# Generative AI Voting: Fair Collective Choice is Resilient to LLM Biases and
Inconsistencies

Srijoni Majumdar  School of Computing, University of Leeds, Leeds, UK,
E-mails<EMAIL_ADDRESS>Edith Elkind  Department of
Computer Science, University of Oxford, Oxford, UK, E-mails:
<EMAIL_ADDRESS>Evangelos Pournaras  School of Computing, University of
Leeds, Leeds, UK, E-mails<EMAIL_ADDRESS>
###### Abstract

11footnotetext: Corresponding author: Srijoni Majumdar, School of Computing,
University of Leeds, Leeds, UK, E-mail<EMAIL_ADDRESS>
Scaling up deliberative and voting participation is a longstanding endeavor –
a cornerstone for direct democracy and legitimate collective choice. Recent
breakthroughs in generative artificial intelligence (AI) and large language
models (LLMs) provide unprecedented opportunities, but also alerting risks for
digital democracy. AI personal assistants can overcome cognitive bandwidth
limitations of humans, providing decision support capabilities or even direct
AI representation of human voters at large scale. However, the quality of this
representation and what underlying biases manifest when delegating collective
decision making to LLMs is an alarming and timely challenge to tackle. By
rigorously emulating with high realism more than >50K LLM voting personas in
81 real-world voting elections, we show that different LLMs (GPT 3, GPT 3.5,
and Llama2) come with biases and significant inconsistencies in complex
preferential ballot formats, compared to simpler and more consistent
majoritarian elections. Strikingly, fair voting aggregation methods, such as
equal shares, prove to be a win-win: fairer voting outcomes for humans with
fairer AI representation. This novel underlying relationship proves paramount
for democratic resilience in progressives scenarios with low voters turnout
and voter fatigue supported by AI representatives: abstained voters are
mitigated by recovering highly representative voting outcomes that are fairer.
These insights provide remarkable foundations for science, policymakers and
citizens in explaining and mitigating AI risks in democratic innovations.

##  1 Introduction

Recent advances in artificial intelligence (AI) provide new unprecedented
opportunities for citizens to scale up participation in digital democracy [1,
2, 3]. Generative AI in particular, such as large language models (LLMs), have
a potential to overcome human cognitive bandwidth limitations and digitally
assist citizens to deliberate and decide about public matters at scale [4, 5,
6, 7, 8]. This is by articulating, summarizing and even providing syntheses of
complex opinions [9, 10, 7, 8], with a potential to mitigate for the voter
fatigue and reduced voters turnout [11, 10], while fostering common ground for
compromises, consensus and lower polarization [11, 12, 10, 13, 4]. However,
understanding the implications and risks of using large language models for
decision support/recommendations or even direct representation of human voters
is a pressing challenge [14, 15, 16, 17]. In this article we study the
inconsistencies of large language models to approximate both _individual and
collective choices_ as well as the inconsistencies of large language models
across different preference elicitation methods. We explore causal links of
these inconsistencies to potential cognitive biases triggered by the input to
large language models based on which choices are made.

Generative AI voting: missing opportunities with inevitable challenges. Large
language models have been used to predict election winners using sensitive
demographic data about the political profile of individuals [18]. They have
also been used to predict pairwise comparisons of proposals for constitutional
changes [6] and summarized opinions expressed with free-form text [19].
However, little is known whether this AI predictive capability can expand to
more complex collective decision-making processes that involve more options to
choose from, with more complex preference elicitation methods [5].

Participatory budgeting [20] is one such process put under scrutiny in this
article. Here city authorities distribute a public budget by letting citizens
propose their own project ideas, which they vote for and often implement
themselves [21]. Projects may be pertinent to different impact areas (e.g.
environment, culture, welfare), beneficiaries (e.g. elderly, children) and can
have different costs. Voters can approve, rank or distribute points over their
preferred projects, while winners are elected based on the popularity of the
projects (utilitarian greedy) or based on a proportional representation of the
voters’ preferences (equal shares) [22, 23]. Recently though, a reinforcement
learning approach has been used to elect winners by assisting voters to reach
a consensus [24]. So far, the scope and citizens’ engagement in participatory
budgeting campaigns remains highly local [25, 11]. With such complexity and
degree of design freedom, scaling up participatory budgeting turns into the
ultimate democratic blueprint to assess capabilities and risks of generative
AI voting.

Inconsistencies among different preference elicitation methods, known also as
intransitivity of preferences [26], have not been studied so far for large
language models. However, there is evidence that such inconsistencies do
manifest even in human voters [22, 23, 27], especially under polarized
contexts [28] that are usually subject of biases [29, 30]. Whether potential
biases that explain the inconsistencies between human and AI choices are of
different nature than the ones between different input voting methods is an
open question studied in this article.

How resilient representative voting outcomes are with generative AI. We
hypothesize that generative AI representatives of human voters build up
resilience for representative voting outcomes in voting processes of low
turnout if these human voters would otherwise abstain or would not have
capacity to actively participate in multiple collective decisions at large-
scale (see Figure 1d. In other words, we explore whether the inconsistencies
of voting outcomes because of low voting turnout are higher than
inconsistencies originated from generative AI voters.

The inconsistencies of generative AI, along with the potential biases
explaining these inconsistencies, are systematically studied here for the
first time within a novel factorial design that consists of 5 dimensions (see
Figure 1): (i) _Real-world voting scenarios_ \- datasets from the 2012, 2016
and 2020 US national elections [31] as well as the mock and actual
participatory budgeting voting of the ‘City Idea’ campaign of 2023 in Aarau,
Switzerland [32] are studied. They come with different number of ballot
options and voters, see Figure 1a and Section 4.2. (ii) _Personal human
traits_ \- for each voter, multiple incremental levels of additional
information are given as input to large language models to generate voting
choices. This information includes socio-demographics, political preferences,
preferences for project categories and preferences for voting outcome
qualities (popular vs. fair election of winners, individual vs. collective
benefit). These are derived from survey data collected along the voting
campaigns (see Section 4.1, and Table LABEL:table:prologue1 and
LABEL:table:prologue2). (iii) _Voting input methods_ \- four methods with
incremental level of complexity and expressiveness are compared [28, 33].
These include single choice for all voting scenarios, approvals, score
(assigning a preference score to each option) and cumulative voting
(distributing a number of points over the options) [34, 35, 36] for the
participatory budgeting scenarios. (iv) _AI models_ \- Three large language
models are assessed along with a more mainstream predictive machine learning
(ML) model used as a benchmark. GPT 3, GPT 3.5, and Llama2 are chosen covering
a large spectrum of capabilities in open and closed generative AI [37], see
Section 4.1 for more information. (v) _Voting aggregation methods_ \- majority
is used for the scenario of US elections. The standard utilitarian greedy and
the method of equal shares [38] are used for the participatory budgeting
scenarios. The former method iterates to elect the next most popular project
as long as the budget is not exhausted. The latter method elects the projects
that make the most people ‘happy’, meaning a combination of projects that
maximizes a proportional representational of voters’ preferences. This is the
method that was actually used in City Idea campaign to elect the winners [39].
See Section 4.1 for more information.

The dimensions of the factorial design are illustrated in Table 1 and the
studied combinations are marked with the color boxes in Figure 1a. This broad
spectrum of analysis allows us to generalize the findings of the study and
make them highly relevant for a broad spectrum of research communities and
policymakers.

Figure 1: An overview of the generative AI framework, along with the factorial
design and the biases studied in this article. (A) The factorial design with
the 5 studied dimensions: (i) real-world voting scenarios in the context of
participatory budgeting and national elections. (ii) Various combinations of
persona human traits based on which AI personas are created. (iii) Four voting
input methods. (iv) Four AI models, three large language models and a
predictive machine learning model (benchmark). (v) Several voting aggregation
methods for elections and participatory budgeting. The studied combinations
for each voting scenario are marked with different colors, see also Table 1.
(B) The framework of generative AI voting. For each voter in the real-world
voting scenario, a prompt is given to large language models to construct the
voting persona. The input is the personal human traits, the voting options and
the ballot format with instructions for the voting persona about how to make a
choice. This choice is the output of the persona. Both human and AI choices
are aggregated using a voting aggregation method. The inconsistencies of
individual and collective choices for humans and AI personas are assessed,
along with potential biases that explain these inconsistencies. (C) The
personal human traits are mapped to cognitive biases. Section 4.4 illustrates
choice inconsistencies and their origin to potential cognitive biases. (D)
Hypothesizing for a recovery of consistency in voting outcomes under low
voters turnout if AI representatives support abstained human voters.

Assessing generative AI voting in action. Voting personas are created using
input prompts of large language models as depicted in Figure 1b. This designed
process aims to emulate the three voting scenarios with the different settings
of Figure 1a. Each input prompt consists of a standardized description of the
voter’s profile (see Section LABEL:sec:study in SI) and an instruction to vote
according to the ballot format. The individual and collective choices of
humans and AI personas are compared by measuring the overlap (Jaccard
similarity) of their selected/winning options as an indicator of consistency
[28]. These consistency values are then becoming the dependent variable to
predict using the personal human traits as independent variables (features),
which are fed in a neural network (see Section 4.5). Based on a systematic
mapping of human personal traits to the cognitive biases as illustrated in
Figure 1c (see Section 4for more detail), this prediction model causally
explains the human traits that contribute to inconsistencies and the potential
underlying biases that explain these inconsistencies.

##  2 Results

The following three key results are illustrated in this article:

1. Fair voting methods to elect winners are more resilient to human-AI inconsistencies in collective choice, demonstrating a novel underlying win-win relationship: fairer voting outcomes for humans with fairer human representation by AI (Figure 2). These inconsistencies are particularly prominent in complex ballot formats with a large number of alternatives, while simple majoritarian voting tends to be highly consistent. GPT 3.5 shows transitivity of preferences among different ballot formats (Figure 3).

2. By representing half of the human voters who abstain with AI, a significant lost consistency is recovered in voting scenarios with low number of alternatives and fair voting aggregation methods to elect winners (Figure 4). For more than 50% of voters turnout, consistency is fully recovered by representing at least 25% of abstained voters with AI in single-choice majoritarian voting with two alternatives.

3. Conformity biases explain human-AI choice consistency, while unconscious biases explain inconsistency (Figure 5). Political/ideological profile determines the human-AI consistency in single-choice majoritarian voting with two candidates. Time discounting factors explain transitivity of AI preferences between different ballot formats, while affect heuristics and unconscious biases explain intransitivity.

###  2.1 Fair collective choice is resilient to human-AI inconsistencies

Figure 2 illustrates the human-AI consistency in individual and collective
choices. The consistency of individual choice remains poor in complex ballot
formats with several alternatives. On average, it is 4.5% and 27.2% for the
actual and mock voting in participatory budgeting, yet it is 82.3% for the
binary majoritarian US elections. GPT 3.5 shows the highest consistency of
individual choice among the large language models, which is 5.2% and 5.31%
higher than GPT 3 and Llama2. On the contrary, the consistency of the
collective choice increases by 51.2% in total. Strikingly, the consistency of
equal shares is 80.8%, which is 32.8% higher than utilitarian greedy. Even
when reducing the number winners of equal shares to the 8 of utilitarian
greedy, the consistency remains 29.4% higher.

Figure 2: Choice by large language models is consistent to humans for single-
choice majoritarian elections, however consistency drops for more complex
ballots with larger number of alternatives as in the case of participatory
budgeting. Strikingly, consistency of collective choice is significantly
higher than individual choice, particularly for the fairer voting aggregation
rule of equal shares. GPT 3.5 shows the highest choice consistency and Llama2
the lowest among the large language models, which though remain inferior to a
predictive machine learning model. The consistency (y-axis) in individual and
collective choice is shown for different AI models (x-axis) across three real-
world voting scenarios: The participatory budgeting campaign of City Idea, (A)
actual and (B) mock, and (C) the US national elections of 2012, 2016 and 2020.
The consistency in the participatory scenarios is shown for different voting
input methods (left and right). The consistency of collective choice with the
two voting aggregation methods of utilitarian greedy and equal shares is also
shown. For the actual voting of City Idea, the consistency of equal shares is
calculated for all winners and 8 winners (as many as greedy) for a fair
comparison.

Compared to the large language models, the machine learning model shows 3.6%
higher consistency in individual choice and 6.6% higher in collective choice.
All consistency values are the maximum ones derived with all personal human
traits as shown in Figure 1a. The removal of project preferences shows the
highest consistency reduction of 19.7%, while political interest shows the
lowest consistency reduction of 3.4%.

Figure 3 illustrates the transitivity of preferences by different large
language models by measuring the consistency of individual choice among
different pairs of ballot formats. ‘Approval - cumulative’ voting shows the
highest consistency, which is 1.8% higher than ‘single choice - approval’ and
3.5% higher than ‘single choice - cumulative’.

Figure 3: Voting personas based on GPT 3.5 show transitivity of the
preferences over the different voting input methods in the actual complex
voting scenario. However, GPT 3 and Llama2 remain with a low consistency <0.5.
The simpler mock voting scenario shows higher transitivity among all language
models. The consistency (y-axis) in individual choice among different pairs of
ballot formats (x-axis) is shown for different large language models, humans
and the two voting scenarios in the participatory budgeting campaign of City
Idea: (A) actual vs. (B) mock.

GPT 3.5 shows 21.1% higher consistency than GPT 3 and 20.2% higher than
Llama2. While human transitivity approaches 95.2% on average, AI transitivity
remains at 70.1% for GPT 3.5. The mock voting with only 5 alternatives
achieves on average 65.5% of consistency, while the actual voting with 33
alternatives shows a consistency of 43.4%.

###  2.2 AI representatives to recover from low voters turnout

Figure 4a illustrates the capability in AI representatives who abstain from
voting to recover the consistency of the voting outcomes lost by low voters
turnouts. Strikingly, up to 50% of AI representatives are sufficient for a
maximum consistency recovery of 53.3% under voting turnouts lower than 80%
when equal shares is used. This significant recovery is despite the very low
consistency of human-AI individual choices in complex ballot formats with
several options (Figure 2a). The recovery is also confirmed for equal shares
with the top-8 winners, which is equal to the number of winners with
utilitarian greedy (on average 18.1% lower consistency recovery). However, AI
representatives fail to recover consistency with utilitarian greedy as the
voting aggregation method.

Figure 4: By representing up to half of the human voters that abstained from
voting with AI, a significant lost consistency in the collective choice is
recovered in participatory budgeting under fair voting aggregation methods
such as equal shares and under low number of alternatives. However, further
consistency loss is observed under a large number of alternatives with the
standard utilitarian greedy method as the number of AI representatives
increases. In contrast, single-choice majoritarian elections with highly
consistent human-AI individual choice maximize consistency recovery for the
whole spectrum of voters turnout by maximizing the AI representatives for the
voters who abstain. Nevertheless, for more than 50% of voters turnout,
consistency is fully recovered by representing at least 25% of abstained
voters with AI. The consistency loss in the voting outcome by low voters
turnout (x-axis) is emulated by removing different ratios of human voters. A
recovery of consistency (y-axis) is hypothesized by different ratios of
abstained voters represented by AI: 25%, 50%, 75% and 100%. The consistency
recovery values are the means of all three large language models (see Figure
LABEL:fig:LLM_factorial for the results of each large language model). The (A)
actual and (B) mock voting in the participatory budgeting campaign of City
Idea are shown with two voting aggregation methods: utilitarian greedy and
equal shares, with all winners and the top-8 (as many as greedy) for a fairer
comparison. (C) US elections. (D) Chord visualizations summarizing how the
interplay between voters turnout and AI representation yields voting outcomes
with consistency gain or loss.

Figure 4b shows that the recovery of consistency is more robust for both
voting aggregation methods under low number of alternatives. Consistency is
recovered with up to 75% of abstained voters represented by AI under a maximum
of 60% voters turnout, after which 50% of AI representatives are sufficient
for consistency recovery.

Under the voting scenario of the US elections that comes with highly
consistent human-AI individual choices, consistency is fully recovered for
$\geq$25% of abstained voters represented by AI under a minimum of 50% voters
turnout. However, below 50% of voters turnout, election results are very
sensitive and consistency recovery is guaranteed only for 100% of abstained
voters represented by AI. More detailed results on mixed populations of humans
and AI representatives, including the consistency loss when human voters
abstain are shown in Figure LABEL:fig:mixed_populations-abstaining.

###  2.3 Biases explaining AI inconsistencies in choice and preference
transitivity

To explain the origin of (in)consistencies in human-AI choice and different
voting input methods, a prediction model is designed for each voting scenario.
The personal human traits, mapped to different cognitive biases, become the
independent variables and the consistency the dependent variable. The
prediction model is built using a neural network (see Section 4.4). The
relative importance of the personal human traits that explain the AI
consistency is calculated using category gradient boosting (see Section 4.5).

In the case of participatory budgeting in Figure 5a and 5b, the preference for
environmental projects (potential conformity bias [40, 41, 42]) shows an
average importance of 26.5% (p=0.0258) in explaining human-AI choice
consistency (25% and 12% higher consistency among environmental supporters,
with p=0.038 for the actual voting). Political belief and ideology are
strongly correlated with the actual two alternatives in the US elections
(right/conservative for republicans and left/liberal for democrats),
therefore, they strongly explain the consistency of human-AI choice (mean
relative importance of 98.7%, p<0.02 and 4.8%, p<0.03 respectively). However,
this is not the case in participatory budgeting as it political belief that
rather explains inconsistency (relative importance of -51.2% with p<0.02).
This demonstrates that the underlying participatory budgeting choices remain
politically neutral to a high extent, despite the fact that the City Idea
campaign mainly attracted left-wing voters (42.5%) versus 31.5% of right wing
voters. In contrast, race and discussion of politics explain inconsistencies
in the US elections with a mean relative importance of -48.1% (p<0.04) and
-64.7% (p<0.04) respectively. Inconsistency is 33% higher than consistency
among white people (p<0.019), while for people who discuss politics
inconsistency is 63.2% higher that the ones who do not (p=0.046).

Figure 5: Conformity bias, such as preference for environmental projects,
positively contribute to the consistency of human-AI choice. On the other
hand, unconscious biases such as race, discussion of politics and political
belief cause inconsistencies. In the case of the US elections though,
political belief and ideology strongly contribute to consistency. Time
discounting factors such as preference for sport, public space and welfare
projects positively contribute to the consistency of large language models
among the voting input methods. However, affect heuristics such as preference
for projects that benefit elderly cause inconsistencies among voting methods.
Unconscious biases originated from socio-demographic information also cause
inconsistencies in the simple mock voting scenario. The relative importance
(x-axis) of the personal human traits (y-axis) to predict the consistency of
choice and consistency between voting input methods (single choice vs.
cumulative/score) is shown for different large language models and the machine
learning model. This is calculated using category gradient boosting. The (A)
actual and (B) mock voting in the participatory budgeting campaign of City
Idea is shown along with the (C) US elections. The ‘*’ denotes that the
feature is statistically significant (p<0.05) for each of the AI models of
Llama2, GPT 3, GPT 3.5 and ML respectively. The chord visualizations summarize
the biases that explain the (in)consistencies of (D) human-AI choice and (E)
voting input methods.

The consistency among the voting input methods is explained by preference
towards time discounting factors [41] in participatory budgeting, such as
sport, welfare and public space projects. The average relative importance is
25.6%, 25.3% and 17.5% respectively (all with p<0.03), with 48.53%, 44.64%,
and 37.5% of higher consistency among supporters in the actual voting
(p<0.044). However, support for elderly, linked to potential affect heuristics
[41, 43, 44], explains inconsistency with an average relative importance of
-35.4% (56.4% higher inconsistency among elderly supporters, with p=0.0371 for
the actual voting).

Figures LABEL:fig:explaination-consistency1, LABEL:fig:merged3a,
LABEL:fig:explaination-top-choice, and Table LABEL:table:error illustrate
additional insights about how personal human traits explain the AI top choice
and the human-AI consistency of the individual choices.

##  3 Discussion

Generative AI voting: an inevitable race to safeguard democracy. Generative AI
voting is likely to emerge as an inevitable technological convergence of AI
and e-voting solutions currently adopted in real-world. On the one hand,
generative AI and large language models are expected to become more open,
pervasive and accessible to citizens [45, 15]. AI personal assistants are
already part of everyday life [46, 47, 48], with their generative version
expected to follow. On the other hand, the mandate of more direct, secure and
active participation in decision making for public matters is expected to
further scale up electronic voting solutions and digital platforms. For
instance, participatory budgeting elections are mainly conducted digitally,
while Estonia has already institutionalized a digital identity for 99% of its
citizens as well as electronic voting since 2005 [49]. As Toomas H. Ilves,
former president of Estonia, emphasized "_with the digital signature and the
machine-readable ID card, we created the e-citizen_ ". In the light of these
converging advancements, the findings of this study become spot on to
understand the implications of such future, while they are significant to
prepare safeguards for digital democracy.

Fair voting design as a safeguard for generative AI voting. Large language
models prove not ready yet to accurately represent humans in complex voting
scenarios such as participatory budgeting. However, a striking finding of this
study is that consistency of large language models becomes significant under
fair voting aggregation methods that promote a proportional representation of
voters’ preferences. This motivates a huge opportunity to get democracy
"right" in the digital era of AI: move to alternative voting methods that
yield fairer voting outcomes for all, while shielding democratic outcomes from
AI biases and inconsistencies. The momentum for this win-win paradigm shift is
tremendous as fair voting rules such as the method of equal shares have been
recently tested in real-world with grand success: more winning projects with
higher proportional representation of citizens’ preferences [39, 22]. How to
scale up these democratic blueprints remains an open question, but AI can play
a key role here.

Why fair collective choice is resilient to AI biases and inconsistencies. We
provide a plausible explanation of this significant and striking finding.
There is evidence that the method of equal shares has an inherent stability in
the resulting voting outcomes [22]. For instance, even with 25-50% of the
voters, the elected projects do not significantly change [22]. This is despite
the fact that projects may end with very different number of total votes, as
low and middle-cost projects require a very minimal support to get elected,
and as a result, these winning projects are likely to be retained in the
winning set, even with different choices or set of voters. Such projects are
expected to be an origin of consistency. Indeed, this effect is also observed
in the real-world voting scenario of City Idea, as with 80% abstaining voters,
84% of the winners are retained with equal shares, see Figure
LABEL:fig:mixed_populations-abstaining. As City Idea promoted equal shares
already in the project ideation phase and made apriori use of equal shares for
the aggregation of the ballots, this study comes with significant realism and
merit for the validity of the findings. This comes in stark contrast to other
earlier studies [22, 39] that hypothesize the application of equal shares over
proposed projects and ballots aggregated with the standard method of
utilitarian greedy.

Mitigating low voters turnout: AI representation is not enough, voting design
matters. This perspective is assessed for the first time in this study by
emulating voting scenarios with varying voters turnout, while letting voters
who abstain to have a representation by generative AI to restore the lost
consistency of abstaining. Nevertheless, the inherent inconsistencies of
generative AI can hinder the effectiveness of AI representatives. Therefore
understanding these opposing dynamics comes with significant merit to mitigate
the impact of AI biases and inconsistencies in collective decision making.
This is by showing striking evidence that voting design has a determining role
on how much consistency can be recovered and what effect this can have for the
legitimacy of the voting process and outcomes. Recovering the lost consistency
under very low voting turnouts in single-choice majoritarian elections
requires populations with very high majorities of AI representatives, which
turns out to be illegitimate. In contrast, the lost consistency under higher
voting turnouts can be restored even with low proportions of AI
representatives. The fair method of equal shares is also here a prominent
accelerator for the consistency recovery: fewer AI representatives are
sufficient to restore consistency even under very low voter turnouts. It is
also evident that such consistency recovery could not be achieved with the
standard voting aggregation method.

Trustworthy generative AI voting: a call for research and policy action. What
information large language models use to reason about voting decisions is
influential for different types biases to manifest. Unconscious and conformity
biases, related to political belief, race and environment, reinforce human
choices or yield inconsistencies. Time discounting biases, related to the
support of projects on welfare, public space and sport, explain preference
transitivity among different ballot formats. These significant findings
demonstrate that training data in generative AI voting are expected to be
highly impactful for the representation of the voting outcomes in the
population. Ethical and democratic guidelines are urgently needed,
particularly for the use of (generative) AI in voting processes. For instance,
who shall determine the input training data of AI representatives? Should the
training data involve only self-determined personal information of voters, or
shall these be augmented with more universal knowledge and experts’ opinions?
How to protect the privacy and autonomy of voters when training such AI
representatives [48]? Will citizens retain power to control AI representatives
that reflect their values and beliefs, while remaining accountable? These are
some key questions as a basis of a call for action on research and policy in
an emerging era of generative AI voting.

##  4 Methods

We show here how AI representatives are emulated and the real-world data based
on which several voting scenarios are constructed. We also illustrate the
evaluation approach in detail and the studied human cognitive biases. Finally,
the approach to explain the inconsistencies and biases of generative AI is
outlined.

###  4.1 Emulating AI representatives

We introduce a generalized design to create AI representatives using three
large language models and a predictive AI method as a benchmark (referred to
as ML) to generate choices (ballots) and emulate AI voting representation.

Input to ballot generation. This comprises of (i) defining the voting
scenario, the voting alternatives and the personal human traits of the voter
as the input context (LLMs) or features (ML) to inform the ballot choice, (ii)
generating the ballots and (iii) aggregating the ballots into a collective
decision (see Figure 1).

  * (i)

Personal human traits: Voter’s information is used to define an AI persona of
a human voter, i.e. the AI representative (more information in Tables
LABEL:table:prologue1 and LABEL:table:prologue2 in SI). This includes (i)
socio-demographic characteristics (e.g., gender, age, education, household
size), (ii) political interests (e.g., ideological profile, political belief),
(iii) personal attitude towards project preferences (e.g., prioritization for
green initiatives, sustainable transport, elderly care facilities) and (iv)
preferences for the qualities of voting outcomes (e.g., favoring cost-
effective winning projects vs. popular ones vs. winning projects with
proportional representation of citizens’ preferences). These traits are
extracted from feedback surveys of voters, who also cast ballots in the voting
scenarios and therefore the survey data are linked to the actual voting
behavior (see field study design in Section 4.2).

  * (ii)

Voting alternatives: The three real-world voting scenarios determine the
voting alternatives given as input to the AI representatives. They are
illustrated in detail in Section 4.2. The actual City Idea scenario comes with
33 project alternatives and their cost, while the mock scenario comes with 5
costed project alternatives. The US elections comes with two alternatives.
These three scenarios provide a broad spectrum of decisions spaces to assess
the AI representatives.

  * (iii)

Ballot formats: The two participatory budgeting scenarios are either approvals
(n-approval ballot where ‘n’ is the number of projects approved) or cardinal
preferences (score or cumulative) [50, 51]. In the case of score ballots, the
voters provide a score to a project from a specified range such as 1 to 5 with
1 being the lowest preference and 5 being the highest. In the case of
cumulative ballots, the voters distribute a number of given points among the
projects. Single-choice ballots are derived from single approvals, top scores
or top points distributed in a project.

Generative AI. The three inputs determine a three-tiered context for ballot
generation. This provides comprehensive information about the voter and the
voting process to prompt large language models for choice generation without
any additional fine-tuning process. We use proprietary models such as GPT 3.5,
GPT3 [52] and the open-source Llama2-13B [53]. GPT 3.5 and GPT 4 have similar
performance for these tasks [54], hence we resort to GPT 3.5 for the analysis.
Furthermore, both GPT 3.5 and GPT 4 use the “ _Let’s think step by step_ ” to
evaluate a prompt and find a solution that mimics human reasoning [54]. For
each of the large language model, predictions are made based on the five
combinations of personal human traits (see Figure 1), however the predictions
with all models have the consistency to human choices, which we use for
further analysis.

Predictive AI benchmark. The personal human traits are used as features to
predict the ballots using classification methods [55]. We sample 40% of the
data and use synthetic minority oversampling [56] to augment the data. This
40% is decided empirically based on the performance of the neural network
models. We also divide the prediction into multiple sub-problems, which
predict the approvals or assigned scores for every option and then compile the
final ballot. This model is then used to predict the 60% of the unknown
ballots and the 40% of the known ballots. As such, we were able to train a
model with good precision and recall using 40% of the data and sampling, which
could be attributed to clusters of voters generating ballots of similar
patterns.

Ballot aggregation. For the US elections, majority determines the winner. For
the two voting scenarios of participatory budgeting, the generated ballots by
the AI representatives are aggregated using either the utilitarian greedy
method or the method of equal shares. Utilitarian greedy simply selects the
next most popular project, the one with the most received votes, as long as
the available budget is not exhausted. Equal shares provides a proportional
representation of voters’ preferences by dividing the budget equally among
voters as endowments. This part of the budget can only be used to fund
projects that the voter has voted for. The method goes through all project
options, beginning with the projects with the highest number of votes. It
selects a project if it can be funded using the budget shares of those who
voted for the project. A full explanation of the is out of the scope of this
article and can be found in earlier work [38, 50, 51]. In practice, the method
of equal shares is likely to sacrifice an expensive popular project to select
instead several low-cost projects, which, in combination, satisfy the
preferences of more voters [39, 22]. As a result, equal shares tends to elect
more winning projects. Because of this effect, it likely that consistency
measurements based on the Jaccard similarity yield higher values for equal
shares. This is the reason we control for the number of winning projects in
equal shares by counting a subset of the most popular winning projects which
is equal in number with winners of greedy. .

Emulated voting processes with human voters and AI representatives. We emulate
the following voting processes: (i) Human voters exclusively with 100%
turnout. (ii) human voters exclusively with varying turnout in the range
[10%,100%] with a step of 10%. (iii) Mixed populations of human voters and AI
representatives assessed whether they can recover the consistency of voting
outcomes lost by different human voters turnouts in the range [10%,100%] with
a step of 10%. The ratio of AI representatives for the abstained voters varies
in the range [25%,100%] with a step of 25%. (iv) AI representatives
exclusively.

Each scenario of voters turnout is repeated 10 times by selecting a random
subset of voters who abstain. For each turnout level, the AI representation of
abstained voters is also repeated 10 times by selecting a random subset of the
abstained voters to represent by AI.

Data collection infrastructure. Generative AI choices are collected via API
prompts to large language models from 16th of June 2023 to 8th of November
2023. We prompted the large language models using the zero-shot learning
feature [52], which does not require any specific fine-tuning. We use chain of
thought prompting [57] to provide a comprehensive and systematic flow of
information for better interpretability. The large language models are run
with temperature settings of 0.2 to 0 (20 runs for each temperature setting),
for a focused decision with less randomness [58]. As we are dealing with a
significantly large decision space particularly the 33 projects in the actual
voting, running with a high-temperature setting can lead to random decision-
making by the large language models. To maintain consistency, we retain the
projects that are approved at the deterministic temperature setting (the
responses are easily reproducible at temperature setting = 0). Further, if a
project is selected both in 0.1 and 0.2, we include the approval for that
project in the ballot to also incorporate diversity. In the case of scores, we
take the frequency of the distinct scores assigned to a project across
different temperature setting and add the score with the highest frequency.
However, for the actual City Idea dataset for which we emulate cumulative
voting, where the total score provided in a ballot should be less than a
maximum point. Hence it would not be not be accurate to derive a score for a
project from multiple outputs corresponding to different temperature settings,
hence we conducted experiments only with temperature setting = 0.2. The
decoding algorithm for the large language model is set to the nucleus sampling
approach [57]. Compared to purely random sampling, it avoids generating
outliers or improbable sequences as choices.

For the predictive AI choices, we use neural networks with 3 hidden layers and
relu [59] activation function for the sub-networks in the neural network. We
employ the ensemble method [60] of designing multiple neural networks with
different parameters and merging in the final layer with cross entropy loss
[61]. This helps to generalize on different sizes of datasets and improve the
prediction scores on unseen data.

###  4.2 Voting datasets

Table 1 outlines the settings of the emulated voting scenarios.

US elections. The 2012, 2016, and 2020 survey waves of the American National
Election Study (ANES) [62] are used. They encompass 4,500 votes together with
the respective voter information for each of these three years. This mainly
includes socio-demographic characteristics: (i) racial/ethnic self-
identification, (ii) gender, (iii) age, (iv) conservative-liberal ideological
self-placement, (v) party identification (political belief), (vi) political
interest, (vii) church attendance, (viii) discussion of politics with
family/friends, (ix) feelings of patriotism associated with the American flag,
and (x) state of residence. A total of 6 elections are emulated using
Llama-13B and GPT-3.5 based on single-choice ballots, with winners decided by
majority for the three years. We extracted information from 20,650 voters of
the ANES dataset [62] and after pre-processing (removing voters with missing
values in more than 5 of the personal traits), a set of 17,010 voters is used.
The examples of the prompts used to generate the AI choices can be found in
Table LABEL:table:Anes.

City Idea: Participatory budgeting campaign in Aarau, Switzerland. The data
from a recent innovative participatory budgeting field study are used [32,
39], which was conducted with ethical approval from University of Fribourg
(#2021-680). It run 2023 and is rigorously designed to assess the application
of equal shares for the first time in real world, in combination with
cumulative voting, using the open-source Stanford Participatory Budgeting
platform [33]. As such, the field study includes a survey conducted before
voting linking the choices of survey respondents and voters. We use the
following personal human traits from the survey (more information in Tables
LABEL:table:prologue1 and LABEL:table:prologue2): (i) 9 fundamental socio-
demographic characteristics (e.g., age, citizenship, education) and 2
political interests (political beliefs and trust in democracy), (ii)
preferences for 9 different types of projects and 6 beneficiaries and (iii) 4
types of preferences for qualities of the voting outcome.

Table 1: The studied dimensions across three real-world voting scenarios. They
provide the necessary diversity to generalize the findings of this study as
they include different scales in number of voters, different ballot formats
and aggregation methods, low and high numbers of alternatives, different
personal human traits for studying a broad spectrum of biases and both
generative and predictive AI methods.

Studied factors | US Elections | City Idea | City Idea
---|---|---|---
| 2012, 2016, 2020 | [Mock] | [Actual]
Ballot Input |  |  |
Personal human traits |  |  |
Socio-demographics | ✓ | ✓ | ✓
Preference for voting outcome qualities | ✗ | ✓ | ✓
Project preferences | ✗ | ✓ | ✓
Political interests | ✓ | ✓ | ✓
Ballot format |  |  |
Single-choice | ✓ | ✓ | ✓
Approval | ✗ | ✓ | ✓
Score | ✗ | ✓ | ✗
Cumulative | ✗ | ✗ | ✓
Alternatives for voting | 2 | 5 | 33
Ballot Generation |  |  |
Generative AI |  |  |
GPT 3 | ✗ | ✓ | ✓
GPT 3.5 | ✓ | ✓ | ✓
Llama2-13B | ✓ | ✓ | ✓
Predictive AI |  |  |
Neural Networks | ✓ | ✓ | ✓
Ballot Aggregation |  |  |
Majority | ✓ | ✗ | ✗
Utilitarian greedy | ✗ | ✓ | ✓
Equal shares | ✗ | ✓ | ✓
Voters |  $\sim$17,000 (across 3 years) | 3,134 | 505

City Idea includes a mock voting of hypothetical costed projects as part of
the initial survey before the actual voting took place. The AI representatives
are constructed using the same personal human traits to emulate both mock and
actual voting. More information about the two voting scenarios is illustrated
below:

  * (i)

Mock Voting: Five hypothetical costed projects belonging in different
categories are put for mock voting as part of the initial survey. Table
LABEL:table:prologueS illustrates the project alternatives and their cost. The
choice of 3134 voters over the same alternatives is tested with three
different ballot formats in a sequence, starting with the simplest one of
single choice to the most complex ones of approvals and score voting. This
allows us to emulate a total of 45 elections based on various combinations of
the ballot format and personal human traits using the AI models and the
aggregation methods outlined in Figure 1. We also emulate an election based on
the predictive AI model.

  * (ii)

Actual Voting: Using the Stanford Participatory Budgeting platform [33], 1703
voters cast their vote using cumulative ballots by distributing 10 points to
at least 3 projects of their preference, out of 33 projects in total (see
Table LABEL:table:prologueA for project descriptions). A subset of 505 of
these voters, which participated in the initial survey and provided their
personal human traits, are used to construct the AI representatives. The
ballot formats of single choice and approvals are derived from the cumulative
ballots by taking the project with the most points and the projects that
received any point respectively. This allows us to emulate a total of 27
elections based on various combinations of the ballot formats and personal
human traits using the AI models and the aggregation methods outlined in
Figure 1. We have also emulated an election based on the predictive AI model.

The examples of the prompts used to generate the AI choices in both mock and
actual voting can be found in Table LABEL:table:prologuePersona.

###  4.3 Evaluation of choices by AI representatives

We compare the emulated elections with AI representatives with the real-world
elections of human voters at two levels: (i) _individual choice_ , i.e. the
ballots, and (ii) _collective choice_ , i.e. the resulting voting outcomes.
Consistency is the main measure of assessment.

Consistency of individual choice. Consistency is assessed at two levels:

  * (i)

Human-AI consistency of individual choice: This is the Jaccard similarity [63]
of individual choices made by AI representatives with the original choices of
the humans they represent. For single-choice ballots, this is either 1
(consistent) or 0 (inconsistent) for each voter and the respective AI
representative. For approval voting, the AI and human choices are binary
sequences representing the ballot, containing _1_ (approved) or _0_ (not
approved) for each alternative. The Jaccard similarity between these sequences
is the ratio between the number of alternatives approved by both the human and
the AI representative over the number of alternatives approved by at least one
of them. For score and cumulative voting, projects are ranked based on the
scores and points received respectively. Each top-k project of a human voter
is compared to the respective top-k project by its AI representative. Matching
is indicated by 1 (consistent), otherwise 0 (inconsistent) indicates no
matching. The mean number of matchings among all ranked projects measures the
consistency for these ballot formats.

  * (ii)

Consistency of AI and human individual choice across ballot formats: We
consider the consistency between different pairs of ballot formats. For
_single choice - score_ and _single choice - cumulative_ voting, consistency
is measured as $1\ -\ (\text{top\ score}-\text{score\ [single\ choice]})$. The
top score represents the highest score provided in the score or cumulative
ballot. The score of the single choice is the score given under the score
ballot format to the project selected by the single-choice ballot format. For
_single choice - approval_ ballots, consistency is either 1 (single choice is
approved) or 0 (single choice is not approved). For the ballot pairs _approval
- score_ and _approval - cumulative_ , score and cumulative voting are first
represented as _1_ (score/points >0) or _0_ (no score/points) for each
alternative. The Jaccard similarity is then used to calculate consistency.

Consistency of collective choice. It is defined for two voting processes that
elect winners from the same alternatives, but have different voters (human, AI
representatives, mixed), and, as a result, can yield different voting
outcomes. The voting outcomes with the winners are first calculated using one
of the ballot aggregation methods, i.e. majority, utilitarian greedy or equal
shares. These outcomes are represented as binary sequences containing either
_0_ (not winner) or _1_ (winner) for each alternative. Consistency is
calculated using the Jaccard similarity [63] between these binary sequences.

Consistency recovery in collective choice with AI representatives. It is
determined here for voting processes with varying voters turnout, in which
abstained voters result in consistency loss, which can be recovered if a
portion of these abstained voters are represented by AI. It is measured as
follows:

$\frac{consistency\ [\text{human\ voters - abstained\ voters + AI\
representatives}]-consistency\ [\text{human\ voters - abstained\
voters}]}{1-consistency\ [\text{human\ voters - abstained\ voters}]},\ $

where the voters turnout $\frac{\text{human\ voters - abstained\
voters}}{\text{human\ voters + abstained\ voters}}$ varies in the range
[10%,90%] with a step of 10%, and AI representation $\frac{\text{AI\
representatives}}{\text{abstained\ voters}}$ varies in the range [25%,100%]
with a step of 25%.

###  4.4 Human cognitive biases in AI collective decision making

Human choices are significantly influenced by potential cognitive biases that
are often a manifestation of socio-economic characteristics, conditions of
life quality, satisfaction with the available public amenities and the overall
life experiences of an individual [41]. Here we map self-reported personal
human traits with which large language models are prompt to potential
underlying human cognitive biases, as a way to explore whether they are
reinforced by the language models and become more likely to manifest in the AI
choices. Figure 1c outlines the mapping we study based on a systematic and
comprehensive review of relevant literature. The following types of biases are
determined:

Time-discounting biases. These are characterized by the tendency to receive
immediate gratification over a larger but future reward. Projects related to
the public space, culture, or welfare are often one-off projects, such as an
annual festival, a cinema night, or educating asylum children (alternatives
proposed for the participatory budgeting campaign in Aarau [32]), where
expected benefits are experienced in the short term. Therefore, such project
are subject of time-discounting biases [41]. In contrast, the construction of
a road, for example, incurs investment costs several years before the actual
project implementation. Such investments create a sustainable reform that
benefits all in the long run. Even though 72% of the public transportation
infrastructure in European cities ends up in cost overruns, people still vote
for these projects due to an underlying necessary optimism to improve
transport [41, 39]. We refer to this economic discounting over a long-term
sustainability impact as ‘optimism’ [64, 65].

Surrogation biases. This reflects how humans favor simpler measures to assess
the impact over ones that are more precise and harder to evaluate. Korteling
et al. [41] argue these biases manifest when deciding projects with large
societal impact such as health or education, while their outcome is subject of
different satisfaction levels among citizens. The project outcomes may be
perceived as successful by part of society using easy-to-evaluate metrics
instead of looking at long-term effects on the community. For instance, a
timely vaccination drive may be preferred over significant changes to
vaccination protocols or health insurance policies covering vaccination. Hence
these projects are likely to be preferred more as they are likely to come with
more intuitive ways to assess for the broader population. This is reflected
with average winning rate of the health and education-related projects of
38.1% and 36.2% respectively in Poland [39], where participatory campaigns
have been actively hosted in the last decade.

Conformity biases. These biases arise out of group pressure under which people
make decisions to be socially desirable [40, 42]. It is argued that a
conformity bias may induce voting for green alternatives [41]. Green-themed
participatory budgeting campaigns have been adopted in European cities such as
Lisbon [66], to promote green initiatives, aligning to a culture for more
sustainable behavior. Poland runs participatory budgeting campaigns at large
scale, which include environment and urban greenery projects. These are within
the top-5 most popular projects with an average of 22.5% and 26.5%
respectively [39]. Even in Aarau we observe the same trend wherein,
environmental friendly projects account for the top-10 most popular projects
[32].

Affect heuristic biases. This defines the tendency to make decisions based on
what intuitively or emotionally feels right. Affect biases have been studied
to analyze the inclusive attitudes most people show towards elderly people
[44]. Similar biases also manifest in welfare of children and in inter-
generational communication [41, 43]. In Aarau, we observe that 71.3% of the
voters prefer projects for younger and elderly people.

Biases for altruism and egotism. Individual interest is often in conflict with
the community interest in participatory and collective decision-making
processes. While voting, the intrinsic altruism of citizens strongly
influences choices. As a result, altruism and egotism are influential for the
fairness of voting outcomes and how these outcomes benefit the city in overall
[67, 41, 43]. In Aarau, we observe that 67.1% of the voters, who prefer better
representation in the outcome are prosocial and prioritize the city-wide
benefit (altruism bias) over individual benefit (egotism bias).

Unconscious biases. Human choices are influenced by socio-economic and
demographic traits such as race, ethnicity, citizenship, household size and
income [68]. Specifically, political ideology and belief shape to a high
degree decisions for candidates in elections [69].

###  4.5 Explainability of generative AI voting

The consistency of the individual AI choices (see Section 4.3) is modeled as
the dependent variable in a predictive machine learning framework. Causal
relationships that explain how personal human traits (independent variables)
contribute to consistency are studied. We model the problem of explaining
inconsistencies as a classification problem, where 10 uniform consistency
levels are defined as $[0.0,0.1],(0.1,0.2],...,(0.9,1.0]$. We also assess a
different prediction model with dependent variable the top single choice,
which is the most preferred alternative, see Figure LABEL:fig:explaination-
top-choice for more details. Moreover, further details about how we account
for imbalances of features, their collinearity and hyperparameter optimization
of the model are illustrated in Section LABEL:sec:ml and Table LABEL:tab:set.

Explainability of choices. There is no established ground truth of how
personal human traits (features) influence voter ballots, and thus we
introduce a two-dimensional feature importance analysis framework to determine
the impact of the personal human traits on individual choice consistency. It
consists of the following methods: (i) _Category gradient boosting_ [70] via
which the relative importance of each personal human trait (feature) is
derived. Results are shown in Figure 5. (ii) _Feature ablation studies_ [71]
via which a specific personal human trait (feature) is removed to understand
the impact on the classification performance. The significance of the
difference in the prediction score with and without the feature is calculated
and comaprisons are made across each single removed feature [72, 70]). Results
are shown in Figure LABEL:fig:explaination-consistency1. (iii) _Feature
permutation methods_ [73], wherein feature data points are shuffled to measure
the impact on the classification performance (Table LABEL:table:error).

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## Acknowledgements

This work is funded by a UKRI Future Leaders Fellowship (MR/W009560/1):
_Digitally Assisted Collective Governance of Smart City Commons–ARTIO_ ’. The
participatory budgeting data were earlier collected in the context of the City
Idea campaign with support from Swiss National Science Foundation NRP77
‘Digital Transformation’ project (#407740_187249): _Digital Democracy:
Innovations in Decision-making Processes_. The authors would like to thank
Thomas Wellings, Regula Hänggli and Dirk Helbing for their support and
encouragement.

## Supplementary Material

Supplementary material is available below.

## Author contributions statement

S.M. wrote the manuscript, collected the data, designed and developed the AI
models, and analyzed the data. E.E. edited the manuscript and analyzed the
data. E.P. wrote the manuscript, conceived the study, designed the AI models
and analyzed the data.

## Preprints

This manuscript was posted on a preprint: https://doi.org/

## Code Availability

The codes are available in the link https://github.com/TDI-Lab/Generative-AI-
Voting