Nowadays, AI has become an integral part of our daily lives. When we scroll through short-video apps, algorithms suggest videos we might like. When we apply for loans, systems assess our creditworthiness automatically. Even in healthcare, AI tools may help doctors analyze medical images. But have you ever wondered if these AI models might “play favorites”? For example, two people with similar qualifications could have different loan approval odds. Minority groups, in particular, get rejected more often in such cases. Or an AI facial recognition system may be less accurate for Asian or African faces. It works much better when identifying Caucasian faces. Behind all these issues is a critical problem: model bias.

The goal of this article is to break down model bias in simple terms. It will help you understand what model bias is, what forms it takes, why it happens, and what we can do to reduce it. After all, AI fairness isn’t just about protecting individual rights—it also impacts the fairness and inclusivity of our entire society. Understanding model bias is the first step to using AI wisely and holding it accountable.​

What Is Model Bias? ​

Put simply, model bias refers to situations where AI models systematically favor certain groups of people, opinions, or outcomes when making decisions or generating outputs—while treating others unfairly. Importantly, this isn’t the same as “random errors.” Random errors are occasional and unpredictable, but model bias is “systematic”: it’s built into the model’s design, training, or use. For example, a resume-screening AI that consistently favors male applicants isn’t just “missing” female resumes by chance—it’s likely been trained or designed to prioritize male candidates, reflecting a hidden assumption that “men are better suited for the role.”​

Here’s a relatable example: imagine an e-commerce platform’s recommendation algorithm. It notices that young users click on beauty ads more frequently, so it keeps showing lipsticks and eye shadows to women aged 20–30. But it rarely recommends anti-aging skincare products that would better suit women over 50. This is model bias in action—the algorithm ignores the needs of older users, fixating only on the group that drives high click rates.​

What Are the Types of Model Bias?​

Data Bias: The Model Learned from “Unbalanced” Raw Materials​

This is the most prevalent type of bias. Think of it as similar to cooking. No matter how skilled the chef is, they can’t make a great dish with bad ingredients. Stale or limited ingredients will ruin the dish. For example, take a facial recognition model. Suppose it’s trained using 90% photos of white people. Then it will often misidentify Asian or African individuals. The reason is simple—it hasn’t “seen” enough faces from these groups. This kind of issue is called underrepresentation bias in data.

There’s also the more hidden historical bias embedded in data. Suppose an AI resume-screening tool is trained on 10 years of past hiring data. If, historically, the company hired far more men for technical roles, the data will show men having much higher acceptance rates. The AI will then learn to assume “men are better for technical jobs,” even if a female candidate is more qualified. In this way, the AI replicates and reinforces past unfairness.​

Algorithmic Bias: The Model’s “Thinking Logic” Is Skewed​

Algorithms are the “brain” of an AI model. If that brain’s “thought process” is flawed, the results will naturally be biased. Take a food delivery platform’s order-assignment algorithm, for example. If its only goal is “maximizing delivery efficiency,” it will keep assigning nearby, easy-to-deliver orders to experienced riders. New riders, meanwhile, get stuck with long-distance or difficult orders. While overall delivery speed improves, new riders earn less and are more likely to quit. This is objective function bias—the algorithm prioritizes “efficiency” over “fairness.”​

Another form is feature selection bias. Imagine a loan-approval model that uses “neighborhood of residence” as an evaluation criterion. If a neighborhood has lower property values, the model might automatically label its residents as “high-risk borrowers.” But many people in that neighborhood have stable incomes and good credit—they’re rejected simply because of where they live. The model uses an “indirect feature” that correlates with socioeconomic status, leading to indirect discrimination against low-income groups.​

Deployment Bias: The Model Is “Misfit” for Real-World Scenarios​

Even if a model performs fairly in a lab, it can “struggle to adapt” when used in real-world settings. For example, a medical AI diagnostic tool might be trained and optimized at hospitals in northern China, where it learns to recognize symptoms of “respiratory diseases common in cold, dry climates.” But when it’s deployed in southern China, it frequently misdiagnoses “damp-heat type respiratory diseases”—a condition more common in the south’s humid climate. The model fails to adapt to regional differences in disease symptoms, resulting in deployment scenario bias.​

There’s also user perception bias. Consider an educational AI recommendation system that only suggests easy questions to students. Easy questions lead to higher accuracy rates, so the model thinks “the student is learning well.” But in reality, students need challenging questions to improve their skills. The model prioritizes avoiding low accuracy over meeting the student’s real needs—focusing on surface-level data instead of understanding what the user truly requires.​

Why Does Model Bias Happen?

Model bias doesn’t emerge out of nowhere. It’s rooted in every stage of AI development, with three key stages being the main culprits:​

Data Stage: “Unbalanced” Training Data​

Data is the “teacher” of AI models. If the teacher’s lessons are biased, the student (the model) will learn poorly. On one hand, data collection often uses shortcuts. For example, when companies gather user data, they might only collect from young people. They end up ignoring older users in the process. On the other hand, data labeling is prone to subjective bias. Suppose a labeler dislikes a certain opinion. When annotating data for a sentiment analysis model, they might mislabel neutral statements. They could mark these neutral words as “negative” by mistake. Then the model learns to dislike that opinion too.

Design Stage: “One-Sided” Goals​

When designing AI models, developers often prioritize “performance” and “efficiency” over “fairness.” For example, developers of recommendation algorithms focus most on metrics like “click-through rate” and “user engagement time.” As long as these metrics are high, they consider the model successful—without asking whether all users can find content that meets their needs. Similarly, developers of financial AI might only care about “reducing default rates,” ignoring whether different groups have equal access to loans.​

Human Stage: “Hidden” Human Biases​

AI development and use are inseparable from humans—and human biases can quietly “infiltrate” models. For example, developers might unconsciously inject their own beliefs into the model: assuming “young people are more tech-savvy,” they might add an “age weight” that favors younger users. Or companies might cut corners when using AI, directly adopting models built by others without adapting them to their specific scenarios—leading to deployment bias.​

How to Address Model Bias?

Addressing model bias isn’t the responsibility of a single person. It requires collaboration between developers, companies, and users, with key actions in three stages:​

Data Stage: Make “Raw Materials” Fairer​

First, ensure data is comprehensive: when collecting data, include people of different genders, ages, ethnicities, and regions. For example, a facial recognition model should include samples of yellow, white, black, and brown skin tones—with proportions that reflect real-world population distributions. Second, clean the data: use tools to detect historical biases. If hiring data shows men have much higher acceptance rates, use technical methods to “balance” the data weights so the model doesn’t learn this bias. If data on certain groups is scarce, use AI to generate synthetic data (e.g., creating simulated profiles of female technical job seekers) to fill the gaps.​

Design Stage: Add “Fairness Constraints” to the Model​

Developers must treat “fairness” as a core goal, on par with “performance.” For example, a food delivery order-assignment algorithm should include a constraint like “new riders must receive a reasonable share of orders”—in addition to optimizing for delivery efficiency. A loan-approval model should not only assess “repayment ability” but also check “approval rate differences between ethnic or gender groups.” If the difference exceeds 5%, the algorithm should be adjusted. Meanwhile, avoid using “sensitive features”: don’t directly use attributes like “gender” or “ethnicity,” and avoid indirect features like “neighborhood” or “name” that might correlate with sensitive information.​

Usage Stage: Continuous Monitoring + Human Review​

Companies shouldn’t “set and forget” AI models. They need to establish monitoring systems: for example, an AI hiring tool should check “gender differences in pass rates” weekly. If bias is detected, the model should be paused and adjusted. For medical AI diagnostic tools, collaborate with doctors—if doctors notice the AI frequently misdiagnoses certain patients, this feedback should be sent to the technical team for optimization. Users also have a role to play in oversight: if you notice an AI recommendation system consistently ignores your needs, or if you feel unfairly treated during loan applications or job searches, provide feedback to the company. In serious cases, you can even file a complaint with regulatory authorities—your input can help make AI fairer.​

Conclusion​

AI “favoritism” isn’t something that has to happen. It comes from human oversights in three key areas. These areas are data collection, model design, and AI usage. But with human effort, this “favoritism” can be corrected. Understanding model bias isn’t just about protecting your own rights. It’s also about shaping AI into a tool that “doesn’t play favorites.” A good AI model isn’t just the “smartest” one out there. Instead, it should be the fairest one. It needs to boost efficiency while keeping fairness in mind. In the end, it should truly serve every person.