{"id":9144,"date":"2023-12-21T12:23:33","date_gmt":"2023-12-21T19:23:33","guid":{"rendered":"https:\/\/www.ometrics.com\/blog\/?p=9144"},"modified":"2026-01-12T12:48:09","modified_gmt":"2026-01-12T19:48:09","slug":"how-to-prevent-ai-bias","status":"publish","type":"post","link":"https:\/\/www.ometrics.com\/blog\/how-to-prevent-ai-bias\/","title":{"rendered":"AI Bias 101: How to Mitigate It in 2026"},"content":{"rendered":"\n[et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]

AI has shot into prominence in the past year. <\/span>Market leaders like Google and Microsoft<\/span><\/a> are already introducing AI-backed products and the rest of the world is following suit.\u00a0<\/span><\/p>\n

AI has become so relevant that bias has come into play regarding AI solutions.\u00a0<\/span><\/p>\n

But why should we even account for AI bias, and does it exist right now? The easy answer is yes! <\/span>DataRobot<\/span><\/a> surveyed technology leaders in 2022, and they found that 56% of them fear AI bias will lead to loss of customer trust.\u00a0<\/span><\/p>\n

In this article, we’ll dig into what AI bias means, different types of AI bias, and real-life examples. Alongside, we\u2019ll explore what to do about AI bias and how to make it less biased as you plan your business strategies for 2024.\u00a0<\/span><\/p>\n

So, let’s dive in and explore this important topic together!<\/span><\/p>[\/et_pb_text][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2023\/12\/1-1.jpg” _builder_version=”4.21.0″ _module_preset=”default” title_text=”1″ align=”center” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″ text_font_size=”13px”]

Image sourced<\/span><\/a> from datarobot.com<\/span><\/p>[\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]

What is AI Bias?<\/span><\/h2>\n

AI bias has been called different names\u2014algorithm bias and machine learning bias to list just two. <\/span>Artificial intelligence<\/span><\/a> bias happens when there are unfair or systematic discrepancies in the AI systems that predict or make decisions.\u00a0<\/span><\/p>[\/et_pb_text][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2023\/12\/2.jpg” _builder_version=”4.21.0″ _module_preset=”default” alt=”Breakdown of how using a sample population can lead to data bias” align=”center” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″ text_font_size=”13px”]

Image sourced<\/span><\/a> from wallstreetmojo.com<\/span><\/p>[\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]

While artificial intelligence biases or prejudices can emanate from different sources, the results are the same: they produce results that are discriminatory or unfair towards certain groups or individuals based on several factors such as age, gender, race, and socioeconomic status.<\/span><\/p>\n

Often, AI biases happen when programmers make assumptions during the algorithm development process or train the algorithm with biased data.<\/span><\/p>\n

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Types and Examples of AI Bias<\/span><\/h2>\n

Below are the most common examples of what AI bias can look like in 2024:\u00a0<\/span><\/p>\n

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Training Data Bias<\/span><\/h3>\n

This bias arises when the input data used to train an artificial intelligence system doesn\u2019t fully represent the population it serves. When there is training data bias, the AI will make biased decisions and recommendations that will negatively affect some groups of people. Training data bias is a <\/span>common AI chatbot mistake<\/span><\/a> and is also prevalent with decision-making AI tools.\u00a0<\/span><\/p>\n

A good example was an incident that happened in 2019. There was an algorithm used in many United States hospitals to determine which patients required further medical care.\u00a0<\/span><\/p>\n

Some researchers found that the <\/span>algorithm favored white patients over their black counterparts<\/span><\/a> by a large margin. This algorithm used datasets of patients\u2019 previous healthcare expenses for training purposes. Although the algorithm itself didn\u2019t use race in its decision-making, black patients have historically incurred lower costs than white patients with the same conditions.<\/span><\/p>\n

For this reason, the algorithm gave white patients higher health risk scores, meaning that they were more likely to be chosen by the hospitals for extra treatment programs like nursing appointments than black people with the same illnesses.\u00a0<\/span><\/p>\n

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Algorithmic Bias<\/span><\/h3>\n

Algorithmic bias is another common type of AI bias, and it happens when the bias comes from the AI\u2019s design or implementation. Algorithms can produce bias on account of their design or certain features they have come to recognize over time. This type of biased algorithm can also unintentionally favor or disfavor a group or groups of people.<\/span><\/p>\n

For example, say a company receives over a thousand job applicants for a role they intend to fill within the next month. To get the job done faster, they employ the service of an AI or ATS (Applicant Tracking System). This ATS has previously been trained on hiring data and, therefore, looks for patterns in the resumes of the candidates.\u00a0<\/span><\/p>\n

If previous data suggested that people submitting applications via the organization\u2019s US or Canada <\/span>Only Domains<\/span><\/a> website, the ATS can begin to favor them in this new data even if they are not the most qualified. This means the ATS will screen out candidates from other countries even if they are qualified for the role.<\/span><\/p>\n

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User Bias<\/span><\/h3>\n

You have a user bias when there is an issue with input data. User bias can happen when users intentionally or unintentionally enter false or discriminatory data that strengthens bias already present in the system.<\/span><\/p>\n

Imagine <\/span>inbound call center technology<\/span><\/a> using AI to make the staff’s accents sound American when talking to United States customers. This helps make communication smoother and reduces mistakes. But here’s the catch: they only use this voice-changing tech for American customers.<\/span><\/p>\n

The issue is that the AI assumes only Americans would like or need this voice change. This is a bias\u2014it’s unfair because it doesn’t think about customers from other countries who would enjoy an improved customer experience with an American accent in the mix.<\/span><\/p>\n

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Technical Bias<\/span><\/h3>\n

A situation can arise where the software or hardware used to deploy or develop an AI system introduces bias into the system. This is a technical bias.<\/span><\/p>\n

The programming teams may be constrained or lack resources like storage capacity or computing power. In that event, all <\/span>AI and machine learning solutions<\/span><\/a> will be trained on a limited dataset. In such a situation, the algorithm will be less accurate or give biased results due to inadequate exposure to a more diverse data set.<\/span><\/p>[\/et_pb_text][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2023\/12\/3.png” _builder_version=”4.21.0″ _module_preset=”default” alt=”7 consideration points for businesses looking to mitigate AI bias” align=”center” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]

Steps to Mitigate AI Bias in 2024<\/span><\/h2>\n

To mitigate bias in the machine learning algorithm, consider all the endpoints through which bias could be introduced into the system. The endpoints will determine which techniques to leverage for a bias-free AI system.<\/span><\/p>\n

With that in mind, the following techniques will help you avoid and account for bias while deploying AI solutions:\u00a0<\/span><\/p>\n

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Pre-processing techniques<\/span><\/h3>\n

As the name suggests, pre-processing techniques involve changing the input data before it is fed into the algorithm. Doing this will create a more representative and diverse dataset, which will help mitigate AI bias and inform on what to do about AI bias.<\/span><\/p>\n

Some examples of pre-processing techniques that help on how to avoid AI bias include the following:<\/span><\/p>\n