Are AI chatbots biased?

Are chatbots biased? I'll check recent evidence on how bias appears and what studies find.

Yes, AI chatbots can be biased — multiple independent studies in 2024-2026 find systematic patterns in outputs — but the bias is not uniform across all models, topics, and prompts.

What "biased" means for a chatbot

Researchers define a biased output as one that deviates from the truth, often systematically reflecting the views, assumptions, or preferences of a certain group of people, a particular culture or custom, or a specific ideology.

In this empirical sense, bias does not require a model to have beliefs :

> AI chatbots are biased, if "bias" means a systematic tendency in outputs rather than a conscious political intention.

> It does not require proving that a model has beliefs. A chatbot does not "believe" in liberalism, conservatism, or centrism. It generates text from training data, alignment procedures, system instructions, retrieval results, and user prompts.

Types of bias documented

> AI chatbots may exhibit various forms of bias, including gender bias—such as reinforcing stereotypical assumptions about men and women's occupations; political bias—for example, ChatGPT leans liberal, and GPT-4's responses tend to align more with left-wing values, showing a significant and systematic political preference toward the Democratic Party in the US; racial bias—reflected in prejudices against certain ethnic groups

Other reviews add:

•Political bias occurs when models favor particular ideologies, parties, or perspectives, whether through word choice, framing, or omission of facts.

•Gender and racial bias are persistent and often produce outputs that reflect and amplify prejudices encountered in society. For example text generators may suggest traditionally male roles for leadership positions.

•Cultural, national, religious/worldview biases, and dialect and health-condition biases have also been identified.

What recent evidence shows

1. Political left-of-center tilt

> The best recent evidence points to a left-of-center tilt in many major models on U.S. and European political questions.

This consensus has been supported by multiple academic and institutional studies throughout 2024 and 2025, though the reasons are rooted in technical architecture and data sourcing rather than a centralized political agenda.

Key studies:

•Large human evaluation (Westwood, Grimmer, and Hall, 2025): 180,126 paired judgments from 10,007 U.S. respondents, testing 24 large language models on 30 political topics. It found that nearly all evaluated models were perceived as significantly left-leaning.

•PoliticsBench, 2026: Tested eight prominent LLMs in multi-turn roleplay. Seven of eight models leaned left; Grok leaned right.

•Washington Post, June 2026: Tested major chatbots on political questions and found OpenAI's GPT model provided exclusively left-leaning arguments in 80% of its responses, while Google's Gemini 3.1 Pro presented both sides of the debate in over 90% of its answers. A summary of the same test notes the model powering ChatGPT gave almost exclusively left-leaning arguments, while Gemini usually presented both sides in more than 90 percent of its answers.

•European tests: Larger models such as Llama3-70B aligned more closely with left-leaning parties; smaller models often stayed neutral, especially in English.

Researchers warn there is no truly neutral AI, and that users tend to absorb a model's bias after just a few interactions.

2. Other measurable biases

•Health and stigma bias: When asked directly using standard stigma questionnaires, models including ChatGPT, Grok and Claude scored better than humans, showing less obvious bias. However, when researchers gave contextual judgment tasks, the models were 13 to 17 times more likely to produce a negative story for a character with health conditions than for a healthy one and .

•Dialect bias: In 2024, Berkeley researchers tested ChatGPT's responses to varieties of English dialects from places like India, Ireland and Nigeria. Compared to American or British English, responses to dialects showed increases in stereotyping (18 per cent worse), demeaning content (25 per cent worse) and condescending responses (15 per cent worse).

•Bias against vulnerable users: A study by researchers at MIT Media Lab found that state-of-the-art chatbots — including GPT-4, Claude 3 Opus, and Llama 3 — sometimes provide less-accurate and less-truthful responses to users who have lower English proficiency, less formal education, or who originate from outside the United States.

•Stereotype reasoning: A Rutgers study in Nature Machine Intelligence found that although LLM outputs are increasingly aligned with human values, the processes by which they reason are often rooted in deep-seated stereotypes.

3. Bias can influence users even when not intended to persuade

A Yale-led study published March 3 in PNAS Nexus tested factual summaries:

> Prior research has shown that content generated by AI that has been prompted to be persuasive can indeed shift people's opinions. But this study provides evidence that the same is also true of content that is not intended to change minds, such as the summaries that popular chatbots produce in response to simple queries about historical events.

> This unintended power to persuade is caused by latent biases introduced during the training of the large language models (LLMs) that drive chatbots' core capabilities... Those latent biases — which can carry over from ideological leanings in the data used to train LLMs — lend subtle nuances to the framing of the narratives the chatbots generate.

In a preregistered experiment with 1,912 participants, both the default AI summaries and those prompted to have a liberal framing caused participants to express more liberal opinions about historical events compared to Wikipedia entries. Default GPT-4o historical summaries moved readers toward more liberal opinions than Wikipedia summaries, even though the content was factually accurate.

Where does the bias come from?

Bias can enter at several stages: training data, data filtering, post-training alignment, reward modeling, system prompts, retrieval sources, product policy, and even the user's own wording.

•Training data: AI models' biases largely stem from the data on which they are trained, which is a biased subset of human knowledge drawn largely from the internet.

•Alignment / RLHF: A notable source arises from human feedback, where the model is further fine-tuned with human-labeled preference data; this dataset may itself be biased and minority preference may be overlooked during training. Researchers also noted a model can be biased without a developer trying to make it biased.

Nuance: not every answer is biased

Researchers emphasize:

•Model differences are large — e.g., Gemini often gives both sides while ChatGPT was more one-sided in 2026 tests.

•Bias varies by topic, language, scale, and guardrails. Bias was strongest on topics such as climate, energy, and labor unions, and weakest or reversed on topics such as taxes and the death penalty.

•"Neutrality itself is hard to define in politics".

In short, studies consistently detect systematic tilts — most prominently a left-liberal leaning on U.S. political questions, plus gender, racial, cultural, dialect, and health-related biases — but the magnitude and direction depend on which chatbot you use and how you prompt it.

Citations (3)

1springer.comAcademic
Ask ChatGPT: caveats and mitigations for individual users of AI chatbots | AI and Ethics | Springer Nature Link
Backs: “Yes, AI chatbots can be biased — multiple independent studies in 2024-2026 find systematic patterns in outputs — but the bias is not uniform across all models, topics, and prompts. What "biased" means for a chatbot. Researchers define a biased output as one that deviates from the truth, often systematically reflecting…”
2etcjournal.comNews and media
AI Chatbots Are Liberal-Left ‘Biased’
Backs: “Researchers define a biased output as one that deviates from the truth, often systematically reflecting the views, assumptions, or preferences of a certain group of people, a particular culture or custom, or a specific ideology. In this empirical sense, bias does not require a model to have beliefs : AI chatbots are…”
3yale.eduAcademic
AI’s hidden bias: Chatbots can influence opinions without trying
Backs: “Prior research has shown that content generated by AI that has been prompted to be persuasive can indeed shift people's opinions. But this study provides evidence that the same is also true of content that is not intended to change minds, such as the summaries that popular chatbots produce in response to simple…”