What is Bias in AI? Challenges, Prevention, & Examples

What is bias in AI? 

Bias in artificial intelligence (AI) occurs when models produce unfair or inaccurate results that reflect existing inequalities in the real world, leading to decisions that disadvantage certain groups. Common causes for AI bias include: 

• Skewed or incomplete training data 

• Human assumptions embedded in design 

• Deliberate manipulation of data or outcomes

According to the Center for Security and Emerging Technology’s AI Harm Taxonomy, the leading causes of bias in reported AI incidents include racial bias (19.6%), gender-based bias (9.6%), and biases related to religion (5.9%), nationality (5.9%), and disability (5.3%). 

Why bias in AI matters 

AI systems are increasingly involved in decisions that affect people’s everyday lives, from job applications to medical care to fraud detection. When those systems are biased, it causes real harm and consequences for individuals and businesses alike: 

• Unequal access: People may be unfairly denied jobs, loans, healthcare, or other essential services. 

• Erosion of trust: Public confidence in AI systems drops quickly when biased outcomes make headlines.

• Legal and compliance risks: Companies could face lawsuits, regulatory action, or brand damage when biased systems cause harm. 

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Types of bias in AI Systems 

AI systems can reflect (and sometimes amplify) real-world bias in different ways. Here are a few of the most common types:

Training data bias 

AI models learn from the data they’re trained on. If that data is incomplete or unbalanced, or reflects historical inequalities, the model can replicate those patterns in its predictions. In some cases, attackers may intentionally insert harmful or misleading data into public datasets, which can influence outcomes, especially when models rely on third-party sources or large language models (LLMs). 

Real-world examples: 

• Resume screening tools that favor one gender or race over another

• Credit models that offer less favorable terms to underrepresented groups

• Health diagnostics that miss key symptoms in certain populations

Algorithmic bias 

Bias can also arise from how an AI system is built or fine-tuned. This can include how it prioritizes data features, how prompts are handled (in the case of LLMs), or vulnerabilities to adversarial inputs. Even well-trained models can behave unexpectedly without proper testing and safeguards.

Real-world examples: 

• Facial recognition tools that misidentify individuals with darker skin tones

• Sentiment analysis tools that interpret facial expressions in an ethnocentric way 

• Predictive systems that flag people based on discriminatory policing patterns

Cognitive bias 

Humans are involved in every step of the AI lifecycle, from collecting and labeling data to interpreting outputs. That means our own assumptions can shape the way models perform, often unintentionally. Even attempts to “correct” bias can go too far, leading to distorted results.

Real-world examples: 

• Training datasets that overrepresent one demographic, leading to skewed results 

• Framing issues incorrectly (e.g., safety vs. fairness) during model evaluation

• Writing instructions that unintentionally encode social biases or stereotypes 

Case studies of bias in AI 

Bias in AI systems can lead to legal, ethical, and reputational consequences for the organizations involved. Here are a few notable examples:

Age discrimination in hiring 

The iTutor Group used AI software to screen job applicants, automatically rejecting women over 55 and men over 60. The U.S. Equal Employment Opportunity Commission (EEOC) filed a lawsuit, and the company ultimately settled for $365,000 to be distributed to affected applicants.

Bias in healthcare risk prediction 

A widely used healthcare algorithm applied to over 200 million patients was found to underestimate the care needs of Black patients. Because it used healthcare costs as a proxy for need, the model overlooked disparities in access, leading to unequal care recommendations. 

Gender and racial bias in image generation 

An AI avatar app created sexualized images of an Asian woman while rendering her male colleagues as confident professionals. Similar tools have repeatedly produced harmful or inappropriate content when depicting women, especially those from minority groups. 

Strategies for addressing and mitigating bias in AI 

Reducing bias in AI requires ongoing attention to how data is collected, models are built, and systems are evaluated. Here are a few proven strategies that teams can use to build more fair and trustworthy AI:

Adopt ethical AI frameworks 

Industry frameworks like the IEEE’s Ethically Aligned Design, the EU’s Ethics Guidelines for Trustworthy AI, and NIST’s AI Risk Management Framework offer practical guidance for building AI responsibly. These standards focus on fairness, transparency, and accountability, all areas where many organizations are still catching up. A recent study found that only 6% of U.S. CEOs have adopted formal ethical AI policies, highlighting a major gap in implementation.

Conduct regular audits and bias testing 

As AI systems become more complex and autonomous, it’s critical to test them regularly for unintended bias. This is especially true for agentic AI, which is a system made up of multiple components that act independently. Without frequent audits, it’s easy for small issues to compound into bigger problems. 

Use fairness metrics and diverse data 

Teams also need to measure how model performance varies across different demographic groups. Using fairness metrics and testing with diverse, representative datasets helps ensure that AI models serve the full range of users, not just the majority or most visible segments.

Prioritize transparency and accountability 

Explainability is a core tenet of ethical AI. When teams prioritize AI systems that clearly show how they make decisions, it becomes easier to detect bias, take responsibility for outcomes, and maintain trust with users and stakeholders. 

Keeping up with AI regulations 

As governments move quickly to regulate artificial intelligence, staying ahead of new rules helps organizations reduce risk, build trust, and demonstrate responsible AI practices. Some of the regions that have already introduced guidelines for AI use include:

• European Union: The EU AI Act, in force since August 1, 2024, introduces strict requirements for high-risk applications, including transparency, oversight, and prohibited use cases. Compliance deadlines will roll out through 2027.

• United States: Colorado’s Artificial Intelligence Act, taking effect in February 2026, also targets high-risk systems and emphasizes fairness and accountability.

• Singapore: The Infocomm Media Development Authority (IMDA) has proposed a flexible governance framework focused on generative AI, aiming to encourage innovation while promoting responsible development. 

Improving AI security with Snyk 

Building secure AI systems starts with secure code. As organizations adopt generative AI solutions for software delivery, developers are increasingly writing and integrating code without full visibility into its origins, risks, or potential for introducing bias. Research shows that more than a third of AI-generated code snippets contain known security issues across multiple languages. 

Snyk helps teams catch these issues as early as possible. By embedding security into the development process, Snyk allows developers to identify and fix misconfigurations, bugs, and vulnerabilities before they reach production. Snyk Code, powered by DeepCode AI, detects and auto-fixes insecure code up to 50x faster than manual reviews.

This shift-left approach helps teams stay compliant, reduce risk, and accelerate development, all while building applications that are secure by design. Learn more about how Snyk supports AI code security.