Bias (AI/ML)

Bias in AI and machine learning means a model consistently favours some outcomes and quietly deprioritises others in ways that do not reflect reality or fairness. It usually comes from the data the model learned from, the way that data was labelled, or the choices made when building and deploying the system.

How it works

Models learn patterns from training data. If that data under-represents a group, over-represents one class, or reflects past human decisions, the model reproduces the skew. NIST groups AI bias into three broad sources: systemic (built into institutions and historical data), statistical and computational (sampling and modelling issues), and human (how people label, interpret and use outputs).

In the Introduction to Machine Learning for SEO course, Lazarina gives familiar examples: job adverts shown mostly to users a platform identified as male, facial recognition that performs worse on certain skin tones, and generative image models that produced results unrepresentative of the real world. A more everyday case is class imbalance: when one group dominates the data, a classifier over-predicts that majority class.

How it’s used in SEO and marketing

You rarely use bias; you watch for it. It shows up in:

• A “will this page rank?” classifier that learns to say no to everything because few pages in the data rank in the top 10.
• Sentiment or entity APIs that perform worse on slang, non-English text or industry jargon.
• Customer segmentation or lead scoring that excludes groups underrepresented in past customers.
• Generated copy and images that default to stereotypes.
• AI search answers that favour already well-known brands.

Example

To reduce bias when training your own model, the course recommends a large and representative dataset, a held-out portion for testing, and deliberately including a range of backgrounds and cases, even using synthetic data where needed. With a pre-trained model such as Gemini or ChatGPT, you can steer it with prompts that include counter-examples to its bias.

Limitations and things to watch

• Bias cannot be removed entirely. It has to be measured, reduced and monitored.
• Overall accuracy can hide poor performance on a subgroup. Evaluate per segment.
• Removing a sensitive field does not stop a model from inferring it from correlated data such as postcode.
• Document data sources and known limitations as part of AI governance.

Learn it on MLforSEO

• The Characteristics of a Machine Learning Problem (section “Do not forget bias”).
• What Machine Learning Classification Is, and Where It Fits in SEO covers imbalanced data.
• Introduction to Machine Learning for SEO course, lesson on ML solution characteristics.