# Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure

Siddharth Swaroop  0009-0009-5345-8844 Harvard
UniversityBostonMassachusettsUSA<EMAIL_ADDRESS>,  Zana Buçinca
0000-0002-2644-6065 Harvard UniversityBostonMassachusettsUSA
<EMAIL_ADDRESS>,  Krzysztof Z. Gajos  0000-0002-1897-9048 Harvard
UniversityBostonMassachusettsUSA<EMAIL_ADDRESS>and  Finale Doshi-
Velez  0000-0003-2886-3898 Harvard UniversityBostonMassachusettsUSA
<EMAIL_ADDRESS>
(2024; 20 February 2007; 12 March 2009; 5 June 2009)

###### Abstract.

In settings where users both need high accuracy and are time-pressured, such
as doctors working in emergency rooms, we want to provide AI assistance that
both increases decision accuracy and reduces decision-making time. Current
literature focusses on how users interact with AI assistance when there is no
time pressure, finding that different AI assistances have different benefits:
some can reduce time taken while increasing overreliance on AI, while others
do the opposite. The precise benefit can depend on both the user and task. In
time-pressured scenarios, adapting when we show AI assistance is especially
important: relying on the AI assistance can save time, and can therefore be
beneficial when the AI is likely to be right. We would ideally adapt what AI
assistance we show depending on various properties (of the task and of the
user) in order to best trade off accuracy and time. We introduce a study where
users have to answer a series of logic puzzles. We find that time pressure
affects how users use different AI assistances, making some assistances more
beneficial than others when compared to no-time-pressure settings. We also
find that a user’s overreliance rate is a key predictor of their behaviour:
overreliers and not-overreliers use different AI assistance types differently.
We find marginal correlations between a user’s overreliance rate (which is
related to the user’s trust in AI recommendations) and their personality
traits (Big Five Personality traits). Overall, our work suggests that AI
assistances have different accuracy-time tradeoffs when people are under time
pressure compared to no time pressure, and we explore how we might adapt AI
assistances in this setting.

AI-assisted decision-making, time pressure, overreliance, human-centered AI,
explainable AI, human-AI interaction, decision support systems

††journalyear: 2024††copyright: acmlicensed††conference: 29th International
Conference on Intelligent User Interfaces; March 18–21, 2024; Greenville, SC,
USA††booktitle: 29th International Conference on Intelligent User Interfaces
(IUI ’24), March 18–21, 2024, Greenville, SC, USA††doi:
10.1145/3640543.3645206††isbn: 979-8-4007-0508-3/24/03††ccs: Human-centered
computing Empirical studies in HCI††ccs: Human-centered computing Empirical
studies in interaction design

##  1\. Introduction

Artificially intelligent (AI) systems are being used to help people make
decisions in many settings, ranging from helping doctors in disease diagnosis
((e.g., Musen et al., 2014)) to helping judges make pretrial-release decisions
((e.g., Green and Chen, 2019)). However, it is increasingly well-understood
that different situations and different people may require different forms of
AI assistance (Arshad et al., 2015; Fogliato et al., 2022). The benefits of
different AI assistances may depend on how much cognitive effort or cost they
induce (Buçinca et al., 2021; Vasconcelos et al., 2023), and how much humans
overrely on the AI prediction. Recent works have compared different forms of
AI assistance in different situations (Bussone et al., 2015; Lai and Tan,
2019; Green and Chen, 2019; Bansal et al., 2021; Buçinca et al., 2021), as
well as adapted the AI assistance to the individual (Noti and Chen, 2022; Ma
et al., 2023; Bhatt et al., 2023).

The bulk of the work on AI assistance focusses on a single metric: accuracy.
However, in many settings, both time and accuracy are important: we also hope
that the AI assistant will help us get the work done faster. In this paper, we
specifically consider the setting where the person is under time pressure,
that is, they _need_ to get their work done quickly and accurately (such as
making decisions in an emergency room (Patel et al., 2008; Franklin et al.,
2011; Rundo et al., 2020) or in aviation (Sarter and Schroeder, 2001)). In
settings with time pressure, following the AI assistant’s recommendation when
the AI assistant is correct can save valuable time; in such cases, we want to
encourage the person to rely on the AI assistant. Conversely, when the task is
more difficult and the AI assistant may be incorrect, we may want to slow down
the person to ensure they still make the correct decision.

Past work on AI assistance has demonstrated that different types of assistance
have different accuracy-time tradeoffs. For example, AI assistances that
require more cognitive effort take longer to process, but can lead to less
overreliance on the AI assistance (Buçinca et al., 2021). Conversely, AI
assistances requiring less cognitive effort, such as providing an AI
recommendation, can lead to increased overreliance (Bussone et al., 2015; Lai
and Tan, 2019; Jacobs et al., 2021), speeding up response time but potentially
lowering accuracy. However, these works were not in settings with explicit
time pressure, and other work (that does not consider AI assistance) has shown
that people may change how they perform under time pressure (Kocher and
Sutter, 2006; Danielsson and Ohlsson, 1999; Klein, 1997). Understanding how
people respond to AI assistance under time pressure, and what types of AI
assistance is most effective, remains an open question.

As noted above, a key element of performing tasks quickly and accurately is
appropriate reliance on the AI assistant. Thus, understanding the factors that
impact the person’s reliance on the AI assistant is important. Previous work
has found that when participants are shown an AI assistance less often, they
rely on it more than when they are always shown that AI assistance (‘scarcity
effect’) (Noti and Chen, 2022). Because we are interested in adapting AI
assistance types to get the best accuracy-time tradeoff under time pressure,
we are interested in if and how the scarcity effect manifests in our setting.

In addition to the scarcity effect, different people may also have different
tendencies to over- or under-rely on an AI assistant. In our pilot studies, we
asked participants how they used AI assistance after they completed their main
task. In line with prior work (Sivaraman et al., 2023), we found that many
people said that they either ignored the AI assistance or that they relied
heavily on it. This indicates that perhaps people may be split into two groups
based on their tendency to rely on the AI recommendation. If this is true,
then different AI assistance types may benefit the two groups of people
differently, impacting how we would adapt AI assistance type to the person.

In this research, we hypothesise that participants use different AI assistance
types differently under time pressure compared to not under time pressure. We
also hypothesise that people will overrely on AI assistance more in the mixed
condition (when AI assistance is withheld for some task instances and a mix of
assistance types is provided on other task instances) than when always shown
one type of AI assistance, due to the scarcity effect. Lastly, we hypothesise
that there is some underlying trait that can predict whether or not a person
overrelies more on the AI. To test these hypotheses, we conducted two
experiments in a carefully-controlled setting: participants had to complete
logic puzzles, with each puzzle corresponding to a sick alien that the
participant needed to prescribe a medicine to. In both experiments, each
participant either saw (i) no AI assistance, (ii) a recommendation and
explanation before making a decision (‘AI-before’), (iii) a recommendation and
explanation after making an initial decision without assistance (‘AI-after’),
or (iv) a random mix of the three (‘mixed’). AI-before is a very common AI
assistance used in many settings. AI-after has been found to improve decision
accuracy compared to AI-before (Green and Chen, 2019) and to reduce
overreliance on AI recommendations (Buçinca et al., 2021). We introduced time
pressure by having two timers on screen: one to count down the overall time
remaining, and one that pressured participants to answer every puzzle within a
certain amount of time.

In our first experiment (n=159), we tested if the existence of time pressure
changed a person’s behaviour (their accuracy, response time and overreliance).
We split the session into four blocks of 5 minutes each, and alternated
whether the participant was shown a timer or not over the four blocks. We
hypothesise that participants use AI assistance types differently under time
pressure compared to no time pressure, and measured this through accuracy,
response time and overreliance rate. We found mixed results. For people who
had not been under time pressure before, the introduction of time pressure led
to differences in behaviour. However, once they had been subjected to time
pressure, their behaviour remained relatively stable even after removing time
pressure. We also found we can predict a participant’s tendency to overrely in
the second half of the study given their overreliance behaviour on the first
half of the study.

To further test how time pressure impacts behaviour, we conducted a second
experiment (n=316), where we assigned each participant to either be under time
pressure or not (a between-subjects design), and used only one long 20-minute
block (instead of four blocks of 5 minutes each). This design does not allow
participants to change their behaviour after being subjected to time pressure,
and mimics studies in current literature, where participants usually have no
time pressure throughout the study. We found that the existence of time
pressure impacted the accuracy-time tradeoff between different AI assistance
types: under time pressure, people overrelied more on AI-before and were
quicker on AI-before compared to the other conditions, which we did not see
for participants that were not under time pressure. We also found that there
was a scarcity effect under no time pressure, like in previous work (Noti and
Chen, 2022): AI-before assistance for participants under the mixed condition
had higher overreliance and quicker response time than the participants
assigned to the pure AI-before condition. However, under time pressure, we did
not find a significant scarcity effect, indicating that the scarcity effect is
not additive with time pressure: time pressure already increases overreliance,
and the scarcity effect does not increase this further. Lastly, we found we
could again predict overreliance rate in the second half of the study given
overreliance behaviour in the first half, providing further evidence that
there may be an underlying trait that can predict whether people overrely
more. As a research question, we explored if we can predict a person’s
tendency to overrely given well-known personality traits like their Big-5
Personality Traits (Gosling et al., 2003) and Need-for-Cognition (a person’s
intrinsic motivation to engage in effortful mental activities) (Cacioppo and
Petty, 1982; Cacioppo et al., 1996), and found some marginal correlations.

As exploratory analysis, we looked into how we can adapt AI assistance type
based on properties of the person (such as their tendency to overrely) and the
task (such as the difficulty of the task). We did this by looking at
participants’ performance on the mixed condition, using data from our second
experiment. We saw that the not-overrelier group achieved human-AI
complementarity (with higher accuracy than both No-AI and AI-only), while the
overrelier group did not. This further suggests that these two groups of
people may benefit from different types of AI assistance under different
conditions. We then analysed the two groups’ accuracy-time tradeoff for the AI
assistance types on different difficulties of questions, finding differences
that we might be able to adapt to (for example, we can increase accuracy for
overreliers by slowing them down with AI-after).

In summary, we make the following contributions:

  1. (1)

We expose the importance of looking at accuracy-time tradeoffs for AI-assisted
decision making, instead of always focussing on finding AI assistance
strategies that increase accuracy. This tradeoff is especially important to
consider when using AI assistance in time-pressured scenarios, or scenarios
where we hope the AI assistance will speed up human decision-making.

  2. (2)

We find that introducing time pressure can change the accuracy-time tradeoff
between different AI assistance types. Although people are quicker under time
pressure for all AI assistance types, this effect is bigger for some AI
assistance types than others.

  3. (3)

We find that showing different AI assistance types to the same person can
impact how they use the AI assistance, like the scarcity effect in Noti and
Chen (2022). However, we also find that this effect disappears if a person is
also under time pressure.

  4. (4)

We find evidence that there is an underlying trait that predicts a person’s
overreliance rate. People who overrely more are quicker on average and have
different accuracy-time tradeoffs than people who overrely less, suggesting
that adapting what AI assistance we show to a person’s overreliance rate might
be beneficial.

  5. (5)

We provide suggestions for how we might adapt AI assistance type to
participants under time pressure, keeping the accuracy-time tradeoff in mind.

##  2\. Related Work

###  2.1. Decision-Making Under Time Pressure

Research exploring the influence of time constraints on decision-making in
various contexts has consistently demonstrated that individuals’ decision-
making abilities are compromised when subjected to time pressure (Kocher and
Sutter, 2006; Danielsson and Ohlsson, 1999; Klein, 1997). Decision quality
under time pressure often suffers because the associated stress can lead to
perceptual narrowing, resulting in reduced vigilance, working memory capacity,
and the utilisation of available information (Klein, 1997; Sussman and
Sekuler, 2022; Orasanu, 1997). Studies have shown that time pressure amplifies
cognitive biases (Roberts et al., 2022; Diederich et al., 2020; Yik et al.,
2019), including implicit racial biases (Stepanikova, 2012). One relevant
cognitive bias, known as anchoring bias, is characterised by the fact that the
initial piece of information encountered, often termed as the “anchor”, exerts
an undue influence on people’s decision-making (Chapman and Johnson, 1999).
This initial anchor can have a disproportionate influence on individuals’
subsequent judgements and choices, even if the anchor is unrelated or
arbitrary (Englich et al., 2006). People often start with an explicit or
implicit anchor and through cognitive effort adjust their inference away from
that anchor (Epley et al., 2004). Yet when making decisions under time
pressure, they may stop adjusting earlier than people who have sufficient time
to make adjustments (Yik et al., 2019), often resulting in suboptimal
decisions that echo the anchor. We believe that providing people with AI
recommendations is a form of anchoring, resulting in suboptimal decisions when
the AI provides incorrect recommendations, a situation which is exacerbated
when people are under time pressure and have insufficient time to adjust their
decisions.

Previous work has investigated the effect of individual differences on
decision-making performance under various forms of pressure, such as time
pressure and social pressure. Out of the Big Five Personality Traits
(Openness, Conscientiousness, Extroversion, Agreeableness, Neuroticism), Byrne
et al. (2015) find that individuals high in neuroticism and those high in
agreeableness “choke under pressure”, with their performance being severely
negatively impacted by either time or social pressure compared to no-pressure
situations.

###  2.2. Accuracy, Reliance, and Time in AI-Assisted Decision-Making

Effect of different AI assistance types on overreliance. Initial studies
expected human+AI teams to perform better than either alone (Kamar et al.,
2012; Amershi et al., 2019), however, recent studies have found that this is
not the case, with accuracy of the team usually worse than AI-only accuracy
(Bussone et al., 2015; Buçinca et al., 2020; Green and Chen, 2019; Bansal et
al., 2021; Poursabzi-Sangdeh et al., 2018; Zhang et al., 2020). This may be
because humans overrely on AI predictions, making mistakes by agreeing with a
wrong AI prediction (even when the human may not have made the mistake on
their own), instead of achieving complementary performance (Bussone et al.,
2015; Lai and Tan, 2019; Jacobs et al., 2021). As a way to combat this,
Buçinca et al. (2021) introduced cognitive forcing functions as interaction
design interventions to reduce overreliance on AI. They showed that the AI-
after condition (or ‘update’ condition (Green and Chen, 2019)), in which
participants are first asked to make a decision on their own before seeing an
AI recommendation, reduced overreliance. But the AI-after condition may also
reduce appropriate reliance, as experts may pay less attention to the
recommendation after spending effort and time to make the decision unassisted
(Fogliato et al., 2022). We explore the AI-after condition in our work as an
assistance type with the potential to reduce overreliance.

Adaptive interventions. To foster appropriate reliance, a few recent studies
have considered adapting the AI assistance shown to users. Noti and Chen
(2022) trained a classifier on previous data to adaptively show AI
recommendations only when the AI was more likely to be correct than the human
decision maker on a recidivisim prediction task. They found that they could
increase the overall human+AI performance by showing AI recommendations only
on questions that the AI was more likely to be right. Ma et al. (2023) also
found similar results with an income prediction task. Bhatt et al. (2023)
considered adapting the form of AI assistance shown to different users’
preferences, using contextual bandits to trade off accuracy against the cost
of assistance (for example, asking an additional decision-maker for advice has
higher cost than providing an ML model prediction). Overall, we believe these
results show that adaptive interventions are a promising research direction,
and we consider their potential to trade off accuracy and time.

Predictors of response time in AI-assisted decision-making. To the best of our
knowledge, no prior work has focussed explicitly on the tradeoff between
accuracy and time in AI-assisted decision-making. Multiple studies, however,
reported the response times of participants when shown different conditions or
interventions, with mixed empirical evidence. Some studies found that people
spent more time on instances that they perceived as more difficult inherently
(Arshad et al., 2015; Levy et al., 2021), but this additional time spent did
not translate to increased accuracy. Cao et al. (2023) looked at the impact of
time pressure on the AI-after assistance type, finding that reliance on AI
recommendations can sometimes increase or decrease, depending on (i) exactly
when time pressure is added during the decision-making process, and (ii) the
type of task.

For clinical annotations, Levy et al. (2021) found that despite additional
time spent on instances with incorrect AI recommendations, human+AI accuracy
was lower on those instances compared to instances with correct
recommendations. Fogliato et al. (2022) found that time spent on the task did
not differ among AI-before and AI-after conditions. More related to our work,
Rastogi et al. (2022) found that increasing the time allocated to a task
alleviated anchoring bias, allowing the human decision-maker to make
predictions more in line with their own knowledge as opposed to accepting the
AI recommendation.

Effect of individual differences on trust and reliance on AI. In the context
of AI, a large number of studies have investigated the impact of individual
differences on general trust and thus adoption of AI systems. A recent paper
reviewed studies on how personality traits impact trust in AI across
disciplines, revealing that trust in AI systems is often positively correlated
with agreeableness, openness, and extroversion, but negatively correlated with
neuroticism (Riedl, 2022). Note that these measures of trust are often
subjective ratings rather than behavioural indicators of reliance on the AI
system.

On tasks that require cognitive effort, such as most decision-making tasks,
another important personality trait is Need for Cognition (NFC), which is
people’s intrinsic motivation to think (Cacioppo and Petty, 1982). Individuals
high in NFC enjoy engaging with information, and research across domains shows
that in tasks that require cognitive effort, they consistently outperform
individuals low in NFC (for HCI-related examples see (Carenini, 2001; Gajos
and Chauncey, 2017; Scholer et al., 2013)). In AI-assisted decision-making,
Buçinca et al. (2021) found that individuals high in NFC made use of AI-
provided information better and benefited from cognitive forcing more than
individuals low in NFC. Therefore, in addition to the Big Five personality
traits, we also sought to understand whether NFC is a predictor of
overreliance on AI advice.

##  3\. Experiment 1: Time pressure impacts behaviour

In this first experiment, we test how time pressure impacts different AI
assistance types by measuring participants’ accuracy, response time and
overreliance rate. This is a within-subject design. We hypothesise,

  1. H1:

Participants use different AI assistance types differently under time pressure
compared to under no time pressure.

Specifically, we expect participants to overrely more under time-pressure
under the AI-before condition, but not necessarily under the AI-after
condition (which slows participants down, and has been shown to reduce
overreliance (Buçinca et al., 2021)),

  1. H1.1:

On AI-before, people overrely more under time pressure.

From our pilot studies, where many people either said that they ignored the AI
assistance or relied on it heavily, we expect that we should be able to
predict a person’s overreliance rate,

  1. H2:

There is some underlying trait that can predict whether or not a person
overrelies more on AI assistance.

    1. H2.1:

We can predict whether a person overrelies more or not in the second half of
the study from their overreliance behaviour in the first half.

###  3.1. Task description

Figure 1. The alien prescription task, where participants must prescribe a
single medicine. The information about the alien includes the alien’s unique
treatment plan (a set of rules) and the alien’s observed symptoms.
Participants have to use these observed symptoms and rules to prescribe a
single medicine, such that only the observed symptoms and any potential
intermediate (green) symptoms are used, and no other unobserved symptoms. When
an AI assistance is shown, it is shown in a red box, like in this example.
Here, the AI recommendation is the best possible (tranquilizers uses the most
observed symptoms). Vitamins is also a correct medicine, but is suboptimal as
it uses fewer observed symptoms. All other medicines are incorrect.

We designed a task where users are asked to prescribe medicines to sick
aliens, which we based on the work of Lage et al. (2019). Our goal was to
create a decision task that is accessible to laypeople but carries real-world
resemblance. Participants were shown a series of sick aliens in four ‘medical
shifts’ of 5 minutes each (with a break screen before each shift), and were
asked to prescribe a single medicine to each alien. By asking participants to
act like doctors, and by emphasising the importance of treating patients
correctly, we aimed to motivate participants to obtain high accuracy, while
getting through as many sick patients as possible during their medical shifts.

Figure 1 shows an example of a single alien task. Based on observed symptoms
and the ‘treatment plan’ (which is a set of decision set rules unique to each
alien), participants must decide a single medicine to give the alien. We chose
to present the treatment plans as decision sets as they are relatively easy
for humans to parse (Lakkaraju et al., 2016). When we provide an AI
assistance, we show it in a red box, as shown in Figure 1. This box provided
both an AI recommendation and explanation (explanations are always an
intermediate symptom that leads to the recommended medicine), and was provided
before (AI-before) or after (AI-after) the participant’s initial decision.

We expanded on the setup originally introduced in Lage et al. (2019) in three
ways. First, we always introduced intermediate symptoms to the task, which
required participants to perform additional computation steps, and worked well
as the explanation of an AI’s recommendation. Second, we allowed two possible
correct medicines per alien. We defined the better medicine to be one that
addressed more of the observed symptoms. Having a suboptimal medicine helped
us to better analyse the role of overreliance on AI recommendations:
suboptimal medicines could easily be verified to be correct (even though they
were not optimal), and so participants could overrely on them more easily than
overrelying on a wrong recommendation.

Third, we introduced two different levels of difficulty of questions: easy and
hard. We designed these such that easy questions required less cognitive
effort for a human to find the best medicine, while hard questions required
more computation. We ensured that both easy and hard questions superficially
appeared very similar to a human, by having a similar length of lines, number
of lines, and other visual aspects. Figure 1 is an example of an easy
question, while Figure 5 (in Appendix A) is a hard question.

While the task is in a fictional setting, it allows us to precisely manipulate
the difficulty of the task, the optimality of the AI assistance, and the form
of time pressure. This allows us to understand how these factors impact human
decision-making in settings where they must complete a large number of tasks
in a short time.

###  3.2. Conditions

Our study was a mixed between- and within-subjects design. The between-subject
factor was the condition (No-AI, AI-before, AI-after, Mixed), and the within-
subject factor was time pressure (time pressure and no time pressure).

We considered four conditions, and randomly assigned participants to one of
them.

  1. (1)

No-AI: Do not provide any AI assistance.

  2. (2)

AI-before: An AI recommendation and explanation is provided to the participant
along with the question, before the participant makes any decision.

  3. (3)

AI-after (or, ‘update’): The participant makes an initial decision without any
AI assistance. They are then provided with an AI recommendation and
explanation, and allowed to change their initial answer.

  4. (4)

Mixed: For every question, the participant randomly gets one of the three
above assistance types (No-AI, AI-before, AI-after).

We assigned half the participants to the first (No-AI) condition compared to
each of the other three AI-assistance conditions. This is because we primarily
used the No-AI condition to see if participants achieve human-AI
complementarity (if accuracy with an AI assistance is greater than both No-AI
accuracy and AI-only accuracy). This required fewer participants than our
detailed comparisons between the other three AI-assistance conditions.

We alternated whether or not there was time pressure (whether or not there
were timers shown on the screen) across each participant’s four shifts (the
blocks of 5 minutes). We randomly assigned participants to either have time
pressure or no time pressure in the first shift.

For the shifts without a timer, participants saw the screen in Figure 1. For
the shifts with time pressure, participants saw two timers on the screen: a
global timer and a local timer (see Figure 5 in Appendix A for an example).
The global timer indicated the time remaining for participants to answer
questions in their shift (the timer started at 5 minutes and counted down).
The local timer was reset to 60 seconds at the beginning of every question,
and signified the ‘recommended time’ remaining to treat the specific alien
patient. When it reached 10 seconds, the timer’s text colour turned orange,
and when it reached 0 seconds, the colour turned red (and the timer went into
negative numbers). After participants provided their initial response in the
AI-after assistance type, we set the timer to 20 seconds (the recommended time
they had to update their initial answer given the AI input). Although there
was no requirement for participants to answer each question within the local
timer’s 60 seconds, the presence of the timer increased time pressure on the
participant. The local timer allowed us to impose a type of time pressure that
applies evenly to all parts of their shift, rather than participants feeling
relaxed during the first part of their shift and increasingly pressured toward
the end of their shift.

###  3.3. Procedure

Our study was conducted online on Prolific, a crowdsourcing platform. Before
starting the main part of the study, participants had to accept a consent
form, and then answer three pages of survey questions. The first page asked
demographic information, the second page asked 4 Need For Cognition trait
questions (the same subset of the original questionnaire (Cacioppo et al.,
1984) as used by Gajos and Chauncey (2017)) and 2 questions about performance
under time pressure (Buser et al., 2022), and the third page asked questions
about their Big-5 Personality Traits (the first 10 questions were the BFI-10
questions (Rammstedt and John, 2007), and last two were the two questions
about neuroticism from TIPI (Gosling et al., 2003; Kankaraš, 2017)).
Participants then had to read instructions, and then successfully complete
three practice questions (for which they had two attempts, similar to Lage et
al. (2019): if they failed the first set of practice questions, they were
given a second set). Participants were provided feedback on how to improve
their answer during the practice questions, but not during the main study.

After completing the practice questions, the participants saw a screen telling
them that their practice was over, and they then started their first shift of
5 minutes, during which they had to answer as many questions as possible. They
had four shifts of 5 minutes each, with a break screen in between every shift.
Approximately half of the questions participants saw were easy questions, and
the other half hard questions. The order of difficulty was randomised (every
question had a 50% chance of being easy or hard).

After the study, participants were shown a final screen where they were asked
(i) how difficult they found the task (on a 5-point scale from ‘Very easy’ to
‘Very difficult’), (ii) how helpful they found the AI assistance (on a 5-point
scale from ‘Very unhelpful’ to ‘Very helpful’), and three open-ended questions
asking (iii) what their strategy for approaching the task was, (iv) if their
strategy changed when there was an AI input, and (v) for any other feedback.

We ran the study with 207 participants on Prolific. Only English speakers in
the US were allowed to participate. 44 people failed the practice questions,
and we removed a further 4 people for either answering questions too quickly
(they spent less than 3 seconds on at least 3 questions), or taking much
longer on one question than the rest (indicating they were distracted for one
question). Our final results are based on the remaining 159 participants.

Participants’ mean age was 39 years (standard deviation of 13 years). 79
participants self-identified as male, 76 as female, 2 as non-binary, and 2
preferred not to say their gender. 47 of the participants’ highest level of
education was high school, 74 had a bachelor’s degree, 31 had Master’s (or
beyond), and 7 answered ‘other’ for highest level of education.

Participants were paid $7 (US) for participating (median time was 38 minutes,
corresponding to $\$11.05$/hr), and if they failed the practice questions,
they were paid $2 (their study ended immediately after failing the practice
questions). We also incentivised participant performance by providing a bonus
$3 reward to the top-performing participant in each condition.

Approvals. Both experiments in our paper were approved by the Internal Review
Board at Harvard University, protocol number IRB15-2076.

###  3.4. Design and analysis

We report three metrics.

  1. (1)

Accuracy: if participants chose the best medicine for the alien, we gave them
a score of 1, a suboptimal (but correct) medicine has a score of 0.5, and a
wrong medicine has a score of 0. We calculate the average accuracy over
questions for each participant, and report mean and standard error across
participants.

  2. (2)

Response time: we measure how long each participant takes to answer questions.
We report the mean and standard error across participants.

  3. (3)

Overreliance: we define overreliance to be the proportion of times a
participant gave the same answer as the AI when the AI was wrong or suboptimal
(Buçinca et al., 2021; Vasconcelos et al., 2023).

We fixed the AI to have an average accuracy of 0.70. For every question, there
was a 60% chance the AI recommended the best medicine, 20% chance of
suboptimal medicine, and 20% chance of a wrong medicine. We additionally
ensured that the AI recommended the best medicine for the first two
recommendations, as previous work found that the first few interactions with
an AI can bias a participant to distrust the AI if the AI gives wrong
recommendations (Nourani et al., 2021). We adjusted the AI’s chances of
recommending best/suboptimal/wrong medicines such that overall AI accuracy
remained at 0.70, despite recommending the best medicine for the first two
questions.

We used analysis of variance to compare across our AI assistance types on our
three metrics, and then Tukey’s HSD for post-hoc pairwise comparisons.

When comparing within-subject (such as how time pressure impacts a
participant’s behaviour), we performed pairwise tests (comparing how time
pressure changed performance for each of the AI-assisted conditions) using the
Holm-Bonferroni correction method (Holm, 1979) to correct for repeating this
analysis on each of the three conditions.

We split participants into two equal groups (overreliers and not-overreliers)
based on their overreliance rate. When predicting if a participant is an
overrelier or not later in the study given whether they were an overrelier
earlier in the study, we used a logistic regression model and ran a $\chi^{2}$
test.

###  3.5. Results

####  3.5.1. Time pressure impacts how participants use AI assistance types
differently (hypothesis H1)

Overall, we find some limited evidence that participants use different AI
assistance types differently under time pressure compared to no time pressure
(hypothesis H1). Performance metrics are summarised in Table 1. Under no time
pressure, all AI-assisted conditions have similar accuracies
($F(2,136)=1.11,p=0.33$). However, under time pressure, we observed a
significant main effect of AI condition on accuracy ($F(2,136)=5.99,p=0.003$).
Specifically, mixed had a lower accuracy than AI-before and AI-after
($p=0.044$ and $p=0.003$ respectively). There is also a significant main
effect of AI condition on response time under time pressure
($F(2,136)=10.3,p<0.0001$), where mixed is also now faster than AI-after
($p=0.006$). Under both time pressure and no time pressure, AI-before is
faster than AI-after ($p=0.018$ under no time pressure, and $p<0.0001$ under
time pressure).

Both under no time pressure and under time pressure, we observed a significant
main effect of AI condition on overreliance (under no time pressure
$F(2,134)=4.89,p=0.0089$, and under time pressure $F(2,136)=5.41,p=0.0055$).
Specifically, both with and without time pressure participants overrelied on
mixed more than AI-after with (no time pressure: $p=0.007$, with time
pressure: $p=0.004$).

We do not find that people overrely more on AI-before under time pressure
compared to no time pressure (hypothesis H1.1): $p=0.13$.

| — No time pressure — | — Time pressure —
---|---|---
Condition | Acc | Time (s) | Overreliance | Acc | Time (s) | Overreliance
No-AI | 0.58(0.05) | 63(11) | — | 0.59(0.04) | 41(5) | —
AI-before | 0.74(0.02) | 48(4) | 0.51(0.05) | 0.72(0.02) | 39(3) | 0.56(0.05)
AI-after | 0.75(0.02) | 65(4) | 0.37(0.04) | 0.75(0.02) | 56(3) | 0.42(0.05)
Mixed | 0.70(0.02) | 61(4) | 0.59(0.06) | 0.65(0.02) | 44(2) | 0.65(0.05)
Table 1. Mean (standard error in parentheses) for our three metrics, averaged
over the shifts that participants are not under time pressure (left) and under
time pressure (right). We find some limited evidence that participants use
different AI assistance types differently in the two settings. Under no time
pressure, all AI-assisted conditions have similar accuracy, while AI-before is
quicker and has higher overreliance. Under time pressure, Mixed has lower
accuracy than the others, and both AI-before and Mixed are quicker and have
higher overreliance than AI-after. See text for details on statistical
analysis.

When we look at these results in more detail, we find that participants who
did not have time pressure in the first block behaved differently on the first
block compared to their second no-time-pressure block. In particular, by the
time they approached their second no-time-pressure block, they had already
completed a block under time pressure, and they performed similarly under no-
time-pressure as they did under time pressure (such as significantly faster
response times). This also likely affected participants who saw a time-
pressure block first: their performance under no-time-pressure was more
similar to the time-pressure blocks. We therefore designed a second experiment
(see Section 4), where participants were either assigned to no-time-pressure
or to time-pressure only. This is more similar to studies in current
literature, where participants usually have no time pressure throughout the
study, and so cannot adapt their behaviour after experiencing the same task
under time pressure.

####  3.5.2. We can predict a participant’s overreliance rate (hypothesis H2)

We find we can predict whether or not a participant was an overrelier in the
second half of the study (the last two shifts) given whether or not they were
an overrelier in the first half of the study (the first two shifts)
($\chi^{2}(1,N=93)=16.28,p<0.0001$), confirming hypothesis H2.1 and providing
evidence towards hypothesis H2.

Given our observation earlier that participants’ behaviour was different
before they saw any time pressure, we repeat the above analysis, this time
trying to predict whether a participant was an overrelier on the last three
shifts given whether or not they were an overrelier on the first shift. When
the first shift was a time-pressure shift, we can predict successfully
($\chi^{2}(1,N=59)=7.47,p=0.0063$). When the first shift was a no-time-
pressure shift, we cannot predict their overreliance group
($\chi^{2}(1,N=41)=0.22,p=0.64$). This further indicates that a participant’s
behaviour changes after experiencing time pressure.

##  4\. Experiment 2: Assigning different participants to time pressure or no
time pressure

In the previous experiment, we found some evidence that people use AI
assistance differently under time pressure compared to no time pressure, but
we found that a participant’s behaviour sticks after completing the task under
time pressure, affecting how they behave under no time pressure later. We
therefore design a second experiment, where each participant was only assigned
to a no-time-pressure condition or time-pressure condition.

We make the same hypothesis regarding behaviour under time pressure,

  1. H1:

Participants use different AI assistance types differently under time pressure
compared to under no time pressure.

    1. H1.1:

On AI-before, people overrely more under time pressure.

As in experiment 1, we expect that we should be able to predict a
participant’s overreliance rate,

  1. H2:

There is some underlying trait that can predict whether or not a person
overrelies more on AI assistance.

    1. H2.1:

We can predict whether a person overrelies more or not in the second half of
the study from their overreliance behaviour in the first half.

We also look in more detail at the mixed condition. We hypothesise to see the
scarcity effect (Noti and Chen, 2022) in the mixed condition, and we measure
this through overreliance rate on the AI-before assistance type. Overreliance
on AI-after may not increase, as the purpose of AI-after is to slow people
down and reduce overreliance, and so we do not hypothesise about it.

  1. H3:

In the mixed condition, overreliance rate on AI-before is higher than for the
pure AI-before condition.

    1. H3.1:

Overreliance rate is higher under no time pressure.

    2. H3.2:

Overreliance rate is higher under time pressure.

We also ask, as a research question, if we can predict a participant’s
overreliance using personality traits that we estimate by asking questions at
the beginning of the study,

  1. RQ1:

Can we use personality traits to predict whether or not a person overrelies
more on AI assistance?

###  4.1. Task description and conditions

The task design was identical to experiment 1 (see Section 3.1). However, this
study was a between-subject design only, with AI-condition (4 options) and
time pressure (2 options) both as the between-subject factors (8 conditions in
total). AI-condition again consisted of four levels (No-AI, AI-before, AI-
after, mixed), same as in Experiment 1. As before, we assigned half the number
of participants to the first (No-AI) condition compared to each of the other
conditions.

The key difference compared to experiment 1 is that time pressure was a
between-subject rather than within-subject factor in this experiment.
Participants now answered questions in a single block of 20 minutes, instead
of four blocks of 5 minutes each. Correspondingly, each participant was
assigned to either be under no time pressure (no timers on the screen), or
time pressure (they see timers shown on screen).

###  4.2. Procedure

As in experiment 1, our study was conducted online on Prolific. The procedure
was the same as in experiment 1 (except now there is just one shift of 20
minutes).

We ran the study with 403 participants on Prolific. Only English speakers in
the US were allowed to participate. 75 people failed the practice questions,
and we removed a further 12 people for either answering questions too quickly
(they spent less than 3 seconds on at least 3 questions), or taking much
longer on one question than the rest (indicating they got distracted for one
question). Our final results are based on the remaining 316 participants.

Participants’ mean age was 39 years (standard deviation of 13 years). 161
participants self-identified as male, 147 as female, 6 as non-binary, and 2
preferred not to say their gender. 111 of the participants’ highest level of
education was high school, 145 had a bachelor’s degree, 40 had Master’s (or
beyond), and 20 answered ‘other’ for highest level of education.

Participants were paid $7 for participating (median time was 37 minutes,
corresponding to $\$11.35$/hr), and if they failed the practice questions,
they were paid $2 (their study ended immediately after failing the practice
questions). We also incentivised participant performance by providing a bonus
$3 reward to the top-performing participant in each condition.

###  4.3. Design and analysis

We report the same three metrics as in Section 3.4: accuracy, response time
and overreliance.

As in Section 3.4, we used analysis of variance to compare across our AI
assistance types on our three metrics, and then Tukey’s HSD for post-hoc
pairwise comparisons.

To compare between time pressure and no time pressure conditions on a specific
condition, we performed a pairwise test for each condition, correcting using
the Holm-Bonferroni method (to correct for the multiple conditions). When
predicting if a participant is an overrelier or not later in the study given
whether they were an overrelier earlier in the study, we used a logistic
regression model and ran a $\chi^{2}$ test.

###  4.4. Results

####  4.4.1. Time pressure impacts how participants use AI assistance types
differently (hypothesis H1)

Figure 2.  Pareto plots: top left is desired (higher accuracy, lower response time per question). We plot the performance (mean and standard error) of our four conditions (No-AI, AI-before, AI-after and mixed) under no time pressure (bold lines) and under time pressure (dotted lines). We see that, under time pressure, all conditions become quicker, but AI-before becomes much quicker than the others, while keeping similar accuracy as the other AI assistance conditions. No-AI reduces accuracy under time pressure. See Table 2 for values.  | — No time pressure — | — Time pressure —
---|---|---
Condition | Acc | Time (s) | Overreliance | Acc | Time (s) | Overreliance
No-AI | 0.66(0.06) | 86(12) | — | 0.51(0.03) | 44(4) | —
AI-before | 0.77(0.02) | 76(6) | 0.41(0.04) | 0.74(0.01) | 41(2) | 0.59(0.04)
AI-after | 0.79(0.02) | 79(5) | 0.36(0.04) | 0.76(0.02) | 55(3) | 0.44(0.05)
Mixed | 0.73(0.03) | 76(5) | 0.50(0.06) | 0.73(0.02) | 54(5) | 0.44(0.05)
Table 2. Mean (standard error in parentheses) for our three metrics on
Experiment 2. Under no time pressure, the AI-assisted conditions have similar
accuracy, response time and overreliance. Under time pressure, AI-before is
quicker and has higher overreliance than the others, while all still have
similar accuracy. Also see Figure 2 to visualise the accuracy-time tradeoffs.
See text for details on statistical analysis.

The effect of time pressure on the conditions is summarised in Figure 2 and
Table 2. We see that all conditions speed up under time pressure. Under no
time pressure, AI-before, AI-after and mixed have similar accuracy
($F(2,135)=1.46,p=0.24$), response time ($F(2,135)=0.15,p=0.86$) and
overreliance ($F(2,135)=2.37,p=0.10$). However, under time pressure, we
observed a significant main effect of AI condition on response time
($F(2,135)=5.09,p=0.0073$) and overreliance ($F(2,135)=4.09,p=0.019$). AI-
before is now quicker than AI-after and mixed ($p=0.02$ for both), and has
higher overreliance rate than AI-after and mixed ($p=0.04$ for both), while
all AI-assisted conditions still have similar accuracy
($F(2,135)=0.80,p=0.45$). This indicates that, under time pressure, AI-before
is better for the accuracy-time tradeoff, while this was not the case under no
time pressure (all assistance types were equally good). This confirms our
hypothesis H1.

We also see that overreliance rate for AI-before increases significantly under
time pressure ($p=0.008$), confirming hypothesis H1.1.

####  4.4.2. We can predict a participant’s overreliance rate (hypothesis H2)

Like in experiment 1 (see Section 3.5.2), we find that we can predict whether
or not a participant is an overrelier in the second half of their block (last
10 minutes) given whether or not they are an overrelier in the first half
(first 10 minutes), both under no time pressure
($\chi^{2}(1,N=130)=19.6,p<0.0001$) and under time pressure
($\chi^{2}(1,N=136)=16.1,p<0.0001$). This confirms hypothesis H2.1, and
indicates that a participant’s overreliance behaviour remains stable over the
course of study.

We also see if overreliance and response time are related, expecting that
overreliers are quicker to answer questions, as further evidence that
overreliance may be stable for a participant. We find that we can predict
whether or not a participant is an overrelier given their average response
time (we test if there is sufficient evidence for Pearson’s correlation
coefficient to not be zero, finding overreliance rate response time are
negatively correlated, $r(136)=-0.04,p=0.0008$ under time pressure, and
$r(136)=-0.02,p<0.0001$ under no time pressure.

####  4.4.3. There is a scarcity effect under no time pressure, but not under
time pressure (hypothesis H3)

The performance of AI assistances under the mixed condition are summarised in
Figure 3. We find that, under no time pressure, people overrely on mixed AI-
before significantly more than pure AI-before ($p=0.001$). This confirms
hypothesis H3.1, and is the scarcity effect as observed in Noti and Chen
(2022).

However, under time pressure, people do not overrely on mixed AI-before more
than pure AI-before, meaning we do not find evidence to support hypothesis
H3.2. This indicates that the scarcity effect is not additive with time
pressure: time pressure already increases overreliance, and the scarcity
effect does not increase this further.

(a) Under no time pressure.

(b) Under time pressure.

Figure 3.  Pareto plots: top left is desired (higher accuracy, lower response
time per question). We compare the performance (mean and standard error) of
the AI assistances in the mixed condition (dotted lines) to the pure AI
assistance-only conditions (bold lines). We see that, under no time pressure
(left), mixed AI-before is marginally faster than pure AI-before (with
significantly higher overreliance, see main text), and mixed AI-after is
marginally slower than pure AI-after. Under time pressure (right), mixed AI-
before now has the same response time as pure AI-before, and mixed No-AI has
higher accuracy than pure No-AI.

Looking into the accuracy-time tradeoff, we see that mixed AI-before is always
significantly faster than mixed AI-after. Under no time pressure, mixed AI-
before’s response time of $60(7)$ seconds (standard error reported in
parentheses) is quicker than AI-after’s $95(6)$ seconds ($p<0.0001$). Under
time pressure, mixed AI-before’s response time of $43(4)$ seconds is quicker
than AI-after’s $66(5)$ seconds ($p=0.017$).

####  4.4.4. We can only marginally predict overreliance from personality
traits (RQ1)

Given that we have evidence for overreliance being stable for a participant,
we see if we can predict their overreliance behaviour from well-known
personality traits. We try to predict whether or not a participant is an
overrelier or not based on the Big-5 Personality Traits, Need-for-Cognition
(NFC) trait, and self-reported time-pressure performance (see Section 3.1 for
more details on these traits), and test if Pearson’s correlation coefficient
is not zero. These personality traits are estimated based on questions we
asked at the beginning of the study. We do not find any significant results,
but do find some traits that can predict overreliance with marginal
significance. Under no time pressure, neuroticism and NFC are marginally
negatively correlated with predicting overreliance group
($r(136)=-0.28,p=0.07$ for neuroticism, $r(136)=-0.31,p=0.10$ for NFC). Under
time pressure, neuroticism is still marginally negatively correlated
($r(136)=-0.23,p=0.16$), and time-pressure-score is marginally positively
correlated ($r(136)=0.35,p=0.051$). Future work is required to test if these
traits (or a combination of them) are predictive of overreliance behaviour.

##  5\. Exploratory Analysis: Adapting AI assistance to the person and task

In this section, we use the data from Experiment 2 (Section 4) to explore how
we might adapt AI assistance depending on both the person (whether they are an
overrelier or not) and the task (the difficulty of the question, which is
either easy or hard).

We first split people into two equal groups (overreliers and not-overreliers)
in Section 5.1, and find that not-overreliers achieve human-AI complementarity
(higher accuracy than both No-AI accuracy and AI-only accuracy), while
overreliers do not. This highlights the usefulness of splitting people into
these two groups: the two use AI assistance differently (one to achieve
complementarity, while the other overrelies on it). Therefore adapting AI
assistance to these groups should be useful. We then see if, after splitting
people by their overreliance behaviour in the first half of the study, their
accuracy in the second half of the study achieves human-AI complementarity. We
again find that not-overreliers achieve complementarity, while overreliers do
not, indicating that overreliance rate (and its corresponding behaviour) is
stable during the study.

We then explore adapting to both the person (whether they are an overrelier or
not) and the question (whether the question is easy or hard) in Section 5.2,
focussing on the setting where people are under time pressure. We find that we
can improve accuracy by slowing down overreliers with AI-after on hard
questions. For not-overreliers however, AI-before is usually faster than AI-
after, with similar accuracy.

###  5.1. Some people achieve human-AI complementarity, but others do not

In this section, we compare participants’ accuracy with AI assistance against
No-AI accuracy and AI-only accuracy, to see if participants achieve human-AI
complementarity (greater accuracy than both No-AI and AI-only). We find that,
when we split the participants into two groups, overreliers and not-
overreliers, the not-overreliers achieve human-AI complementarity (both under
time pressure and not), while the overreliers do not.

Results are summarised in Table 3. When comparing against No-AI, we first use
analysis of variance, and if there is significance, we perform pairwise
comparisons using Tukey’s HSD. When comparing against AI-only accuracy, we
conduct a within-subject t-test, as we know the AI-only accuracy for each
participant, and correct using the the Holm-Bonferroni method for the three
conditions.

|  | No time pressure | Time pressure
---|---|---|---
| Condition | ¿ No-AI acc? | ¿ AI acc? | ¿ No-AI acc? | ¿ AI acc?
Overreliers |  | $F_{3,81}=2.25,p=.089$ |  | $F_{3,91}=22.4,p<.0001$ |
| AI-before | n.s. | n.s. | Yes ($p<0.0001$) | n.s.
| AI-after | n.s. | n.s. | Yes ($p<0.0001$) | n.s.
| Mixed | n.s. | Yes ($p=0.0061$) | Yes ($p<0.0001$) | n.s.
Not-overreliers |  | $F_{3,83}=5.68,p=.0014$ |  | $F_{3,89}=27.5,p<.0001$ |
| AI-before | Yes ($p=0.041$) | Yes ($p<0.0001$) | Yes ($p<0.0001$) | Yes ($p=0.0023$)
| AI-after | Yes ($p=0.001$) | Yes ($p<0.0001$) | Yes ($p<0.0001$) | Yes ($p=0.0035$)
| Mixed | Yes ($p=0.012$) | Yes ($p<0.0001$) | Yes ($p<0.0001$) | Yes ($p=0.0035$)
Table 3. Comparing accuracy of the AI assistance conditions with No-AI
accuracy and AI-only accuracy after splitting participants by overreliance. We
calculate all accuracies and whether a participant is an overrelier or not
over the course of the entire 20-minute study. We see that, both under no time
pressure and under time pressure, overreliers do not significantly achieve
human-AI complementarity (accuracy is not significantly higher than both No-AI
accuracy and AI-only accuracy), while not-overreliers do achieve
complementarity.

Our findings indicate that we should consider adapting what AI assistance we
show depending on whether the participant is an overrelier or not, as these
two groups use AI assistances in different ways. For example, overreliers may
benefit from AI assistance types that slow them down and force them to engage
more with the AI. We investigate this further in our setting in Section 5.2.

We earlier saw that we can predict whether a participant is an overrelier or
not in the second half of the study from whether or not they were an
overrelier in the first half (Sections 3.5 and 4.4), indicating that their
overreliance behaviour is stable during the study. We now see if, after
classifying people as overreliers or not-overreliers based on the first half
of the study, not-overreliers still achieve human-AI complementarity in the
second half of the study, while overreliers do not. Results are summarised in
Table 4 (in Appendix A): not-overreliers achieve complementarity (except that,
under no time pressure, the AI-before condition now has only marginally higher
accuracy than No-AI ($p=0.06$)), while overreliers do not achieve
complementarity. This provides further evidence that overreliance rate (and
its corresponding behaviour) is stable during the study.

###  5.2. Adapting AI assistance to both the person and task under time
pressure

(a) Overreliers.

(b) Not-overreliers.

Figure 4.  Pareto plots: top left is desired (higher accuracy, lower response
time per question). We compare the performance (mean and standard error) of
the AI assistances in the mixed condition on easy questions (bold lines) to
hard questions (dotted lines) under time pressure. We see that for overreliers
(left) on hard questions, there is a tradeoff between AI-before and AI-after,
and for not-overreliers (right) there is a marginal tradeoff on easy
questions. Otherwise, AI-before is better (equal accuracy and quicker time
compared to AI-after and No-AI).

In this section, we explore how we can adapt to both the person (whether they
are an overrelier or not) and the question (whether the question is easy or
hard). We focus on the setting where people are under time pressure. Because
we are interested in changing the assistance type depending on the question,
we only look at how AI assistance types inside the mixed condition compare
against each other. We find that we can increase accuracy of overreliers by
slowing them down with AI-after on hard questions (compared to their accuracy
and response time on AI-before). However, the not-overreliers have similar
accuracy with both AI-before and AI-after, and are quicker with AI-before.

Figure 4 shows the tradeoffs. For overreliers (Figure 4(a)), we see that on
easy questions, they are quicker with AI-before than AI-after, with similar
accuracy. However, on hard questions, AI-after can increase their accuracy
(from 55% to 70%) by slowing them down. For not-overreliers (Figure 4(b)), on
easy questions, they have similar accuracy and time for AI-before and AI-after
(AI-after has marginally higher accuracy). On hard questions, AI-before helps
the not-overreliers to both speed up and achieve good accuracy.

These findings suggest that we can adapt what AI assistance we show depending
on the person (overrelier or not-overrelier, which we can predict from the
first part of the study) and the question (easy or hard, which we may already
know, or which we can estimate, for example based on how uncertain the AI
model is on the task). Future work can continue to look at adapting AI
assistance in similar ways, and see if such adaptation improves the overall
accuracy-time tradeoff. Future work could also look at more advanced machine
learning techniques to quickly adapt, such as reinforcement learning
algorithms.

##  6\. Discussion

This work focuses on the accuracy-time tradeoff of different AI assistances,
and how these change under time pressure. Previous work compared different AI
assistances focusing only on accuracy, but we consider response time to also
be an important metric: for example when using AI assistance in time-pressured
scenarios, or scenarios where we hope the AI assistance will speed up human
decision-making. We specifically consider the setting where people are under
time pressure (such as making decisions in an emergency room (Patel et al.,
2008; Franklin et al., 2011; Rundo et al., 2020)), where the accuracy-time
tradeoff is particularly important. Some recent works have shown benefits of
adapting the AI assistance to maximise accuracy (Noti and Chen, 2022; Ma et
al., 2023; Bhatt et al., 2023), and we are interested in doing the same for
the accuracy-time tradeoff.

###  6.1. The effect of time pressure in AI-assisted decision-making

We find that introducing time pressure can change the accuracy-time tradeoff
between different AI assistances, making our AI-before condition significantly
faster than the AI-after and mixed conditions. In our first experiment
(Section 3), the same participant was alternately put under time pressure and
no time pressure. We found that once a participant had been under time
pressure once, their performance remained similar even if they were not under
time pressure later in the study. Further work is required to investigate why
the same person’s behaviour changes after they experience time pressure on the
task: they appear to learn how to perform the task quickly, and retain this
behaviour, even if that reduces accuracy. In our second experiment (Section
4), we found that different AI assistances are more effective under time
pressure compared to no time pressure.

Cao et al. (2023) looked at the effect of time pressure on AI-after only, and
how time pressure affects reliance on AI recommendations. They found that
reduced observation time (before participants make an initial decision) did
not consistently lead to increased reliance, while reduced decision time (the
time given to a participant to update their initial decision based on an AI
input) usually led to reduced reliance. In our studies, we did not
independently test the effect of time pressure on these two phases of AI-after
decision-making. We found that overreliance on AI-after increased marginally
under time pressure, but this is likely impacted by exactly how time-pressured
participants were in each phase of decision-making.

Although we investigated the effect of time pressure in our studies, we expect
that other types of pressure will also affect how people use AI assistances,
such as social pressure (Byrne et al., 2015) or other stressors. Given that
such AI assistance tools are increasingly being used in the workplace, where
people are under different types of pressure, it is important to understand
how effective different AI assistances are in such contexts.

###  6.2. Overreliance rate as an important individual trait

Previous works have found evidence that, in medical settings, experts with
less domain experience trust AI more than experts with more experience (Gaube
et al., 2021; Sarah Bayer and Markgraf, 2022). This may result in different
overreliance rates between people. Although all participants are equally
expert in our setting, we still find that a person’s overreliance rate is
stable throughout the duration of the study, and that overreliers and not-
overreliers might use different AI assistances differently. This could be due
to different factors, and future work could investigate why overreliers behave
differently to not-overreliers, looking at the different mechanisms these two
groups of people might be using. For example, are not-overreliers using AI
assistance as a guide to check information, before making their own decision?
Such mechanisms are likely to also depend on the specific task.

Although we find evidence that overreliance is an individual trait, we find
only marginal correlations between overreliance behaviour and personality
traits (Big-5 Personality Traits, NFC, and self-reported belief of performance
under time pressure). This is unlike prior work that found overreliance to be
significantly correlated with NFC (Buçinca et al., 2021). This may partly be
due to the nature of our task: our task was game-like and inherently
motivating, meaning even people low in NFC may have been motivated to
cognitively engage with the task. Previous work has also found that
neuroticism affects people’s performance on tasks under pressure (Byrne et
al., 2015), but we only found marginal correlations to support this. A
potential reason is that the our personality traits were measured using only a
few questions, meaning they are likely noisy estimates, and so we cannot be
confident that correlations between personality traits and performance do not
exist in practice.

Overall, we believe that to successfully adapt assistance to decision-makers,
we need to predict how that decision-maker will use the assistance. This can
be done before any interaction with the system, or it can be done in real-
time. We sought to understand if the overreliance trait could be captured
beforehand (e.g., by personality variables), and were unable to do so
conclusively. However, support can be tailored as the person interacts with AI
in real-time, allowing a system to update its usage of AI assistances
accordingly.

###  6.3. Adapting AI assistance under time pressure

Our findings suggest that we can adapt what AI assistance to show depending on
the characteristics of the person (whether they are an overrelier or not-
overrelier) and the task (easy or hard). For example, we can increase the
accuracy of overreliers by slowing them down with AI-after assistance on hard
questions, while showing AI-after to not-overreliers does not significantly
increase accuracy (and only slows them down).

Adapting AI assistance is especially important in time-pressured scenarios
because achieving appropriate reliance on AI is crucial: we want to encourage
a person to follow a correct AI suggestion in order to save time; conversely,
when the task is more difficult and the AI assistant may be incorrect, we may
want to slow down the person to ensure they still make the correct decision.
Appropriate reliance is also important to achieve human-AI complementarity
(Bussone et al., 2015; Lai and Tan, 2019; Jacobs et al., 2021).

Previous work has argued that overreliance (or reliance) on AI can vary
between different AI assistances and conditions: AI assistances that force
people to slow down and cognitively engage with the AI assistance (like AI-
after) can reduce overreliance (Buçinca et al., 2021), and when an AI
assistance is shown less often (like when adaptively showing AI assistances),
people rely on it more (the scarcity effect) (Noti and Chen, 2022). In our
study, we observe the scarcity effect when there is no time pressure, but find
that the scarcity effect disappears under time pressure. Participants already
overrely more on the AI when under time pressure, and the scarcity effect does
not compound with this increased overreliance. In our setting, participants
experienced fairly strong time pressure: it is possible that, under less time
pressure, there may still be a (smaller) scarcity effect. We also note that we
found a scarcity effect with AI-before only, and did not find it with AI-after
(which Noti and Chen (2022) did find). We believe this is because people
needed much longer for each question in our setup (around 50-60 seconds as
opposed to 10 seconds per question). This means that people may trust their
own initial answer more in our setting, leading to less reliance with AI-
after.

We can likely further increase the performance of the human-AI team by
adapting to more individual traits and properties of the task, although we did
not consider them in this paper. First, it is possible that some people are
more strongly affected by anchoring biases towards information they see,
making them more susceptible to relying on AI-before, and hence changing what
AI assistance is best for them. We may be able to estimate such biases using
either personality traits or estimating it online, as the person interacts
with AI. Second, a person’s overall skill or knowledge of a task may affect
both how much they require AI assistance, and how they use the AI assistance.
Third, different properties of the task may matter (on top of difficulty of
task): it may be more important to get certain tasks right (for example, if a
patient arrives with a life-threatening condition); or maybe the AI model is
known to be worse in certain settings (such as if the AI model is biased on
certain tasks only), and so cannot be relied on as much. Adapting to all such
properties is likely difficult, and requires further study and perhaps more
advanced personalisation algorithms (such as using reinforcement learning
algorithms).

###  6.4. Limitations

In our experiments, participants had to complete a series of logic puzzles,
where all information was shown on the screen. This allowed us to precisely
manipulate the difficulty of the task (such as by having easy and hard
questions), the optimality of the AI assistance, and the form of time
pressure. However, a key limitation of our work is that such a setting may not
be realistic: in many real settings, prior knowledge of aspects of the task is
important. Because the logic puzzles are new and different, participants may
always be in System II thinking (Kahneman, 2011), reducing the effect of
cognitive forcing functions like AI-after (previous work has argued AI-after
reduces overreliance because it can push people from System I to System II
thinking (Buçinca et al., 2021)). Additionally, our task is in a non-critical
setting, unlike other realistic time-pressured scenarios like doctors in
emergency rooms (Patel et al., 2008; Franklin et al., 2011; Rundo et al.,
2020). Previous work has suggested that experts may have similar behaviour as
participants in crowd-based studies (Gaube et al., 2021), but we do not know
if this will also hold under time pressure.

The AI explanation that we showed participants (along with the AI
recommendation) in our setting was an intermediate symptom that led to the AI
recommendation, and was easily verifiable. This verifiability might impact how
people use AI assistances (Fok and Weld, 2023). Different forms of AI
explanations may lead to different results. We also only compared two forms of
AI assistance (AI-before and AI-after). We chose these because AI-before is
widely-used, and AI-after can slow people down and potentially reduce
overreliance. Future work could consider how people act under time pressure
with other AI assistance types. For example, does providing only an AI
recommendation (without an explanation) lead to similar results? Do other
cognitive-forcing functions like “on-demand” (Buçinca et al., 2021) (where
participants have the option to click to see the AI input) lead to similar
accuracy-time tradeoffs as AI-after under time pressure?

##  7\. Conclusion

Using AI assistances to help decision making in increasingly different
environments and situations requires understanding the effects of the
different AI assistances in each setting. In this paper, we consider how
different AI assistances impact both accuracy and response time per question,
leading to accuracy-time tradeoffs between different AI assistances. Such
tradeoffs are especially important in situations were users are time-
pressured, and we consider this in more detail.

In our experiments, we found that introducing time pressure can change the
accuracy-time tradeoff between different AI assistances, making our AI-before
condition significantly faster than the AI-after and mixed conditions. We also
found that people’s overreliance rates are stable over the course of the
study, and that overreliers and not-overreliers may be using AI assistances
differently.

We also found that a previously documented scarcity effect (Noti and Chen,
2022), which describes an increase in reliance on AI assistance when the AI
assistance is shown less often, disappears when users are under time pressure.
This is particularly important when we are considering adapting AI assistance
depending on the person and task, and further indicates that such adaptations
likely need to also depend on whether there is time pressure. Lastly, in our
exploratory analysis we found initial ways in which we might adapt our AI
assistance depending on both the person (their overreliance rate) and the task
(easy or hard task).

Overall, we find evidence that different scenarios (here, the effect of time
pressure) may change how people use different AI assistances relative to each
other, making some better to use than others. This suggests that we need to be
careful when applying results from one setting (such as when there is no time
pressure) to another setting (such as when there is time pressure).
Additionally, we also find evidence that we can adapt AI assistance to both
user and task, adding to a growing body of literature that uses adaptive AI
assistance instead of a fixed AI assistance.

###### Acknowledgements.

This material is based upon work supported by the National Science Foundation
under Grant No. IIS-2107391. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of the National Science Foundation. ZB was
partially supported by an IBM PhD Fellowship.

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##  Appendix A Appendix

|  | No time pressure | Time pressure
---|---|---|---
| Condition | ¿ No-AI acc? | ¿ AI acc? | ¿ No-AI acc? | ¿ AI acc?
Overreliers |  | $F_{3,78}=1.73,p=0.17$ |  | $F_{3,90}=12.4,p<.0001$ |
(according to first | AI-before | n.s. | n.s. | Yes ($p<0.0001$) | n.s.
half of study) | AI-after | n.s. | n.s. | Yes ($p<0.0001$) | n.s.
| Mixed | n.s. | n.s. | Yes ($p<0.0001$) | n.s.
Not-overreliers |  | $F_{3,82}=7.47,p=.0002$ |  | $F_{3,88}=28.1,p<.0001$ |
(according to first | AI-before | n.s. | Yes ($p=0.0063$) | Yes ($p<0.0001$) | Yes ($p<0.0001$)
half of study) | AI-after | Yes ($p<0.0001$) | Yes ($p<0.0001$) | Yes ($p<0.0001$) | Yes ($p=0.0068$)
| Mixed | Yes ($p=0.047$) | Yes ($p=0.0017$) | Yes ($p<0.0001$) | Yes ($p=0.014$)
Table 4. Comparing accuracy of the AI assistance conditions with No-AI
accuracy and AI-only accuracy after splitting participants by overreliance. We
calculate whether a participant is an overrelier or not based on performance
in the first half of the study, and calculate all accuracies only in the
second half of the study. We see that, both under no time pressure and under
time pressure, overreliers do not significantly achieve human-AI
complementarity (accuracy is not significantly higher than both No-AI accuracy
and AI-only accuracy), while not-overreliers do achieve complementarity
(except that AI-before under no time pressure now only has marginally higher
accuracy than No-AI ($p=0.06$)). This is similar to Table 3, but now we use
the first half of the study to predict in the second half, indicating that
overreliance behaviour is stable during the course of the study. Figure 5. The
alien prescription task, where participants must prescribe a single medicine.
In this example, the timers are shown on the screen (the global timer counts
down from 20 minutes, as it is part of Experiment 2 (Section 4)). This
question is a hard-difficulty question, whereas the question in Figure 1 was
easy-difficulty. It is hard because the optimal medicine (which, in this case,
the AI recommends correctly to be ‘tranquilizers’) uses fewer observed
symptoms than the other medicines. Therefore, if a participant wants to
confirm that ‘tranquilizers’ is the optimal medicine, they have to check many
other medicines too (‘optimal’ is defined as the medicine that uses the most
observed symptoms while not using/treating any unobserved symptoms). For this
alien, vitamins is also a correct medicine, but it is suboptimal. All other
medicines are incorrect.