What Causes Algorithmic Bias in Machine Learning
Imagine teaching a child only one side of a story, and then asking them to make fair decisions. That’s a bit like what happens when algorithms learn from biased data.
Machine learning may feel like a world of cold, objective numbers, but in reality, it’s shaped by the information—and the people—behind it.
When the data is flawed or incomplete, the algorithms inherit those flaws, leading to unfair results. This is called algorithmic bias, and it can quietly influence everything from job hiring to loan approvals to facial recognition.
The causes aren’t just technical—they often reflect human decisions, assumptions, and blind spots.
Understanding the causes of algorithmic bias isn’t just about fixing code. It’s about making AI fair, accurate, and trustworthy for everyone it serves.
In this blog post, we will uncover the main causes of algorithmic bias in machine learning—from biased data and sampling errors to feedback loops and opaque models.
Read Here: The Difference between Algorithmic Bias and Data Bias
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| Algorithmic bias tips the scales, showing how machine learning can impact fairness. |
Top 10 Causes of Algorithmic Bias in Machine Learning
Algorithmic bias in machine learning stems from flawed data, design choices, and evaluation methods. Understanding its top causes helps build fairer, more ethical AI systems that serve diverse communities responsibly.
Here are the top 10 causes of algorithmic bias in machine learning, drawn from expert sources like IBM, GeeksforGeeks, Analytics Vidhya, etc.
1. Biased Training Data
If the data used to train an algorithm is biased, the algorithm will learn and repeat that bias.
For example, if a hiring dataset contains mostly resumes from men in leadership roles, the AI may learn to favor male candidates over equally qualified women. This happens because the algorithm sees “male leadership” as a pattern to copy. It’s like teaching a child only one version of history—they’ll naturally think that’s the whole truth.
In machine learning, the “garbage in, garbage out” rule applies: if biased data goes in, biased predictions come out.
The fix starts with diverse, representative datasets that reflect reality more accurately.
2. Sampling Bias
Sampling bias occurs when the data collected doesn’t represent the whole population.
Imagine training a medical AI on health data only from young adults. The algorithm might fail to diagnose diseases common in older patients. This isn’t because the AI is “against” seniors—it simply hasn’t seen enough examples to make fair predictions.
Sampling bias can happen in crime prediction systems, too—if the data comes mostly from high-policing areas, the algorithm might wrongly suggest those areas are more dangerous.
The key is balanced sampling, where every relevant group is proportionally represented, so the model learns fairly and makes decisions that work for everyone.
3. Historical Bias
Historical bias happens when past inequalities get baked into present-day data.
For example, if a loan approval system is trained on decades of bank records where certain minority groups were denied loans more often, the algorithm may “learn” that those groups are risky borrowers.
The bias doesn’t come from bad intentions in the algorithm—it comes from old, unfair human decisions. This can create a feedback loop, where old discrimination keeps influencing new AI decisions.
Breaking this cycle requires questioning whether the patterns in historical data reflect fairness or just outdated prejudice. If not corrected, AI becomes a powerful tool for repeating the past.
4. Labeling Bias
In supervised learning, humans label the data the AI learns from. If the labeling process is biased, the AI inherits that bias.
For instance, in a facial recognition project, if annotators label images of dark-skinned faces as “unclear” more often, the system might learn to be less accurate for those faces.
Similarly, if medical images are labeled incorrectly due to human error or bias, the algorithm will misdiagnose similar cases. This cause is subtle because it hides in the “truth” we feed the AI.
The solution is to train labelers, use multiple reviewers, and apply quality checks so the AI learns from accurate, unbiased examples.
5. Measurement Bias
Measurement bias occurs when the way we measure data is flawed or unfair.
For example, if a job performance score is based only on sales numbers, it may undervalue customer service skills, creativity, or teamwork. In AI, this can lead to algorithms that favor one type of success over others.
Another example is using arrest records to measure crime rates—it ignores crimes that go unreported or under-policed. The AI ends up learning patterns that reflect the measurement tool’s limits, not reality.
To fix this, we need better, more holistic ways to measure success, so AI decisions aren’t built on narrow or misleading metrics.
6. Confirmation Bias in Data Collection
Sometimes researchers unintentionally collect data that confirms their existing beliefs.
For example, if a company believes that young people are better at tech jobs, they might only gather performance data from younger employees. This creates a one-sided picture that “proves” their assumption—then the AI happily reinforces it.
In predictive policing, focusing data collection on certain neighborhoods will confirm the false idea that those areas have higher crime, just because that’s where police are looking. This bias is tricky because it feels logical to the data collector.
The fix? Actively seek out data that challenges assumptions, not just data that supports them.
7. Feature Selection Bias
Feature selection bias happens when the wrong variables (or features) are chosen for training.
For instance, if a university admissions model uses “distance from campus” as a factor, it might unintentionally disadvantage rural students.
In healthcare AI, including “zip code” as a feature could encode socioeconomic and racial biases without anyone realizing. Sometimes these features seem harmless but carry hidden signals about race, gender, or income. This bias can sneak in when teams pick features without deeply analyzing their social implications.
The solution is careful auditing of chosen features to ensure they’re relevant, fair, and not secretly acting as proxies for protected attributes.
8. Algorithm Design Bias
Sometimes the algorithm’s design itself introduces bias. For example, a recommendation system might prioritize engagement, leading to clickbait or polarizing content because that’s what gets the most clicks.
In facial recognition, using a model architecture that isn’t well-suited for a wide range of skin tones can lower accuracy for certain groups. This type of bias isn’t about the data—it’s about the assumptions baked into the code and formulas. Developers may unknowingly make design choices that favor one group over another.
The fix involves testing multiple algorithms, comparing performance across groups, and choosing designs that work well for everyone.
9. Feedback Loop Bias
Feedback loops happen when biased outputs from an AI system influence the data it later learns from.
For example, if a predictive policing system sends more officers to a certain neighborhood, more crimes will be recorded there—not necessarily because crime is higher, but because more police are present. That new “evidence” then reinforces the AI’s belief, creating a cycle.
In hiring algorithms, if certain applicants are repeatedly rejected, fewer of them will apply, and the system will have less data about them—reinforcing bias.
Breaking feedback loops requires constant monitoring, using external checks, and updating training data to prevent self-reinforcing prejudice.
10. Lack of Diversity in Development Teams
Bias often slips in when the people building the AI all share similar backgrounds. A team made entirely of young engineers from the same country might overlook cultural nuances or issues that affect other groups.
For example, early voice assistants struggled with accents simply because their creators didn’t test them widely. If a diverse team had been involved, they might have spotted the problem sooner.
Diverse perspectives help challenge blind spots in data collection, feature selection, and testing. It’s not just about fairness—it’s about building better, more accurate AI that works for everyone, regardless of language, culture, or appearance.
Read Here: How to Identify Algorithmic Bias in AI Systems in 2025
Conclusion: Building Fair and Trustworthy AI
Algorithmic bias in machine learning isn’t just a technical glitch—it’s a reflection of the data, choices, and assumptions that go into building AI.
From biased training data to lack of diversity in development teams, each cause reveals how human influence shapes machine decisions.
The real challenge is that bias often hides in plain sight, buried in datasets, feature choices, or even the way problems are defined. Left unchecked, it can lead to unfair treatment, missed opportunities, and mistrust in AI systems.
The good news is that bias can be reduced with conscious effort—by collecting better data, testing across diverse groups, and including multiple perspectives in the design process.
Creating fair algorithms isn’t just a responsibility for developers; it’s a shared mission to ensure technology reflects the values of equality, accuracy, and fairness for everyone it impacts.
Read Also: How to Mitigate Algorithmic Bias in AI Models Effectively