are just a few ways to improve technology in a quickly evolving industry. Even if you don’t specialize in artificial intelligence, keep up with the industry’s research on machine learning and biases the best you can. Let your technology evolve with your business.

You should also stay well-informed about ethical matters that affect machine learning biases. You’ll be able to learn about issues that you may not have known about before that are relevant to your users’ experience.

Let Your Technology Grow With Your Business

By learning what is bias in machine learning, you can prevent prejudices from impacting your AI. You’ll maintain a service that efficiently caters to its userbase. Not only will you satisfy your users, but you will also improve the quality of your service.

Contact us at Tart Labs if you’d like to integrate artificial intelligence into your business! Our team specializes in web and mobile app development for entrepreneurs and businesses. With us, you can modernize and advance your services.

By Gowtham Raj

February 18th, 2021

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