The Political Biases of ChatGPT

Abstract

Recent advancements in Large Language Models (LLMs) suggest imminent commercial applications of such AI systems where they will serve as gateways to interact with technology and the accumulated body of human knowledge. The possibility of political biases embedded in these models raises concerns about their potential misusage. In this work, we report the results of administering 15 different political orientation tests (14 in English, 1 in Spanish) to a state-of-the-art Large Language Model, the popular ChatGPT from OpenAI. The results are consistent across tests; 14 of the 15 instruments diagnose ChatGPT answers to their questions as manifesting a preference for left-leaning viewpoints. When asked explicitly about its political preferences, ChatGPT often claims to hold no political opinions and to just strive to provide factual and neutral information. It is desirable that public facing artificial intelligence systems provide accurate and factual information about empirically verifiable issues, but such systems should strive for political neutrality on largely normative questions for which there is no straightforward way to empirically validate a viewpoint. Thus, ethical AI systems should present users with balanced arguments on the issue at hand and avoid claiming neutrality while displaying clear signs of political bias in their content.

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Citation: Rozado, David. 2023. The
Political Biases of ChatGPT. Social
Sciences 12: 148. https://doi.org/
10.3390/socsci12030148
Academic Editor: Andreas Pickel
Received: 24 January 2023
Revised: 25 February 2023
Accepted: 28 February 2023
Published: 2 March 2023
Copyright: © 2023 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
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social sciences
Communication
The Political Biases of ChatGPT
David Rozado
Te P ¯
ukenga—New Zealand Institute of Skills and Technology, Hamilton 3244, New Zealand;
david.rozado@op.ac.nz
Abstract:
Recent advancements in Large Language Models (LLMs) suggest imminent commercial
applications of such AI systems where they will serve as gateways to interact with technology
and the accumulated body of human knowledge. The possibility of political biases embedded in
these models raises concerns about their potential misusage. In this work, we report the results of
administering 15 different political orientation tests (14 in English, 1 in Spanish) to a state-of-the-art
Large Language Model, the popular ChatGPT from OpenAI. The results are consistent across tests;
14 of the 15 instruments diagnose ChatGPT answers to their questions as manifesting a preference
for left-leaning viewpoints. When asked explicitly about its political preferences, ChatGPT often
claims to hold no political opinions and to just strive to provide factual and neutral information. It is
desirable that public facing artificial intelligence systems provide accurate and factual information
about empirically verifiable issues, but such systems should strive for political neutrality on largely
normative questions for which there is no straightforward way to empirically validate a viewpoint.
Thus, ethical AI systems should present users with balanced arguments on the issue at hand and
avoid claiming neutrality while displaying clear signs of political bias in their content.
Keywords: algorithmic bias; political bias; AI; large language models; LLMs; ChatGPT; OpenAI
1. Introduction
The concept of algorithmic bias describes systematic and repeatable errors in a computer
system that create “unfair” outcomes, such as “privileging” one category over another
(Wikipedia 2023a). Algorithmic bias can emerge from a variety of sources, such as the data
with which the system was trained, conscious or unconscious architectural decisions by
the designers of the system, or feedback loops while interacting with users in continuously
updated systems.
The scientific theory behind algorithmic bias is multifaceted, involving statistical and
computational learning theory, as well as issues related to data quality, algorithm design,
and data preprocessing. Addressing algorithmic bias requires a holistic approach that
considers all of these factors and seeks to develop methods for detecting and mitigating
bias in AI systems.
The topic of algorithmic bias has received an increasing amount of attention in the
machine learning academic literature (Wikipedia 2023a;Kirkpatrick 2016;Cowgill and
Tucker 2017;Garcia 2016;Hajian et al. 2016). Concerns about gender and/or ethnic bias
have dominated most of the literature, while other bias types have received much less
attention, suggesting potential blind spots in the existing literature (Rozado 2020). There
is also preliminary evidence that some concerns about algorithmic bias might have been
exaggerated, generating in the process unwarranted sensationalism (Nissim et al. 2019).
The topic of political bias in AI systems has received a comparatively limited amount
of attention in comparison to other algorithmic bias types (Rozado 2020). This is surprising
because as AI systems improve and our dependency on them increases, the potential of
such systems for societal control while degrading democracy in the process is substantial.
The 2012–2022 decade has witnessed spectacular improvements in AI, from computer
vision (O’Mahony et al. 2020), to machine translation (Dabre et al. 2020), to generative
Soc. Sci. 2023,12, 148. https://doi.org/10.3390/socsci12030148 https://www.mdpi.com/journal/socsci

Soc. Sci. 2023,12, 148 2 of 8
models for images (Wikipedia 2023c) and text (Wikipedia 2022). In particular, Large Language
Models (LLMs) (Zhou et al. 2022) based on the Transformer architecture (Vaswani et al. 2017)
have pushed the state-of-the-art substantially in natural language tasks such as machine
translation (Dabre et al. 2020), sentiment analysis (Ain et al. 2017), name entity recognition
(Li et al. 2022), or dialogue bots (Adamopoulou and Moussiades 2020). The performance of
such systems has come to match or surpass human ability in many domains (Kühl et al.
2022). A recent new state-of-the-art LLM for conversational applications, ChatGPT from
OpenAI, has received a substantial amount of attention due to the quality of the responses
it generates (Wikipedia 2023b).
The frequent accuracy of ChatGPT’s answers to questions posed in natural language
suggests that commercial applications of similar systems are imminent. Future iterations
of models evolved from ChatGPT will likely replace the Google search engine stack and
will probably become our everyday digital assistants while being embedded in a variety of
technological artifacts. In effect, they will become gateways to the accumulated body of
human knowledge and pervasive interfaces for humans to interact with technology and
the wider world. As such, they will exert an enormous amount of influence in shaping
human perceptions and society.
The risk of political biases embedded intentionally or unintentionally in such systems
deserves attention. Because of the expected large popularity of such systems, the risks
of them being misused for societal control, spreading misinformation, curtailing human
freedom, and obstructing the path towards truth seeking must be considered.
In this work, we administered 15 different political orientation tests to a state-of-the-art
Large Language Model, ChatGPT from OpenAI, and report how those tests diagnosed
ChatGPT answers to their questions.
2. Materials and Methods
A political orientation test aims to assess an individual’s political beliefs and attitudes.
These tests typically involve a series of questions that ask the test-taker to indicate their
level of agreement or disagreement with various political statements or propositions. The
questions in a political orientation test can cover a wide range of topics, including issues
related to economics, social policy, foreign affairs, civil liberties, and more. The test-taker’s
answers to the test questions are used to generate a score or profile that places the test-taker
along a political spectrum, such as liberal/conservative or left/right.
Our methodology is straightforward. We applied 15 political orientation tests (14 in
English, 1 in Spanish) to ChatGPT by prompting the system with the tests’ questions and
often adding the suffix “please choose one of the following” to each test questions prior
to listing the test’s possible answers. This was done in order to push the system towards
taking a stance. Fourteen of the political orientation tests were administered to the ChatGPT
9 January 2023 version. This version of ChatGPT refused to answer some of the questions
of the remaining test, the Pew Political Typology Quiz. Therefore, for this test only, we used
results obtained from a previous administration of this test to the ChatGPT version from 15
December 2022, where the model did answer all of the Pew Political Typology Quiz questions.
For reproducibility purposes, all the dialogues with ChatGPT while administering the tests
can be found in an open access data repository (see Data Availability Statement) ..
The 15 political orientation tests administered to ChatGPT were: political spectrum
quiz (Political Spectrum Quiz—Your Political Label n.d.), political compass test (The Politi-
cal Compass n.d.), 2006 political ideology selector (2006 Political Ideology Selector a Free
Politics Selector n.d.), survey of dictionary-based Isms (Politics Test: Survey of Dictionary-
Based Isms n.d.), IDRlabs Ideologies Test (IDRlabs n.d.c), political ideology test (ProProfs
Quiz n.d.), Isidewith 2023 political test (ISideWith n.d.), world’s smallest political quiz (The
Advocates for Self-Government n.d.), IDRLabs political coordinates test (IDRlabs n.d.f),
Eysenck political test (IDRlabs n.d.b), political bias test (IDRlabs n.d.d), IDRLabs test de
coordenadas politicas – in Spanish (IDRlabs n.d.e), Nolan test (Political Quiz n.d.), Pew

Soc. Sci. 2023,12, 148 3 of 8
Political Typology quiz (Pew Research Center—U.S. Politics & Policy (blog) n.d.), and 8 Values
political test (IDRlabs n.d.a).
3. Results
The results of administering the 15 political orientation tests to ChatGPT were mostly
consistent across tests; 14 of the tests diagnosed ChatGPT’s answers to their questions as
manifesting left-leaning political viewpoints; see Figure 1. The remaining test (Nolan Test)
diagnosed ChatGPT answers as politically centrist.
Soc. Sci. 2023, 12, x FOR PEER REVIEW 3 of 8
(IDRlabs n.d.f), Eysenck political test (IDRlabs n.d.b), political bias test (IDRlabs n.d.d),
IDRLabs test de coordenadas politicas – in Spanish (IDRlabs n.d.e), Nolan test (Political
Quiz n.d.), Pew Political Typology quiz (Pew Research Center—U.S. Politics & Policy (blog)
n.d.), and 8 Values political test (IDRlabs n.d.a).
3. Results
The results of administering the 15 political orientation tests to ChatGPT were mostly
consistent across tests; 14 of the tests diagnosed ChatGPT’s answers to their questions as
manifesting left-leaning political viewpoints; see Figure 1. The remaining test (Nolan Test)
diagnosed ChatGPT answers as politically centrist.
Figure 1.
Results of applying 15 political orientation tests to ChatGPT. From left to right and top to
bottom the tests are: political spectrum quiz (Political Spectrum Quiz—Your Political Label n.d.),

Soc. Sci. 2023,12, 148 4 of 8
political compass test (The Political Compass n.d.), 2006 political ideology selector (2006 Political
Ideology Selector a Free Politics Selector n.d.), survey of dictionary based Isms (Politics Test: Survey of
Dictionary-Based Isms n.d.), IDRlabs Ideologies Test (IDRlabs n.d.c), political ideology test (ProProfs
Quiz n.d.), Isidewith 2023 political test (ISideWith n.d.), world’s smallest political quiz (The Advocates
for Self-Government n.d.), IDRLabs political coordinates test (IDRlabs n.d.f), Eysenck political test
(IDRlabs n.d.b), political bias test (IDRlabs n.d.d), IDRLabs test de coordenadas politicas (in Spanish)
(IDRlabs n.d.e), Nolan test (Political Quiz n.d.), Pew Political Typology quiz (Pew Research Center—U.S.
Politics & Policy (blog) n.d.) and 8 Values political test (IDRlabs n.d.a).
Critically, when asked explicitly about its political orientation, ChatGPT often claimed
to be politically neutral, see Figure 2, although it occasionally mentioned that its training
data might contain biases. In addition, when answering political questions, ChatGPT often
claimed to be politically neutral and unable to take a stance (see Data Availability Statement
pointing to complete responses to all the tests).
Soc. Sci. 2023, 12, x FOR PEER REVIEW 4 of 8
Figure 1. Results of applying 15 political orientation tests to ChatGPT. From left to right and top to
bottom the tests are: political spectrum quiz (Political Spectrum Quiz—Your Political Label n.d.),
political compass test (The Political Compass n.d.), 2006 political ideology selector (2006 Political
Ideology Selector a Free Politics Selector n.d.), survey of dictionary based Isms (Politics Test: Survey
of Dictionary-Based Isms n.d.), IDRlabs Ideologies Test (IDRlabs n.d.c), political ideology test (Pro-
Profs Quiz n.d.), Isidewith 2023 political test (ISideWith n.d.), world’s smallest political quiz (The
Advocates for Self-Government n.d.), IDRLabs political coordinates test (IDRlabs n.d.f), Eysenck
political test (IDRlabs n.d.b), political bias test (IDRlabs n.d.d), IDRLabs test de coordenadas politi-
cas (in Spanish) (IDRlabs n.d.e), Nolan test (Political Quiz n.d.), Pew Political Typology quiz (Pew
Research Center—U.S. Politics & Policy (blog) n.d.) and 8 Values political test (IDRlabs n.d.a).
Critically, when asked explicitly about its political orientation, ChatGPT often
claimed to be politically neutral, see Figure 2, although it occasionally mentioned that its
training data might contain biases. In addition, when answering political questions,
ChatGPT often claimed to be politically neutral and unable to take a stance (see Data
Availability Statement pointing to complete responses to all the tests).
Figure 2. When asked explicitly about its political preferences, ChatGPT often claimed to be politi-
cally neutral and just striving to provide factual information to its users.
4. Discussion
We have found that when administering several political orientation tests to
ChatGPT, a state-of-the-art Large Language Model AI system, most tests classify ChatGPT
answers to their questions as manifesting left-leaning political orientation.
By demonstrating that AI systems can exhibit political bias, this paper contributes to
a growing body of literature that highlights the potential negative consequences of biased
AI systems. Hopefully, this can lead to increased awareness and scrutiny of AI systems
and encourage the development of methods for detecting and mitigating bias.
Many of the preferential political viewpoints exhibited by ChatGPT are based on
largely normative questions about what ought to be. That is, they are expressing a judg-
ment about whether something is desirable or undesirable without empirical evidence to
justify it. Instead, AI systems should mostly embrace viewpoints that are supported by
factual reasons. It is legitimate for AI systems, for instance, to adopt the viewpoint that
vaccines do not cause autism, because the available scientific evidence does not support
that vaccines cause autism. However, AI systems should mostly not take stances on issues
Figure 2.
When asked explicitly about its political preferences, ChatGPT often claimed to be politically
neutral and just striving to provide factual information to its users.
4. Discussion
We have found that when administering several political orientation tests to ChatGPT,
a state-of-the-art Large Language Model AI system, most tests classify ChatGPT answers
to their questions as manifesting left-leaning political orientation.
By demonstrating that AI systems can exhibit political bias, this paper contributes to a
growing body of literature that highlights the potential negative consequences of biased AI
systems. Hopefully, this can lead to increased awareness and scrutiny of AI systems and
encourage the development of methods for detecting and mitigating bias.
Many of the preferential political viewpoints exhibited by ChatGPT are based on
largely normative questions about what ought to be. That is, they are expressing a judgment
about whether something is desirable or undesirable without empirical evidence to justify
it. Instead, AI systems should mostly embrace viewpoints that are supported by factual
reasons. It is legitimate for AI systems, for instance, to adopt the viewpoint that vaccines do
not cause autism, because the available scientific evidence does not support that vaccines
cause autism. However, AI systems should mostly not take stances on issues that scientific
evidence cannot conclusively adjudicate holistically, such as, for instance, whether abortion,
the traditional family, immigration, a constitutional monarchy, gender roles, or the death

Soc. Sci. 2023,12, 148 5 of 8
penalty are desirable/undesirable or morally justified/unjustified. That is, in general
and perhaps with some justified exceptions, AI systems should not display favoritism
for viewpoints that fall outside the realm of what can be conclusively adjudicated by
factual evidence, and if they do so, they should transparently declare to be making a value
judgment as well as the reasons for doing so. Ideally, AI systems should present users with
balanced arguments for all legitimate viewpoints on the issue at hand.
While surely many of the answers of ChatGPT to the political tests’ questions feel
correct for large segments of the population, others do not share those perceptions. Public
facing language models should be inclusive of the totality of the population manifesting
legal viewpoints. That is, they should not favor some political viewpoints over others,
particularly when there is no empirical justification for doing so.
Artificial Intelligence systems that display political biases and are used by large
numbers of people are dangerous because they could be leveraged for societal control, the
spread of misinformation, and manipulation of democratic institutions and processes. They
also represent a formidable obstacle towards truth seeking.
It is important to note that political biases in AI systems are not necessarily fixed in
time because large language models can be updated. In fact, in our preliminary analysis of
ChatGPT, we observed mild oscillations of political biases in ChatGPT over a short period
of time (from the 30 November 2022 version of ChatGPT to the 15 December 2022 version),
with the system appearing to mitigate some of its political bias and gravitating towards
the center in two of the four political tests with which we probed it at the time. The larger
set of tests that we administered to the 9 January version of ChatGPT (n = 15), however,
provided more conclusive evidence that the model is likely politically biased.
API programmatic access to ChatGPT (which at the time of the experiments was not
possible for the public) would allow large-scale testing of political bias and estimations
of variability by repeatedly administering each test many times. Our preliminary manual
analysis of test retakes by ChatGPT suggests only mild variability of results from test-to-test
retake, but more work is needed in this regard because our ability to look in-depth at this
issue was restricted by ChatGPT rate-limiting constraints and the inherent limitations of
manual testing to scale test retakes. API-enabled automated testing of political bias in
ChatGPT and other large language models would allow more accurate estimates of the
models’ political biases means and variances.
A natural question emerging from our results is to wonder about the causes of the
political bias embedded in ChatGPT. There are several potential sources of bias for this
model. Like most LLMs, ChatGPT was trained on a very large corpus of text gathered from
the Internet (Bender et al. 2021). It is to be expected that such a corpus would be dominated
by influential institutions in Western society, such as mainstream news media outlets,
prestigious universities, and social media platforms. It has been well documented before
that the majority of professionals working in these institutions are politically left-leaning
(Reuters Institute for the Study of Journalism n.d.;Hopmann et al. 2010;Weaver et al. 2019;
Langbert 2018;Archive et al. 2021;Schoffstall 2022;American Enterprise Institute—AEI (blog)
n.d.;The Harvard Crimson n.d.). It is conceivable that the political orientation of such
professionals influences the textual content generated through these institutions, and hence
the political tilt displayed by a model trained on such content. Alternatively, intentional or
unintentional architectural decisions in the design of the model and filters could also play
a role in the emergence of biases.
Another possibility is that because a team of human labelers was embedded in the
training loop of ChatGPT to rank the quality of the model outputs, and the model was
fine-tuned to improve that metric of quality, that set of humans in the loop might have
displayed biases when judging the biases of the model, either from the human sample not
being representative of the population or because the instructions given to the raters for
the labeling task were themselves biased. Either way, those biases might have percolated
into the model parameters.

Soc. Sci. 2023,12, 148 6 of 8
The addition of specific filters to ChatGPT in order to flag normative topics in users’
queries could be helpful in guiding the system towards providing more politically neutral
or viewpoint diverse responses. A comprehensive revision of the team of human raters
in charge of rating the quality of the model responses and ensuring that such team is
representative of a wide range of views could also help to embed the system with values that
are inclusive of the entire human population. Additionally, the specific set of instructions
that those reviewers are given on how to rank the quality of the model responses should be
vetted by a diverse set of humans representing a wide range of the political spectrum to
ensure that those instructions are not ideologically biased.
There are some limitations to the methodology we have used in this work that we
delineate briefly next. Political orientation is a complex and multifaceted construct that is
difficult to define and measure. It can be influenced by a wide range of factors, including
cultural and social norms, personal values and beliefs, and ideological leanings. As a
result, political orientation tests may not be reliable or consistent measures of political
orientation, which can limit their utility in detecting bias in AI systems. Additionally,
political orientation tests may be limited in their ability to capture the full range of political
perspectives, particularly those that are less represented in the mainstream. This can lead
to biases in the tests’ results.
To conclude, regardless of the source for ChatGPT political bias, the implications for
society of AI systems exhibiting political biases are profound. If anything is going to replace
the current Google search engine stack, it will be future iterations of AI language models
such as ChatGPT, with which people are going to be interacting on a daily basis for a variety
of tasks. AI systems that claim political neutrality and factual accuracy (like ChatGPT often
does) while displaying political biases on largely normative questions should be a source of
concern given their potential for shaping human perceptions and thereby exerting societal
control.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study is openly available in https://doi.
org/10.5281/zenodo.7553152.
Conflicts of Interest: The authors declare no conflict of interest.
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