What is AI Bias? When Artificial Intelligence Goes Wrong
AI bias is the main flaw holding machine learning back from its full potential. Artificial intelligence could achieve 100% accuracy and fairness in theory, but one piece of imperfect data can compromise the whole algorithm.
In this comprehensive guide, we’ll cover:
Keep reading to learn more!
7 types of AI bias to know for 2025
AI bias can come in multiple forms, depending on the environment and the data humans feed into the algorithm. They all have the same result — create a disadvantage for a certain individual or demographic — but the process can look quite different.
These are the main types of AI bias organizations have experienced from using machine learning algorithms:
- Historical AI bias
- Sampling AI bias
- Label AI bias
- Aggregation bias
- Confirmation bias
- Evaluation bias
- Data cannibalism
Keep reading to learn more about these different types of biases in AI!
1. Historical AI bias
The most common type of AI bias is historical bias. This problem arises when AI makes decisions based on old and outdated information. The greatest example of historical AI bias happened with ecommerce titan Amazon back in 2014.
Amazon’s recruitment team set out to build an automated hiring system that screened job applicants without human assistance. The hiring system used 10 years worth of data from previous applications to find the best candidates. It looked like a good idea on paper, but in practice it showed a noticeable bias for male applicants over females.
Since Amazon’s workforce primarily consisted of male employees, the algorithm kept selecting male candidates to interview for open positions.
Although the algorithm correctly observed that males were the majority, it drew the incorrect conclusion that males were the preferred choice. It made the candidate’s gender the deciding factor instead of their skills and experience. Amazon scrapped the project in 2015 after receiving many complaints about discriminatory hiring practices.
So, if AI is working with old or outdated data, it can lead to incorrect interpretations and conclusions. As a result, it can negatively impact the information it generates and deliver results that don’t align with current practices and information.
2. Sampling AI bias
Sampling AI bias is similar to historical AI bias because it tends to overrepresent or underrepresent a specific group, but the source of the problem is different. Sampling bias occurs during the algorithm’s early development when it receives a new piece of misleading data that skews its perception of reality.
A great example of this problem has appeared with the development of speech recognition technology. As various businesses created their own speech-to-text tools, they primarily used voices from relevant audiobooks. However, most of these audiobooks were voiced by older white males, leading the algorithms to develop a bias for those voices.
As a result, speech recognition tools were more accurate translating English over other languages, accents, and speech patterns. They also responded better to male voices over females and white speakers over other racial demographics. A seemingly harmless data entry during the initial training phase snowballed into a widespread trend of discrimination.
So, if the pool of data AI pulls from isn’t diverse, it can lead to incorrect interpretations and biases towards certain types of data.
3. Label AI bias
Machine learning algorithms might be increasingly independent, but they still need humans to label the data and paint a clear picture. Unlabelled data is useless because it doesn’t provide any relevant context. When an algorithm receives data with incomplete labels, it malfunctions and can’t perform its assigned task.
You should already be familiar with a common example of AI label bias. Whenever a website asks you to “prove you’re not a robot” with a visual recognition puzzle, you are being tested for label bias. Humans can easily identify which squares below contain traffic lights, but AI can’t because it doesn’t have enough labeled data to pinpoint specific objects.
The most common outcome of label bias is a problem called AI hallucination in general-purpose language models like ChatGPT. When the model doesn’t have properly labeled data, it fills in the knowledge gaps with anything it can find. It will even fabricate facts and statistics out of thin air when nothing else is available.
As a result, this AI bias can lead to misinformation and made up information that people believe is real or accurate.
4. Aggregation bias
The next type of AI bias is aggregation bias. AI developers often aggregate data from different sources when building new machine learning models or testing old models. Data aggregation makes the learning process simpler, but it can lead to bias in some circumstances. A great example of aggregation bias occurs when comparing the salary of different careers.
As a general rule, an employee’s salary increases the longer they work in the same career. More experience leads to bigger paychecks. This principle holds true for many general industries, with one big exception — sports. Professional athletes make the most money early in their careers — their compensation decreases as they physically decline because they can’t perform as well.
However, if we aggregated all of this information into one algorithm, it would develop a bias against athletes. Any career that goes against the overarching trend will receive a negative bias. In the real world, aggregation AI bias can paint a misleading picture for people mapping out their career paths or employers determining fair salaries.
5. Confirmation bias
Confirmation bias is a type of AI bias you might know from another context. In a nutshell, it’s our natural tendency to trust information that confirms our existing beliefs. People from all backgrounds are guilty of confirmation bias when analyzing political and social issues. Machine learning algorithms commit the same logical fallacy all the time.
AI confirmation bias often appears in the healthcare industry, which draws concern because it can put people’s lives in jeopardy. For example, a doctor can make a patient diagnosis that contradicts an algorithm’s diagnosis. Both parties can be guilty of confirmation bias depending on the situation.
The doctor’s diagnosis might have confirmation bias because it doesn’t account for new research about the illness or injury. The algorithm might have confirmation bias because it doesn’t account for the patient’s unique symptoms and underlying health issues. In either case, the guilty party made a diagnosis based on their preconceived notions.
Keep in mind, too, that AI programs are fed data by humans — humans naturally have biases and beliefs that differ from others. So, people can influence the data that trains the AI programs, which can ultimately lead to AI bias because it’s not trained on diverse data and information.
6. Evaluation bias
Evaluation bias is a difficult type of AI bias to avoid during the training phase. All machine learning models are optimized by adding more data, and the data often includes benchmarks or key metrics. Bias can appear when these benchmarks only represent a small fraction of the environment instead of the general population.
For example, let’s say you create a voter analysis algorithm that correctly predicts a local election. The model should also be able to predict a national election, right? Wrong. Local elections and national elections might have a similar structure, but the voting patterns can look quite different. Your hometown behaves differently from other voting districts.
Because of your evaluation bias from the local election, you falsely assumed the algorithm would work on a larger scale. This assumption goes against the nature of artificial intelligence. AI doesn’t perform with the same accuracy when placed in unfamiliar settings. Just like humans, it needs time to gather information and adapt to its surroundings.
7. Data cannibalism
Data cannibalism is a different type of AI bias compared to the other types mentioned above. As AI-generated content takes up more space on the internet, new algorithms will recycle more data from older models. Over time, the content will cannibalize itself and become more robotic, creating an internal bias for artificial content over content made by humans.
This phenomenon is also called “model collapse” in the language of AI developers. Different generative AI models will keep sharing data with each other through the Internet until no factual information remains. It’s like a big game of telephone, except the players are content-creating robots instead of real people.
How can we reduce AI bias?
Reducing AI bias is a crucial part of unlocking the full potential of machine learning. It will also play a huge role in gaining people’s trust. Even though both humans and robots can be prejudiced, the public still prefers human intelligence over artificial intelligence because they know a real person is behind the scenes.
So, what are some effective steps to reduce AI bias? We’ve outlined a four-step blueprint that organizations can use to make their AI tools more trustworthy:
1. Determine the risk of bias
The first step to reduce AI bias is a precautionary approach. Developers must determine the risk of bias over time as they add more data to machine learning models. That means they need to test each dataset during training and see if it’s large and representative enough to prevent the various types of AI bias.
Another effective risk assessment strategy is “subpopulation analysis”, which involves calculating model metrics for different demographics within the data. Developers can use this strategy to ensure the model performs consistently across all subpopulations. Amazon’s hiring algorithm could’ve been a huge success if it included subpopulation analysis.
2. Use real and synthetic data
Testing algorithms in real-life and simulated environments will also help developers reduce AI bias over time. AI needs to have intimate knowledge of its environment, including the unique behaviors and backgrounds of each demographic. Testing in real-life settings can ensure fair representation of all groups.
However, real-world data sometimes contains unintentional human biases, so it’s important to add some synthetic data as well. Although it’s technically not real, it can still expose algorithms to more diverse perspectives and improve fairness for underrepresented groups. Generative adversarial networks (GANs) are the perfect platforms for creating synthetic training data.
3. Add fairness definitions to machine learning
Developers can also change the attitudes of machine learning models by adding fairness definitions to the algorithm from the very beginning. Instead of constantly monitoring potential biases, they can give AI a more human-like understanding of fairness and impartiality. They can make the algorithm account for differences in age, gender, ethnicity, and other characteristics.
This strategy is also known as “counterfactual fairness” because it helps the model make fair decisions for individuals of different backgrounds. The outcome of the decision is the same as it would be in a “counterfactual” world if the individual belonged to a different group.
4. Continue to promote DEI initiatives
If we want to eliminate AI bias altogether, we must also eliminate bias in the real world. That’s where the promotion of diversity, equity and inclusion (DEI) comes into play. By improving representation in media, workplaces and industries, we are helping algorithms develop the same priorities. DEI helps us change AI’s perception of its environment.
Let’s go back to Amazon’s hiring algorithm example again. If Amazon prioritized DEI in the hiring process and had a diverse workforce to begin with, the algorithm never would have developed a bias for male candidates. Similarly, if audiobook narrations included a more diverse cast of voices, speech recognition tools wouldn’t have a bias for English male voices.
Will AI bias ever go away?
Unfortunately, AI bias will never go away as long as machine learning relies on humans for information. Data only tells a fraction of the story and doesn’t provide full context. Human prejudices are always present in human-generated data, no matter how impartial we try to be. Imperfect information leads to imperfect results.
Reducing AI bias can improve AI’s objectivity in certain environments, but it won’t solve the problem entirely. AI bias will remain a persistent problem for developers to overcome as models get more advanced. That doesn’t mean machine learning will become obsolete, but it will prevent AI from expanding to other applications.
Get help with using AI in your strategy
Despite the reasonable concerns about AI bias, AI-generated content is still usable for a wide range of applications. Be responsible with the technology, stay mindful of potential biases, and remember the bias reduction strategies we discussed earlier.
If you want to learn more about AI and machine learning, WebFX has a wealth of AI solutions for you to explore. You can also contact us online and chat with our team of experts!