Artificial intelligence has become one of the most influential technologies shaping Britain’s economy, culture, and democratic life. Among these technologies, ChatGPT stands out as the most widely used conversational AI, integrated into schools, workplaces, newsrooms, and even healthcare settings. But as the UK public increasingly relies on AI-generated information, the question grows louder: Is ChatGPT truly fair? And, more importantly, how should Britain think about algorithmic fairness in the age of generative AI?
As a member of a UK academic committee focused on AI governance, I have spent considerable time reviewing the evidence, listening to expert testimony, and evaluating real-world consequences. This commentary aims to provide a comprehensive, accessible exploration of ChatGPT’s potential biases, the broader challenge of algorithmic fairness, and what the UK can do to ensure AI serves the public good.
The goal is not to criticise or defend any particular system. Instead, it is to help British readers understand a nuanced and urgent issue—one that affects economic opportunities, political decision-making, media literacy, healthcare access, and the everyday ways we interact with technology.
Bias is not new. Human decision-makers—judges, employers, teachers, police officers—have always had biases. But AI introduces something fundamentally different: bias at scale. When millions of people consult ChatGPT for advice, explanation, or interpretation, even small distortions can produce large societal effects.
Job applications: AI writing tools influence CVs, cover letters, and candidate screening.
Education: UK students use ChatGPT for essays, revision, and exam preparation.
Healthcare: Doctors increasingly rely on AI to summarise research or generate patient-facing explanations.
Policing and law: AI is used for risk assessments, predictions, and analysis of evidence.
Media and politics: ChatGPT can shape public narratives about elections, conflicts, and policy debates.
In these contexts, fairness is not an abstract ideal. It affects who gets a job interview, who receives certain medical advice, how communities are represented, and which political views appear most credible.
If bias exists—even unintentionally—it becomes a public concern.
Bias in AI can mean many things, so it’s helpful to break it down into clear categories.
ChatGPT learns from vast amounts of text on the internet. This data contains:
historical inequalities
cultural stereotypes
political biases
overrepresentation of certain voices
underrepresentation of others
When the training data is skewed, the model will inevitably reflect that skew.
Even when trained on balanced data, the mathematical mechanisms underlying AI can amplify patterns in surprising ways. AI does not “understand” fairness; it learns what is statistically common.
The way users prompt ChatGPT can influence outcomes:
leading questions
emotionally charged phrasing
political framing
assumptions baked into queries
This creates a feedback loop, where public expectations shape model behaviour.
AI systems designed for one purpose may act differently in another context—for example, when a large language model meant for general dialogue is used as a recruitment assistant or psychological counsellor.
AI does not operate in a vacuum. It operates in Britain—a society with its own legacies of class inequality, racial disparities, regional divides, and political polarisation. AI reflects all of these unless actively corrected.
Research from universities, independent labs, and civil society organisations (including several based in the UK) has documented recurring challenges. These do not indicate intentional wrongdoing but are structural issues inherent to large-scale AI.
Studies show that generative models often appear:
socially liberal
economically centrist or left-leaning
more receptive to progressive framing
This can affect:
political education
media narratives
debates about immigration, welfare, or healthcare
For UK readers, this is particularly relevant in an election year.
AI may:
generate stereotypical job descriptions
suggest different career paths for men and women
respond differently to identical CVs with gender-swapped names
Examples include:
assumptions about crime patterns
biased descriptions of communities
different politeness levels based on perceived ethnicity
uneven representation of accents or dialects (e.g., Yorkshire vs. London vs. Nigerian English)
A uniquely British issue. AI may overvalue:
Received Pronunciation
London-centric language
professional middle-class norms
“standard” grammar use
This affects inclusivity for working-class users.
Systems may:
misrepresent certain conditions
offer unequal advice
reinforce outdated medical assumptions
Models sometimes over-correct:
refusing to generate content about some groups
being more protective toward certain identities
uneven enforcement of safety policies
This creates the impression of double standards, even when well-intentioned.
AI engineers can reduce bias but cannot completely eliminate it. There are five structural reasons for this.
The English-language internet overrepresents:
American perspectives
white male authors
Western political systems
Models absorb these emphases.
ChatGPT predicts words based on statistical likelihood. It does not reason about ethics, fairness, or justice. Even attempts to “steer” the model rely on human-defined rules that can themselves be biased.
Efforts to avoid harmful content sometimes create asymmetry. For example, AI may decline to discuss one group while freely discussing another.
Fairness itself is contested:
equal treatment of all groups
compensating for historical inequality
neutral tone
representation of diversity
People disagree on what fairness looks like, so AI cannot satisfy all definitions simultaneously.
What Britain considers fair in 2025 may differ from 2035. AI needs continuous revision to keep pace.
To illustrate the stakes, consider cases documented globally that echo emerging UK concerns.
Large companies used algorithmic filters that consistently downgraded CVs containing women’s extracurricular activities or women’s colleges. When generative AI is used to draft CVs or shortlist candidates, the issue compounds.
In some models, identical symptoms produce different recommendations depending on implied ethnicity or gender. This has been observed in chronic pain and cardiovascular risk evaluations—areas where UK health inequalities already exist.
Predictive policing systems in the US, UK, and EU have shown tendencies to reinforce existing policing patterns, affecting Black and minority ethnic communities disproportionately.
Analyses show that AI sometimes frames certain political ideologies more sympathetically than others. During UK elections, this could shape perceptions of legitimacy and credibility.
Generative AI used for emotional support can inadvertently reinforce stereotypes about men’s and women’s emotional expression.
These examples demonstrate that bias is not a technical quirk—it is a societal risk.
The UK is positioning itself as a global leader in AI safety:
The 2023 AI Safety Summit
New guidelines from the Information Commissioner’s Office
Cross-party efforts in Parliament
Active research hubs at UCL, Oxford, Cambridge, Edinburgh, and Bristol
The UK’s strength lies in combining:
rigorous academic expertise
robust regulatory traditions
public expectations of fairness
a diverse, multicultural society
However, Britain must confront pressing questions:
Most generative AI originates outside UK jurisdiction. This raises questions about:
cultural alignment
political neutrality
representation of British values
Transparency is currently voluntary. A regulatory requirement could help.
This could reduce overdependence on US-centric data.
Approaches include:
demographic parity
equality of opportunity
equalised odds
representational fairness
harm-based metrics
Each has trade-offs.
One of the most subtle challenges is invisible bias—bias that is not obvious from isolated responses but emerges over hundreds of interactions.
Examples include:
tone differences
assumptions about lifestyle
which examples are chosen
which stories are referenced
use of London-centric or American-centric metaphors
slight differences in encouragement or discouragement
Invisible bias is powerful precisely because it is hard to detect. For the average UK citizen, it may feel like “the model is neutral” when, in fact, patterns shift depending on identity cues.
Attempts to debias AI involve several methods:
But removing biased data also removes important historical context.
Humans rate responses, but raters have biases too—often aligned with specific cultural or political norms.
These can reduce harmful stereotypes but risk feeling artificial or heavy-handed.
Effective for public awareness but does not fix underlying patterns.
Improves cultural balance but cannot cover all perspectives.
These tools help, but no single method solves the problem.
To ensure AI serves all British citizens, fairness must include:
Cultural plurality
Northern, Welsh, Scottish, Midlands, and London voices should be represented.
Class inclusivity
AI should avoid framing working-class English as “improper” or “incorrect”.
Political neutrality
No ideological preference should shape factual explanation.
Gender representation
Equal recognition of men, women, and non-binary people without stereotyping.
Racial and ethnic representation
Inclusion of Black, Asian, and minority ethnic perspectives.
Linguistic respect
Recognition of dialects: Scouse, Geordie, Yorkshire, Glaswegian, etc.
Media literacy support
Help users critically evaluate information rather than imposing opinions.
The goal is constructive neutrality—not avoiding difficult topics but addressing them responsibly and symmetrically.
Based on UK academic evaluations and global research, here are key national recommendations.
Establish a legal obligation for AI transparency reports on known biases.
Create British public datasets to complement global training data.
Fund independent algorithmic audits, with publicly accessible results.
Protect whistle-blowers and researchers who uncover harmful AI behaviour.
Support AI literacy programmes in schools, universities, and adult education.
Engage UK-based evaluators to capture British cultural nuance.
Provide balanced political framing around sensitive issues.
Offer tools for users to customise AI behavioural profiles.
Open-source more evaluation methodologies for academic scrutiny.
Track long-term societal impacts, not just accuracy metrics.
Use AI critically, not passively.
Compare multiple sources, especially when engaging with political or economic content.
Be aware of prompt framing, which can shape AI outcomes.
Give feedback—public input is vital to improving fairness.
Recognise that no AI is fully neutral, but better systems are possible through public pressure and informed debate.
This is a deeply philosophical question.
On one hand, complete neutrality may be impossible. Every choice—from training data to safety protocols—involves value judgements.
On the other hand, useful fairness is achievable. We can build systems that:
minimise harm
offer balanced perspectives
reduce stereotypes
serve diverse communities
adapt to new social realities
allow user customisation
The challenge is not to eliminate bias entirely but to ensure AI does not amplify existing inequalities or create new ones.
If the UK takes the lead in defining what algorithmic fairness should mean in a diverse, democratic society, it can set a global precedent. Britain has:
world-class universities
a strong legal tradition
an engaged public
a commitment to evidence-based policymaking
experience managing complex ethical questions in medicine, media, and science
AI should not be left to Silicon Valley alone.
Britain can shape a uniquely British model of AI fairness, grounded in:
pluralism
accountability
transparency
equality under the law
respect for difference
This will require:
public debate
political will
investment in research
responsible regulation
continuous auditing
But the outcome will be worth it: AI that reflects the best of Britain rather than its historical distortions.
ChatGPT is not an enemy. Nor is it a neutral tool. It is a mirror—reflecting both the strengths and flaws of the societies that built it. Bias is not solely a technical problem; it is a societal one. And that means the solution must be societal too.
As the UK continues its national conversation about AI, one principle should guide us:
AI must be fair not because fairness is easy, but because fairness is necessary for a just and democratic society.
The question is not whether ChatGPT is biased. All AI is.
The question is whether Britain will take the steps needed to shape AI in the public interest—responsibly, transparently, and inclusively.
The future of algorithmic fairness is not written in code. It is written in the choices we make today.
