What is AI Bias? How To Detect and Prevent AI Bias
AI Bias: Key Takeaways
What it is: AI bias occurs when AI systems make discriminatory decisions towards certain groups or individuals, based on the data they were trained on.
Why it matters: Biases can reinforce harmful perceptions and stereotypes, particularly toward marginalized groups.
Types and solutions: There are seven types of AI biases that you can work around by correcting with feedback or updated data.
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:
- What is AI bias?
- Why does AI bias matter?
- 12 types of AI bias in 2025
- Examples of AI bias
- How to reduce AI bias
What is AI bias?
AI bias refers to any situation where AI systems make unfair or discriminatory decisions, especially towards certain groups or individuals, based on the data they were trained on.
Why does AI bias matter?
Biased AI systems promote negative perceptions of marginalized groups, leading to further inequity and stereotyping in society. AI biases can cause models to:
- Reject qualified applicants based on their race or gender
- Misidentifying faces and people, causing legal concerns
- Creating false diagnoses for medical patients based on invalid information
To prevent the spread of harmful, incorrect information, companies, programmers, and users must notice, report, and correct AI biases. Failing to do so can damage your reputation, alienate customers, and even lead to penalties.
12 types of AI bias in 2025
AI bias can come in multiple forms, depending on the environment and the data humans feed into the algorithm. Though the process can look different, they all have the same result — creating a disadvantage for a certain individual or demographic.
These are the main types of AI bias organizations have experienced from using machine learning algorithms:
- Algorithmic bias: Misinformation from an incorrect or unspecific prompt given to a machine learning algorithm.
- Cognitive bias: Personal bias derived from the human input that often affects the dataset or model behavior.
- Confirmation bias: A natural tendency to trust information that confirms existing beliefs.
- Exclusion bias: Occurs when important data is left out of the AI algorithm training, leading to blind spots and misinformation.
- Historical bias: Bias that occurs when AI makes decisions based on old and outdated information.
- Label bias: The presence of unlabeled or incorrectly labeled information and groups that causes errors in the model behavior.
- Measurement bias: Bias that is caused by an incomplete dataset.
- Out-group bias: Bias that occurs from not knowing enough about a particular group to provide accurate information on it.
- Prejudice bias: Bias that appears when stereotypes and negative assumptions exist in the algorithm’s dataset.
- Recall bias: Inconsistent labelling of information based on subjective observations that lead to biases.
- Sample/selection bias: Occurs during the algorithm’s early development when it receives a new piece of misleading data that skews its perception of reality.
- Stereotyping bias: Occurs when an AI system enforces negative stereotypes toward a particular group.
Examples of AI bias
So, what does AI bias look like in practice?
Below are a few examples of AI bias that have affected real people and businesses.
Generative AI image biases
One study from 2024 found that DALL-E Mini produced images that reinforced gender and racial stereotypes based on occupation.
For example, when asked to populate different professionals, it would create images representing only men (e.g., pilot, builder, plumber) or women (e.g., hairdresser, receptionist, dietitian). These images reinforce gender stereotypes surrounding career paths.
The same issue occurred with race. DALL-E Mini populated images of White people (e.g., farmer, painter, prison officer, software engineer) and very few by non-White people (e.g., pastor, rapper).
With these results, it’s clear that the algorithms still need some work to reduce those biases.
AI bias in healthcare
Ai bias in the healthcare industry can put people’s lives in jeopardy. For example, a 2024 study into AI algorithms in healthcare risk assessment showed that bias in AI resulted in:
- Worse performance in detecting valvular heart disease in Black patients
- Underestimated risks in racial or ethnic minorities, women, and lower-income individuals
- Lower recommendations for care for Black patients compared to White patients
These systems still require work and training to remove racial bias and ensure each patient receives the best healthcare solutions available, regardless of race, sex, or socioeconomic factors.
AI bias in Amazon’s hiring algorithm
In 2018, it was discovered that Amazon’s hiring tool, which was trained to scan résumés and find the right candidate, did not like women. That AI bias led to a large oversight in the hiring process that became unfair to all female applicants.
Amazon’s model was trained on resumes submitted to the company over a 10-year period. Since the industry is already male-dominant, the tool found a simple pattern — men were perceived as better fits for the role. Any résumé with the word “women” was penalized and removed.
Though this example is older, it shows the real implications of how AI bias can affect people’s lives.
How to 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 concrete steps that companies can use to make their AI tools more trustworthy:
- Test and audit AI models
- Diversify your datasets
- Add fairness definitions to machine learning
- Document model decisions
- Stay ahead of regulations
1. Test and audit your AI models
To reduce AI bias, 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.
Aequitas, Fairlearn, and AI Fairness 360 are popular tools for detecting AI biases.
2. Diversify your datasets
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. Document model decisions
To make it easy to track and monitor biases in AI training, you should document all decisions related to the model training. This transparency will help companies and AI developers track any issues in the datasets and training decisions.
5. Stay ahead of regulations
As AI becomes more integral with all aspects of society, regulations are emerging to help businesses and consumers. Some current examples in 2025 are:
- AI Executive Order (October 2023): President Biden issued this order to set national standards for AI safety, civil rights, and equity to be enforced by the Department of Justice and other agencies. These organizations must enforce existing civil rights laws when AI leads to discrimination.
- AU AI Act (May 2024): This act is the first comprehensive law regulating AI. The goal of this law is to organize tools based on their risk factor and evaluate high-risk systems with assessments.
- Equal Employment Opportunity Commission (EEOC) regulations for hiring: According to rhe EEOC, companies can be held liable for biased AI hiring tools under Title VII of the Civil Rights Act.
Be sure to align with these standards however necessary to prepare your company for the future.
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 about using AI in your strategy!