Generative artificial intelligence, machine learning models, and large language models generate bias when they favor one social group over another (Jennings, 2025). For instance, in hiring practices, an AI model may favor applicants of a certain gender or race than others. Or, if an AI model has a specific political leaning, its responses could be tilted toward that political party. This is why identifying and correcting AI bias is crucial.
Below are some of the most common types of bias that occur in machines and humans:
- Data bias: Data bias happens when a distortion takes place in a dataset or data-generating process, and this leads the data to misrepresent the behavior, situation, or population it is meant to explain (MacPherson and Gaule, (n.d).
- Algorithmic bias: This type of bias occurs when an algorithm is pre-built with a slant towards a specific group or position.
- Cognitive bias: A cognitive bias is a mental shortcut people take when assessing a situation. Instead of looking at the objective facts, they form opinions based on what they already know, believe, and feel at the time.
- Confirmation bias: Finally, confirmation bias occurs when someone seeks out information that supports their current beliefs. This involves “cherry-picking” the facts they agree with and ignoring other, contrary evidence. This type of bias can occur in both machines and humans: if a machine is pre-fed biased information, then, naturally, it’s likely to seek out data that correlates with the evidence it has been given, even if that data isn’t complete or true.
We can see bias infiltrate many companies’ datasets in the real world. For instance, Amazon (Post #8: Into the Abyss, 2024) implemented an algorithm that favored male applicants over female applicants. This practice was unfair and violated the principle of fairness, so they had to take down the algorithm.
When discussing bias, it’s important to note the distinction between statistical bias and social bias. Statistical bias occurs when the math deviates from reality, while social bias occurs when an AI model perpetuates human prejudices and stereotypes (Emory and Grossman, 2026).
Understanding the difference between these two types of bias is crucial for determining the root cause of the bias. Only from there can we make a change.
Bias matters for business, ethics, and regulatory compliance because treating all customers and employees equally is a major part of what makes a socially responsible and equitable business. Not only is addressing and correcting bias the right thing to do — it affects people’s lives, so taking care to correct it is of utmost importance.
In this post, we’ll explore the following concepts as they pertain to bias in AI systems:
Common Sources and Causes of Bias in Generative AI
First, let’s discuss some of the common sources and causes of bias in generative AI.
To begin, training data can be biased to overlook underrepresented groups, reflect historical prejudices, or create sampling errors.
One example of this occurred when an AI tool called the COMPAS recidivism algorithm unfairly predicted that African American subjects were more likely to re-offend (Real Life Examples, (n.d).
This type of racial bias even dates back to the 1970s, when Dr. Geoffrey Franglen of St. George’s Hospital Medical School in London built a computer-based application vetter that docked 15 points off applications that included non-Caucasian names or places of birth (Schwartz, 2019).
These examples of historical prejudice are unjust and prove that AI is only as good as the data we give it (Schwartz, 2019).
Furthermore, model architecture decisions can amplify existing biases. According to Jennings, model architectures may easily emphasize co-occurrences in datasets, which makes biases more likely to perpetuate (Jennings, 2025). Also, feedback loops can reinforce existing biases over time.
Moreover, context and prompt engineering can also influence the creation of bias. If someone asks a leading question rather than an open-ended question in their prompt, they’re more likely to receive a skewed answer.
Finally, deployment environment factors can introduce new biases into the system. If the population that uses the AI model(s) differs from the population(s) the model(s) were trained on, errors can occur when the model(s) give information (Series of Thoughts, 2026).
Bias Detection Techniques and Methods
As you can tell, AI can demonstrate bias on numerous fronts. So, how do we spot it?
First, perform pre-processing detection by auditing training datasets for any representation gaps. To do this, you can use bias detection tools such as Fairness Indicators, AI Fairness 360, and the What-If tool (Kooistra, 2025).
Fairness Indicators evaluates binary and multiclass classifiers across several fairness metrics simultaneously and creates interactive visualizations that display the performance disparities in real time.
Fairness 360 includes over 70 fairness metrics and 10 bias mitigation algorithms and assists with both pre-processing and post-processing bias mitigation in many languages.
Finally, the What-If tool allows professionals to explore model behavior in various scenarios and demographic groups.
You also want to track statistical fairness metrics, such as the following:
Demographic Parity – Demographic parity represents the notion that positive prediction rates are equal across a group, such as in hiring practices (Kooistra, 2025).
Equal Opportunity– Equal opportunity occurs when a model’s predictions have the same true/false and positive rates amongst groups (Yardstick, (n.d).
Equalized Odds– Equalized odds means there are equal true/false positive rates across groups (Kooistra, 2025).
Throughout the process, you’ll want to monitor your model during training and fine-tune it as needed. In addition, test your model with diverse test sets to evaluate its fairness across sets, and test it with adversarial examples to ensure it’s safe from harm.
Red-teaming is especially important now that AI cyberattacks are increasingly common. Red-teaming is simulating an AI hacker to make sure your system is hacker-proof.
Test both manual and automated adversarial tools to safeguard your models. One open-source tool includes Microsoft’s PyRIT, as PaloAlto Networks suggests in a blog chock-full of tips about how to red-team (What is AI Red Teaming? (n.d.)).
Last but not least, it’s always a good idea to have a human in the loop and thoroughly review processes conducted by experts before and after deployment.
Industry Standards and Regulatory Considerations
Before you implement an AI bias detection program, make sure your practices comply with industry standards and regulations.
For starters, the EU AI Act outlines two requirements for high-risk AI systems, where at least one of the two must be met to qualify as high-risk:
“High-risk” is defined as falling under EU harmonization legislation and subject to conformity assessments per the legislation, or if it’s listed as one of the applications in Annex III and thus mandated to follow strict design criteria and operator rules.
The obligations these systems must follow are:
- Create a risk-management system.
- Upkeep appropriate data governance and management protocols.
- Outline a technical documentation.
- Keep records.
- Enforce transparency and the supervision of information to deployers.
- Keep an appropriate level of oversight.
- Ensure the system’s accuracy, strength, and cybersecurity.
Secondly, the National Institute of Standards and Technology shares guidelines organizations can follow to promote the ethical use of AI, such as by ensuring accountability and transparency: (National Institute of Standards and Techology, 2023.
Furthermore, IEEE and ISO offer similar ethical frameworks for AI use, as demonstrated in this video (YouAccel, (n.d.). The IEEE is the Institute of Electrical and Electronics Engineers, and the ISO is the International Organization for Standardization. Both entities offer ethical guidelines surrounding AI; however, the ISO provides a broader focus, whereas the IEEE focuses more exclusively on AI.
Organizations that followed ISO standards reported a 25% increase in compliance with regulatory mandates and a 30% increase in customer trust (YouAccel, (n.d.).
Also, a financial institution that followed the IEEE P7001 policy reported that when they implemented an AI system that explained its credit decisions, it enhanced customer trust (YouAccel, (n.d.). These findings imply that adhering to international and industry standards can help your audience trust your company more.
Finally, Article 22 of the GDPR states that individuals have a right not to be subject to decisions made solely based on automated outputs, such as profiling decisions made by AI: (Art. 22 GDPR, (n.d)).
It’s important to note that specific industries have special guidelines when it comes to AI.
In healthcare, professionals must continue to comply with HIPPA regulations when using AI. This looks like protection automation software with HIPPA encryption, for example (HEALTH CARE, (n.d).
In finance, professionals take consumer protections into account when using AI, along with typical policies of data privacy and security, transparency, and explainability (U.S Department of The Treasury, 2024).
Moreover, in hiring pratices, several states have laws to protect the fairness of AI use in the job application process. AI may be used to ask applicants questions through chat messaging, grade video submissions, or read and check resumes (Hoopai, (n.d).
Illinois, for example, requires companies to explain the use of AI in video applications, share how the AI works and how it will grade applicants, and ask applicants for consent (Hoopai, (n.d).
Last but not least, no matter what industry you work in, you’ll need to document AI compliance and audit trails so that everyone in your company can know who accessed what, and when.
Specifically, track:
- Access Attribution Records: This answers “who accessed what data, when, and through which system?”
- Policy Enforcement: Answers, “For each access record, was a policy reviewed, and if so, what did the policy allow or deny the user to do?”
- Data Asset Specificity Records: These indicate which data was accessed and at what timestamp. This specificity is crucial for creating accurate audit trails.
- Governance Policy Documentation: Answers, “What policies rule AI data access, when were these policies approved, and how are they enforced and communicated?”
Keeping detailed records of these AI data access milestones helps you keep track of your company members’ compliance with AI policies.
Implementing a Bias Management Program
A bias management program is a great way to keep your organization fair and equitable. It also further demonstrates your commitment to being socially responsible.
To create your own bias management program, commit to the following:
1. Build cross-functional teams with diverse perspectives.
When you have people from different industries and walks of life come together, they can offer AI improvements from various angles. For instance, someone from Japan may have a different worldview and perspective on an ad than, say, someone from the Netherlands, and all employees will have insights to contribute that better equip your business to serve both international and national audiences.
2. Establish governance structures and accountability.
Follow guidelines such as the EU AI Act and NIST AI policies to check that your AI systems are compatible with legal and ethical guidelines.
Also, be sure to follow the five principles that the IBM lists as key factors of ethical AI use: transparency, accountability, explainability, fairness, and data and privacy.
3. Create bias detection and mitigation workflows.
Set up a system to identify and prevent bias as soon as possible.
Perform model evaluation, monitoring, and auditing on a regular basis, and use fairness metrics to spot bias, such as demographic parity, equal opportunity, and equalized odds (MacPherson and Gaule, (n.d).
Furthermore, train your teams on bias awareness and mitigation techniques. Teach them real-life examples of how bias can show up across different demographic groups, and explain how the data can reveal bias. This can help them identify and respond to bias when it comes up.
4. Develop incident response procedures for bias issues.
To be able to address bias issues before they actually occur, your team needs a bias incident response plan.
Dr. Dédé Tetsubayashi recommends incorporating the following steps in your response plan:
- Detection: How can you address the situation proactively?
- Triage: How do you evaluate the incident by “severity, scope, duration,” and impacted population?
- Communication: Who is contacted? When?
- Containment: Can you halt the system while you look into the problem?
- Remediation: Includes the timeline for solving the issue, named owners, and follow-up testing details.
- Post-incident review: Asks, “What structural shift is needed to prevent this from happening again?
Measure ROI and effectiveness of bias management.
Finally, measure the ROI and effectiveness of bias management initiatives.
To do this, you can track leading indicator metrics like pre-shift checks completed, customer follow ups completed, daily prioritization review completed, and safety checks performed (Cohen, 2025),
Tracking these metrics helps you know that bias intervention checks are being performed to the best of your team’s ability. It also offers your team a way to hold themselves accountable to quality standards.
Conclusion
To wrap it up, bias can easily slip into your AI systems if they are not trained and tested on fair data. By creating and sticking to a bias incident response plan, your team can set itself up for success in treating all customers equitably.
To learn more about how we can help your team implement AI in a fair and balanced manner, visit www.psycray.com/contact-us/.
We look forward to learning more about how we can help you.
FAQ
Q: What is the difference between bias detection and bias mitigation in AI?
A: Bias detection identifies bias in AI systems, while bias mitigation involves taking active steps to prevent bias both before and after system deployment.
Q: Can you completely eliminate bias from generative AI models?
A: As long as humans are feeding the models (which they are), it’s likely not possible to entirely eliminate bias from generative AI models. However, we can do the best we can to feed generative AI objective data that treats all populations equitably, and test the models as we go to correct any biases that come up.
Q: What are the most common types of bias found in large language models?
A: Data bias, algorithmic bias, cognitive bias, and confirmation bias are the most common types of bias found in large language models.
Q: How much does bias detection and mitigation cost for AI projects?
A: The cost largely depends on the complexity of the project, but a typical AI auditing project can range anywhere from $5,000 to $25,000 (Patel, 2026), though this is a rough estimate and will vary from case to case.
Q: What tools can developers use to detect bias in their AI models?
A: Developers can use Fairness Indicators, AI Fairness 360, and the What-If Tool to assess whether data is favoring one group over another. If it is, the information is biased and can be adjusted as needed to be fair.
Q: How often should AI models be tested for bias?
A: We would suggest testing models quarterly, as data can change quickly. By keeping your model up-to-date on a seasonal basis, you can set yourself up to have accurate models throughout the year.
Q: Who is responsible for bias in generative AI systems?
In a sense, all of us are responsible. As Internet users, anything we post can be biased and then incorporated into a generative AI system.
A: This doesn’t mean we should stop posting; rather, we just need to be careful about the data these models are getting (and where they are pulling it from).
Q: What is the best approach for mitigating bias in existing AI models?
A: The best approach for mitigating bias in existing AI models is to keep testing and refining them; since data can change and AI can make mistakes, continuously adapting the model will help us prevent it from giving biased responses.
References
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