Cognitive bias in AI refers to systematic patterns of deviation from rationality that are introduced into AI systems through training data, algorithmic design, or developer assumptions. These biases often reflect human errors in judgment or decision-making that have been unknowingly transferred into machine learning models.
This topic matters because bias embedded in AI systems can reinforce inequality, create unfair treatment, and reduce trust in automated decisions. For AI governance and risk teams, detecting and correcting cognitive bias is key to maintaining fairness, accountability, and regulatory compliance—especially under laws like the EU AI Act or standards such as ISO/IEC 24029.
“78% of AI professionals say they are worried about bias in AI systems, yet only 24% of teams routinely test for it.”
(Source: World Economic Forum, Global AI Survey 2023)
What cognitive bias means in AI systems
Cognitive bias in AI does not only mean statistical unfairness. It includes human-originated thinking patterns like confirmation bias, anchoring, or framing effects that influence how algorithms are created, trained, or interpreted. If not managed, these biases can scale rapidly and invisibly.
For example, if a model is trained on hiring data that favors candidates from certain schools, it may learn to prioritize those profiles unfairly. This mirrors confirmation bias from past hiring managers.
Types of cognitive bias in AI
Some of the most common types of cognitive bias that affect AI development include:
-
Confirmation bias: Models trained on narrow datasets may reinforce existing patterns or beliefs.
-
Anchoring bias: Early data points or assumptions during development shape later decisions disproportionately.
-
Availability bias: Over-reliance on data that is easy to collect, but not necessarily relevant or complete.
-
Framing effect: Model outcomes depend on how input is phrased, leading to different results for similar queries.
-
Selection bias: Training data does not represent the full spectrum of real-world cases.
These biases impact fairness, safety, and effectiveness—especially in high-risk fields like health, finance, or criminal justice.
Real-world examples of AI bias
In 2019, an algorithm used by hospitals in the United States was found to refer white patients for extra care more often than Black patients, even when they were equally sick. This happened because the model used healthcare spending as a proxy for health status—a variable shaped by historical access disparities.
Another example comes from facial recognition systems that show higher error rates for darker-skinned individuals, as reported by the MIT Media Lab. This stems from skewed training data and flawed assumptions in model design.
Best practices to reduce cognitive bias
Bias can never be fully removed, but it can be identified and reduced through structured action.
Effective approaches start early in development and continue throughout a system’s lifecycle. Teams must think beyond technical fixes and question how decisions are made, who makes them, and which voices are missing.
Recommended practices include:
-
Audit datasets: Identify gaps, over-represented groups, or missing labels.
-
Diverse teams: Include people with different backgrounds to catch hidden assumptions.
-
Bias testing tools: Use open-source libraries like Fairlearn, AIF360, or What-If Tool.
-
Counterfactual testing: Check if changing sensitive inputs (like gender or race) affects outputs unfairly.
-
Document assumptions: Use model cards or datasheets to record model limits and known risks.
FAQ
What is the difference between algorithmic bias and cognitive bias?
Algorithmic bias refers to unfair patterns in outputs due to data or models. Cognitive bias comes from human thinking errors that shape how data is selected, models are built, or decisions are made.
Can bias in AI be removed completely?
No. Bias is part of both human and machine decision-making. The goal is to make it visible, measurable, and manageable—not to eliminate it entirely.
Are there laws that require bias checks?
Yes. The EU AI Act requires high-risk systems to include fairness and bias mitigation strategies. Other jurisdictions, like the New York City AI Bias Law, mandate impact audits for hiring algorithms.
Who is responsible for AI bias?
Responsibility is shared. Developers, product managers, data scientists, and business leaders must all check how their choices affect outcomes.
Summary
Bias in AI reflects human bias. But unlike personal judgment, it can scale across millions of users. Teams that take bias seriously early on reduce harm, increase trust, and build more reliable systems.
