Recognising and Addressing Bias

Bias is a tendency to favour or reject certain ideas, viewpoints, or individuals unfairly or uncritically. Everyone has biases — acknowledging and addressing them is key to strong academic work.

Even peer-reviewed academic sources can contain bias — particularly in how studies are designed, funded, or interpreted.

Watch for:

• Methodological bias – e.g. small sample sizes, selective data reporting

• Disciplinary bias – e.g. favouring certain theoretical frameworks over others

• Publication bias – e.g. only publishing positive results, excluding null findings

Tip: When reviewing studies, look for transparency in method and funding, and compare findings across multiple sources to spot inconsistencies or gaps.

AI tools can reflect and sometimes amplify biases present in the data they were trained on. This means outputs may:

• Over-represent dominant perspectives (e.g. Western-centric or majority cultural views).
• Reproduce stereotypes relating to gender, race, disability, or other social groups.
• Work unevenly across subjects — more reliable in well-published areas, less so in niche or emerging fields.
• Simplify or distort debates by leaving out alternative or minority viewpoints.

Safe action: Apply the same critical evaluation skills you use for other sources — check for missing perspectives, compare across multiple sources, and question whether the AI is oversimplifying complex issues.

For fuller guidance, see our page on AI Risks and Limitations.

Here are some examples of how bias might appear in different subject areas:

Legal Studies: Case selection may reflect jurisdictional or doctrinal bias — consider whose interpretation is being privileged.

Creative Practice: Cultural or aesthetic biases may affect how design trends or artistic works are evaluated.

Applied Fields (e.g. Business, Computing): Be aware of industry bias — favouring particular vendors, tools, or methodologies due to commercial influence or dominant paradigms.

Be self-aware:
Regularly reflect on your assumptions and how they influence your choices.

Seek alternative perspectives:
Intentionally explore viewpoints that differ from your initial opinions.

Evaluate sources critically:
Examine author backgrounds, funding sources, and publisher biases before accepting information as reliable.

Diversify your sources:
Aim to include multiple credible perspectives on any given topic.

Use objective criteria:
Evaluate evidence based on clear, consistent standards rather than personal preferences or pre-existing beliefs.

Engage in reflective practice:
Regularly reflect on why you chose specific sources and consider how different choices might change your conclusions.

Seek feedback:
Talk to peers, tutors, or writing support staff to uncover blind spots in your thinking. Others can help you spot assumptions you may have overlooked.

Ask yourself these questions when researching or writing:

• Have I sought evidence from multiple credible sources, even those challenging my initial views?
• How might my personal experiences or beliefs be influencing my interpretation?
• Are there credible arguments or evidence I might be unintentionally dismissing?

To build on your understanding of bias, explore these related guides:

• Understanding Misinformation and Disinformation: Spot false or misleading content and avoid being misled
• Evaluating Information: Develop broader critical evaluation skills for academic study
• Critically Evaluating Generative AI: Recognise hidden bias and misinformation in AI-generated content

This guide is part of our critical thinking and information evaluation series.