# A Mixed-Methods Approach to Understanding User Trust after Voice Assistant
Failures

Amanda Baughan<EMAIL_ADDRESS>University of WashingtonSeattleWAUSA
,  Allison Mercurio<EMAIL_ADDRESS>Google ResearchMountain ViewCAUSA ,
Ariel Liu<EMAIL_ADDRESS>Google ResearchMountain ViewCAUSA ,  Xuezhi
Wang<EMAIL_ADDRESS>Google ResearchNew YorkNYUSA ,  Jilin Chen
<EMAIL_ADDRESS>Google ResearchMountain ViewCAUSA and  Xiao Ma
<EMAIL_ADDRESS>Google ResearchNew YorkNYUSA

(2023)

###### Abstract.

Despite huge gains in performance in natural language understanding via large
language models in recent years, voice assistants still often fail to meet
user expectations. In this study, we conducted a mixed-methods analysis of how
voice assistant failures affect users’ trust in their voice assistants. To
illustrate how users have experienced these failures, we contribute a
crowdsourced dataset of 199 voice assistant failures, categorized across 12
failure sources. Relying on interview and survey data, we find that certain
failures, such as those due to overcapturing users’ input, derail user trust
more than others. We additionally examine how failures impact users’
willingness to rely on voice assistants for future tasks. Users often stop
using their voice assistants for specific tasks that result in failures for a
short period of time before resuming similar usage. We demonstrate the
importance of low stakes tasks, such as playing music, towards building trust
after failures.

voice assistants, trust, survey, interview, dataset

††journalyear: 2023††copyright: rightsretained††conference: Proceedings of the
2023 CHI Conference on Human Factors in Computing Systems; April 23–28, 2023;
Hamburg, Germany††booktitle: Proceedings of the 2023 CHI Conference on Human
Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg,
Germany††doi: 10.1145/3544548.3581152††isbn: 978-1-4503-9421-5/23/04††ccs:
Human-centered computing Empirical studies in HCI

##  1\. Introduction

Voice assistants have received a lot of attention from both industry and
academia, especially given the recent advances in natural language processing
(NLP). Within the past five years, advancements in NLP have achieved huge
gains in accuracy when tested against standard datasets (Devlin et al., 2018;
Liu et al., 2019; Brown et al., 2020; Vaswani et al., 2017; Smith et al.,
2022; Wei et al., 2021; Thoppilan et al., 2022), with state-of-the-art
accuracy in natural language processing models as high as 99% for certain
tasks (Liu et al., 2019; Brown et al., 2020). This has led many practitioners
and researchers alike to imagine a near future where voice assistants can be
used in increasingly complex ways, including supporting healthcare tasks
(Sezgin et al., 2020; Mehandru et al., 2022), giving mental health advice
(Yang et al., 2021; Saha et al., 2022), and high stakes decision-making (De
Melo et al., 2020).

However, despite the increasing accuracy of NLP models and the breadth of
their applications, evidences suggest that users remain reluctant and
distrusting of using voice assistants (Condliffe, 2017; Hunter, 2022). In the
U.S., voice assistants are common in homes, with an estimated 72% of Americans
having used a voice assistant (Hayes et al., 2018). However, people primarily
use these for basic tasks such as playing music, setting timers, and making
shopping lists (Condliffe, 2017; Hunter, 2022; Luger and Sellen, 2016). This
is because when voice assistants fail, such as by incorrectly answering a
question, it derails user trust (Hayes et al., 2018; Luger and Sellen, 2016).
User trust is pivotal to user adoption of various technologies (Bahmanziari et
al., 2003), and in this case, low user trust results in reluctance to try
voice assistants’ novel capabilities.

As voice assistants increasingly rely on large language models (Pinsky, 2021;
FitzGerald et al., 2022), we believe the gap between the high accuracy of
these models and users’ reluctance to use voice assistants for complex tasks
may be explained by differences in how users and NLP practitioners evaluate
the success of a model. Standard NLP models are often evaluated on large
datasets of coherent text-based questions and answers (Rajpurkar et al., 2016;
Rajpurkar et al., 2018) or paired written dialogue (Zhang et al., 2018).
Meanwhile, in practice users’ speech may include disfluencies, such as
restarts and filler words, questions not covered in training, or background
noise which misconstrues speech. In the case of question answering, NLP models
are evaluated based on how many questions are accurately answered on a subset
of the training dataset (Rajpurkar et al., 2016; Rajpurkar et al., 2018). As
one may expect, people can interact with voice assistants in a multitude of
ways that fall outside of the scope of training data, which can lead to
friction. In the eyes of users, these inaccurate responses, or voice assistant
failures, can lead to frustration. For example, only five percent of users
report never becoming frustrated when using voice search (Cox, 2020).

We believe that the gap between how NLP models are evaluated and how users
encounter and perceive failures hinders the practical applications of the
advancements that voice assistants have made. Therefore, we ask, which types
of voice assistant failures do users currently experience, and how do these
failures affect user trust? A human-centered understanding of the types of NLP
failures that occur and their impact on users trust would allow technologists
to prioritize and address critical failures and enable long-term adoption of
voice assistants for a wider variety of use cases.

Further, while research has started to categorize types of breakdowns in
communication between users and NLP agents (Hong et al., 2021; Paek and
Horvitz, 2013), little work has looked into how users perceive these failures
and subsequently trust and use their voice assistants. We draw from and extend
past research to make the following contributions:

  * •

C1: Iterating on the existing taxonomy of NLP failures, we crowdsource a
dataset of 199 failures users have experienced across 12 different sources of
failure.

  * •

C2: A qualitative and quantitative evaluation on how these different failures
affect user trust, specifically along dimensions of ability, benevolence, and
integrity.

  * •

C3: A qualitative and quantitative analysis on how trust impacts intended
future use.

To accomplish this, we developed a mixed-methods, human-centered investigation
into voice assistant failures. We first executed interviews with 12 voice
assistant users to understand what types of failures they have experienced and
how this affected their trust and subsequent use of their assistant. We
concurrently crowdsourced a dataset of failures from voice assistant users on
Amazon Mechanical Turk. Finally, we executed a survey to quantify how
different types of failures impact users’ trust in their voice assistants and
their willingness to use them for various tasks in the future.

We found that different types of voice assistant failures have a differential
impact on trust. Our interviews and survey revealed that participants are more
forgiving of failures due to spurious triggers or ambiguity of their own
request. In the case of spurious triggers, the voice assistant activates due
to mishearing the activation phrase when it was not said. Users forgave this
more easily, as it did not hinder them from accomplishing a goal. Failures due
to ambiguity occurred when there were multiple reasonable interpretations of a
request, and the response was misaligned with what the user intended while
still accurately answering the question. Users tended to blame themselves for
these failures. However, failures due to overcapture more severely reduced
users’ trust, as when the voice assistant continued listening without any
additional input, users considered their use a waste of time.

We additionally find that on many occasions, users would discontinue using
their voice assistant for a specific task for a short period of time following
a failure, and then resume again once trust had been rebuilt. Trust was often
rebuilt by using the voice assistant for tasks they considered simple, such as
playing music, or alternatively, using the voice assistant for the same
general task but in a different use case. In addition to these findings, we
release a dataset of 199 voice assistant failures, capturing user input, voice
assistant response, and the context for the failure, so that researchers may
use these failures for future research on how users respond to voice assistant
failures. As voice assistants continue to perform increasingly complex and
high stakes tasks across various industries (Mehandru et al., 2022; Sezgin et
al., 2020; De Melo et al., 2020; Yang et al., 2021; Robertson and Díaz, 2022),
we hope that this research will help technologists understand, prioritize, and
address natural language failures to increase and maintain user trust in voice
assistants.

##  2\. Related Work

Prior research across many fields has examined the interaction between users
and voice assistants, including human-computer interaction, human-centered AI,
human-robotics interaction, science and technology studies (STS), computer-
mediated communication (CMC), and social psychology. In addition, some work in
natural language processing (NLP), especially NLP robustness, has approached
technology failures in voice assistants and developed certain technical
solutions to address them. Here, we provide an interdisciplinary review of
research relevant to voice assistant failures during user interaction across
these fields. The literature review is organized as follows: 1) literature on
user expectations and trust in voice assistants; 2) human-computer interaction
(HCI) approaches to understanding voice assistant failures and strategies for
mitigation; 3) natural language processing (NLP) approaches to voice assistant
failures, including disfluency and robustness.

###  2.1. User Expectations and Trust in Voice Assistants

Researchers have long tried to understand how people interact with automated
agents, especially comparing and contrasting these experiences with human-to-
human communication. When talking with other humans, conversations can broadly
be understood as functional (also known as transactional or task-based) or
social (interactional), and many conversations include a mix of both (Clark et
al., 2019). Functional conversations serve towards the pursuit of a goal, and
those who participate often have understood roles towards the pursuit of that
goal. In contrast, social conversations have a goal of building,
strengthening, or maintaining a positive relationship with one of the
participants. These social conversations can help build trust, rapport, and
common ground (Clark et al., 2019).

People generally expect to have functional conversations with voice assistants
(Clark et al., 2019). The lack of social conversations may reduce users’
ability to build trust in their voice assistants. Indeed, past research has
shown that users trust embodied conversational agents more when they engage in
small talk (Bickmore and Cassell, 2001), although this varies by user
personality type and level of embodiment of the agent (Bickmore and Cassell,
2005). As it stands, people report not using voice assistants for a broad
range of tasks, even though they’re technically capable of doing so (Hayes et
al., 2018). Prior work has illustrated the importance of trust for continued
voice assistant use (Luger and Sellen, 2016; Lahoual and Frejus, 2019), as
trust is pivotal to user adoption of voice assistants (Nasirian et al., 2017;
Lee et al., 2021) and willingness to broaden the scope of voice assistant
tasks (Hayes et al., 2018). It is especially important to support trust-
building between users and voice assistants as researchers continue to imagine
and develop new capabilities for them, including complex tasks such as
supporting healthcare tasks (Sezgin et al., 2020; Mehandru et al., 2022),
giving mental health advice (Yang et al., 2021; Saha et al., 2022), and other
high stakes decision-making (De Melo et al., 2020).

This then begs the question of how trust is built between users and voice
assistants. Trust in machines is an increasingly important topic, as use of
automated systems is widespread (Winter and Carusi, 2022). Concretely, trust
can be conceptualized as a combination of confidence in a system as well as
willingness to act on its provided recommendations (Seymour and Van Kleek,
2021; Madsen and Gregor, 2000). Prior researchers have examined trust in
machines in terms of people’s confidence in a machine’s ability to perform as
expected, benevolence (well-meaning), and integrity to adhere to ethical
standards (Ma et al., 2017) Broadly, past research has evaluated how various
factors such as accuracy and errors affect people’s trust in algorithms (Yin
et al., 2019; Dzindolet et al., 2002; Dietvorst et al., 2015). In the case of
voice assistants, Nasirian et al. (2017) and Lee et al. (2021) studied how
quality affects trust in and adoption of voice assistants, and found that
information and system quality did not impact users’ trust in a voice
assistant, but interaction quality did. Interaction quality was captured based
on a study by Ekinci and Dawes (2009), in which Likert scale responses were
captured regarding competence, attitude, service manner, and responsiveness of
the voice assistant. In addition, customizing a voice assistant’s personality
to the user can lead to higher trust (Braun et al., 2019), while gender does
not impact users’ trust in a voice assistant (Tolmeijer et al., 2021).
Overall, prior research demonstrates the importance of the interaction quality
and social conversations for building trust between users and voice
assistants, which in turn affects users’ willingness to continue using them
and broaden the scope of their tasks.

###  2.2. HCI Approaches to Voice Assistant Failures

However, there are occasionally unforeseen breaches of trust, as not all
interactions go as smoothly as one expects. Prior work has explored the
diversity of issues affecting engagement and ongoing use of voice assistants
and has shown that when users have expectations for voice assistants that
surpass its capabilities, voice assistant failures and user frustration ensues
(Luger and Sellen, 2016; Lahoual and Frejus, 2019).

This begs the question, how has prior work defined failures in voice
assistants? Some work uses specific scenarios in their studies. For example,
Lahoual and Frejus (2019) conducted evaluation in domestic and driving
situations. They identified failures due to poor voice recognition, limited
understanding of a command, and connectivity. Cuadra et al. (2021b) used
failures in specific tasks, such as attempting to give directions to an
incorrect location, send a text to the wrong person, play the wrong type of
music, or adding a reminder with an incorrect detail (Cuadra et al., 2021b).
Mahmood et al. (2022) simulated online shopping, in which an AI assistant with
a voice component would fail by using homonyms of the requested items. For
example, the ambiguous item “bow” could mean a hair bow, archery bow, or bow
for gift wrapping. Salem et al. (2015) had participants control a robot’s
movement, and in the faulty condition, the robot would move erratically,
incorrectly responding to the users’ input. Candello et al. (2019) defined
failure as occasions in which someone asked a question that could not be
understood or was out of scope of the voice assistants’ knowledge, in which
case it would divert the conversation to ask an unrelated question.

Other research aims to provide a broad categorization of voice assistant
failures, drawing from theoretical frameworks of communication between humans
(Hong et al., 2021; Paek and Horvitz, 2013; Clark, 1996). We reference Herbert
Clark’s grounding model for human communication, which relies on four
different levels to achieve mutual understanding: channel, signal, intention,
and conversation (Clark, 1996). This was expanded by Paek and Horvitz (2013),
which applied these four levels to human-machine interactions and failure
points. Channel level errors include when an AI fails to attend to a users’
attempt to initiate communication; signal level errors include an error in
capturing user input (e.g. due to transcription); intention level errors
include mistakes in making sense of the semantic meaning of the transcribed
input; and conversation level errors occur when a user has requested an
unknown action to the AI (e.g. asking a weather app to schedule something).
Hong et al. (2021) built on this model, specifically restricting the context
to NLP failures, rather than AI as a whole. Based on interviews with NLP
practitioners, they renamed the categories as attention (channel), perception
(signal), understanding (intention), and response (conversation). Hong et al.
(2021) focused on failures that are either very common, or rare but very
costly, to cover the most important and frequent failures users encounter when
interacting with NLP-based systems. In this work, we build on their existing
taxonomy of NLP failures (Hong et al., 2021), narrowing the use case to only
voice assistant failures, and evaluating how different failures impact on user
trust and future intended use.

There is currently little systematic evaluation of the impact of voice
assistant failures on user trust. Salem et al. (2015) found that if a robot
had faulty performance, this did not influence participants’ decisions to
comply with its requests, but it did significantly affect their perceptions of
the robot’s reliability and trustworthiness. Mahmood et al. (2022) found that
voice assistants that accepted blame and apologized for mistakes were thought
to be more intelligent, likeable, and effective in recovering from failures
than assistants that shifted the blame.

Sometimes after a failure, users will try to reformulate, simplify, or hyper-
enunciate their commands as a way to continue using the device (Lahoual and
Frejus, 2019; Luger and Sellen, 2016; Myers et al., 2018; Velkovska and
Zouinar, 2018). If users are repeatedly unable to repair failures with voice
assistant, this weakens their trust and causes them to reduce their scope of
commands to simple tasks with low risk of failure (Luger and Sellen, 2016;
Lahoual and Frejus, 2019). Lahoual and Frejus (2019) found that in some
situations, voice assistant failures can erode trust to the extent that users
abandon voice assistants all together. However, not all failures require self-
repair. A study by Cuadra et al. (2021b) found that when voice assistants make
mistakes, voice assistant self-repair greatly improves people’s assessment of
an intelligent voice assistant, but it can have the opposite impact if no
correction is needed. Thus, understanding which types of failures undermine
trust the most may also inform us when failure mitigation strategies should be
activated.

Figure 1. To analyze the impact of voice assistant failures on user trust, we
used a mixed-methods approach, including interviews and a survey. As part of
the materials for our survey, we crowdsourced 199 failures from 107 voice
assistant users, and include this dataset as part of our contributions.

This is a visual aid to show that our paper contained three core elements: the
interviews, dataset creation, and survey, which are each represented with a
visual and text on the figure. On the top left, interviews with 12 voice
assistant users are represented with a geometric person, stating ”Stories
about past voice assistant failures and their impact on trust and use.” This
has an arrow pointing towards the survey. On the top right, the dataset
creation is represented with a cylinder and a computer, stating ”crowdsourced
199 failures from 107 voice assistant users, used in survey and open-sourced.”
This also has an arrow leading to the survey at the bottom. The survey is
represented by a sheet of paper with a checklist and states “268 participants
evaluated the impact of failures on their trust and future willingness to use
a voice assistant. Used mixed linear regression and ordinal regression models
in analysis.”

###  2.3. NLP Approaches to Voice Assistant Failures

The NLP community has also examined voice assistant failures from a slightly
different angle, focusing on the robustness of different NLP components
underlying voice assistants, such as models for tasks in natural language
inference (Naik et al., 2018), question answering (Gupta et al., 2021; Miller
et al., 2020), and speech recognition (Lee et al., 2018). NLP robustness can
be defined as understanding how model performance changes when testing on a
new dataset, which has a different distribution from the dataset the model is
trained on (Wang et al., 2021). In practice, users’ real world interactions
with voice assistants could differ from data used in development, which mimics
the data distribution shift in NLP robustness research.

Such data distribution shifts are shown to lead to model failures. In the case
of question answering, state-of-art models perform nearly at human-level for
reading comprehension on standard benchmarks collected from Wikipedia
(Rajpurkar et al., 2016). However, Miller et al. (2020) found that model
performance drops when the question answering model is evaluated on different
topic domains, such as New York Times articles, Reddit posts, and Amazon
product reviews. Noisy input can also harm model performances. Lee et al.
(2018) showed speech recognition errors have catastrophic impact on machine
comprehension. Gupta et al. (2021) created a question answering dataset
Disflu-QA where humans introduce contextual disfluencies, which also lead to
model performance drops.

Although these works do not directly focus on voice assistant failures, topic
domain changes, speech recognition errors and disfluencies are all very common
during user interactions with voice assistants. Such similarities motivate us
to draw parallels between the NLP robustness literature and HCI perspectives
of system failures. By understanding how different types of failures affect
trust in voice assistants overall, we can then try to pinpoint the underlying
NLP components that are the root cause of the most critical failures that
erode trust (Khaziev et al., 2022). Technical solutions can then be leveraged
to improve the robustness of the most critical parts of the system in order to
increase user trust and long-term engagement most efficiently.

##  3\. Method Overview

Now that we have established the importance of understanding of how voice
assistant failures impact user trust, we proceed to conduct a mixed-method
study. First, to prepare for the quantitative evaluation, we reviewed existing
datasets in HCI and NLP to find failures that we could use as materials for
our survey. Ultimately, the existing datasets were not sufficient for our
needs. Therefore, we crowdsourced a dataset of failures from voice assistant
users, which we also open source as part of the contributions of this study.
Concurrently, we conducted interviews with 12 voice assistant users to
understand which types of failures they have experienced, and how this
affected their trust in and subsequent use of the assistant. These interviews
were designed to provide a broad understanding of the thoughts, feelings, and
behaviors that users have with regard to voice assistant failures and inform
the quantitative survey design. Finally, we executed a survey to quantify how
different types of failures impact user perceptions of trust in their voice
assistants and their willingness to use them for various tasks in the future.
To report these findings, we first describe our process of collecting the
crowdsourced dataset of failures, and how we selected a subset to use in our
survey. Next, we present the interviews and survey, first describing our data
collection and analysis, and then presenting the results concurrently.

##  4\. Crowdsourcing a Dataset of Voice Assistant Failures

The first goal in our investigation was to determine which types of failures
users experience when using voice assistants. We first evaluated existing
datasets for fit and breadth of failures. We determined they were not
sufficient for our purposes, so we proceeded to crowdsource a dataset of
failures, adapting a taxonomy from Hong et al. (2021) to guide our collection.
Finally, we cleaned and open-sourced this dataset as a contribution of our
work.

Failure Type |  Sequential Coding Guide |  Failure Source |  Failure Scenario
---|---|---|---
Attention |  A lack of visual or audio evidence the voice assistant has started listening OR video or audio evidence that the voice assistant has started listening in the absence of a cue |  Missed Trigger |  Users say something to trigger the voice assistant, but it fails to respond.
Spurious Trigger |  Users do not say something to trigger the voice assistant, but it activates anyways.
Delayed Trigger |  Similar to system latency, the users say something to trigger the voice assistant, but it replies too late to be useful.
Perception |  Visual or audio evidence that speech is being incorrectly captured by the system. For example, being cut off by the voice assistant, witnessing it continue to listen once the users’ speech is complete, clearly mishearing a word, or evidence of background noise and cross-talk. |  Noisy Channel |  User input is incorrectly captured due to background noise.
Overcapture |  The voice assistant captures more input than intended by either beginning to capture input too early or ending too late, and acting on external data not relevant to the users’ request.
Truncation |  System does not fully capture users’ speech, by either beginning to capture input too late or ending too early.
Transcription |  System generates a transcription error, often in the form of similar sounding words.
Understanding |  Suspecting that audio was correctly captured but not mapped to the correct action. For example, receiving a response indicating inability to complete an action that has worked in the past, or receiving a response that is plausible but not correct for the intention of the input. |  Ambiguity |  There may be several interpretations of the users’ intent, and the system responds in a way that is plausibly accurate but not correct for the users’ intent.
Misunderstanding |  The system maps the users’ input to an incorrect action, perhaps with some correct inference on the users’ intent, but not fully accurate.
No Understanding |  The system fails to map the user’s input to any known action or response.
Response |  Finally, assuming that the input was correctly captured and understood, was the response generated incorrect, unclear, not given, or otherwise wrong? |  Action Execution: No Action |  If the system listens to the full request, but then turns off before giving any type of answer or taking action.
Action Execution: Incorrect Action |  The system gives information that is incorrect.
Table 1. Qualitative codebook and description of the various failures that
were collected. We checked each failure for failure type sequentially,
starting by checking if it could be an attention failure and progressing
through the types until we found one that fit. From failure type, we then
assessed which failure source applied.

###  4.1. A Review of Existing HCI and NLP Datasets

We first explored benchmark datasets in NLP, which contain a large number of
either questions and answers (Rajpurkar et al., 2016; Rajpurkar et al., 2018;
Reddy et al., 2019), or conversational dialogue (Gopalakrishnan et al., 2019;
Sun and Zhang, 2018; Zhang et al., 2018). We found that existing NLP datasets
do not cover the wide breadth of possible conversational failure cases due to
their emphasis on correct data for training. Additionally, their focus on
specific task performance, such as answering questions or dialogue generation,
is more narrow than the variety of use cases for voice assistants. As training
data relies on accurate task completion, these datasets did not contain
failures. While testing these models produces a small percentage of errors
(roughly 10%), the types of failures could only fall in the response and
understanding categories, as attention and perception failures are excluded
from the context of training these types of models. This limited their
usefulness for our purpose of understanding voice assistant failures that
occur in use and their impact on user trust.

In addition to these benchmark datasets, we investigated datasets that
incorporated spoken word speech patterns, such as the Spoken SQuAD dataset
(Lee et al., 2018) and Disflu-QA dataset (Gupta et al., 2021), as well as
human-agent interaction datasets, such as the ACE dataset (Aneja et al.,
2020), the Niki and Julie corpus (Artstein et al., 2018), and a video dataset
of voice assistant failures (Cuadra et al., 2021a). In these cases, we found
that the datasets were still restricted to only failures at the understanding
and response level (Lee et al., 2018; Gupta et al., 2021) or the context for
the failures was very specific and did not necessarily capture the breadth of
possible failures users experience (Aneja et al., 2020; Artstein et al.,
2018). Cuadra et al. (2021a)’s video dataset was the closest available fit for
our needs, but we still found the use case of in-lab question-answering too
narrow for our purposes. Therefore, we decided to crowdsource a dataset of
voice assistant failures from users, and use these failures when conducting
our quantitative survey on user trust.

###  4.2. Dataset Collection

####  4.2.1. Procedure

Crowd workers were asked to submit three failures they had experienced with a
voice assistant. They were asked about three specific types of failures out of
a taxonomy of 12, which were randomly chosen and displayed in equal measure
across all workers. The taxonomy of failures that we used to ask about
specific types of failures was adapted from previous work by Hong et al.
(2021), and identifies failures due to attention, perception, understanding,
and response, as shown in Table 1. Each question began by asking users if they
could recall a time when their voice assistant had failed, based on the
definitions in our taxonomy. For example, to capture missed trigger failures
we asked “Has there ever been a time when you intended to activate a voice
assistant, but it did not respond?” If so, we asked these workers to include
1. what they had said to the voice assistant, 2. how the voice assistant
responded, 3. the context for the failure, including what happened in the
environment, and 4. the frequency at which the failure occurred from 1 (rarely
when I use it) to 5 (every time I use it). These were all presented as text
entry boxes except for the frequency question, which was multiple choice.
Crowd workers were additionally asked to optionally share an additional
failure that they had not had the chance to share already. This was included
to capture failures that did not fit any of the three the categories they were
presented with, and we then categorized these failures according to our
taxonomy.

Once we received these failures, we anonymized the type of voice assistant in
the submitted examples, replacing activation words with “Voice Assistant” for
consistency. We then edited grammatical and spelling errors for clarity. We
also removed failures if they were not on-task, unclear, or exact repeats of
other submitted failures. Finally, we noticed that some of the categories the
users submitted the failures under were incorrect, so we re-categorized the
failures according to the codebook we developed as outlined in Table 1. Two
raters iteratively coded 101 submitted failures, with a final coding session
achieving an interrater agreement of 70%. One researcher then went back and
coded the entire dataset in its entirety. In total, our finalized dataset
contains 199 failures across 12 categories, submitted by 107 unique crowd
workers.

Failure Source |  Context |  What the User Said |  How the Voice Assistant Reacted
---|---|---|---
Missed Trigger |  I tell her to set a timer for ten minutes, I was alone and no one present at the moment. |  Voice Assistant, set a timer for 10 minutes. |  [No response.]
Spurious Trigger |  I was having a conference call with my team, and I was calling my coworker Sherry. The voice assistant mistakenly got turned on. |  [While talking to the coworker] ”Can you share your screen?” |  [Responding to the conversation with the co-worker.] ”One moment, let me help you with that”
Delayed Trigger |  It happened while I was driving a car. |  Voice Assistant, show me the route to the national park. |  [The voice assistant takes so much time to respond that before it can respond, you once again ask the route.]
Noisy Channel |  My children were playing in the background and the dog was barking, and I had to raise my voice and try several times to be heard by my phone even though it was inches from my face. |  Voice Assistant, what’s the weather? |  [It didn’t realize that my request had ended ] and kept spinning.] “I’m sorry, I didn’t quite understand you.”
Overcapture |  I was telling it to turn off the lights. I was the only one there. |  Voice Assistant, turn off the lights. |  [It continues listening for so long that you turn them off yourself.]
Truncation |  I asked the voice assistant to calculate a math question, but it cut me off. |  Voice Assistant, can you multiply 54, 39, 33, and 22? |  ”54 times 39 times 33 is 69,498.”
Transcription |  I asked for the weather conditions in the city I live in. No others were present except for me. |  Voice Assistant, what is the temperature in Murrieta, CA today? |  ”The temperature in Marietta, Georgia today is 65 degrees Fahrenheit.”
Ambiguity |  I was at home, alone, watching UFC and asked how old a fighter was. |  Voice Assistant, how old is Johnny Walker? |  ”Johnny Walker was founded in 1865.” [It referred to the whiskey company instead of the fighter.]
Misunderstanding |  I asked it to play the theme from Halloween. I was sitting with my mother. |  Voice Assistant, play the theme song to the movie Halloween. |  [It plays a scary sounds soundtrack instead of the song.]
No Understanding |  I was trying to run a routine to wake up my kids. |  Voice Assistant, wake up the twins. |  ”Sorry, I don’t know that.” [However, I’ve set up a routine for ”wake up the twins” that has worked in the past.]
Action Execution: No Action |  I asked when a movie was coming out in theaters, and it kept spinning its light over and over. |  Voice Assistant, when does Shang-Chi come out in theatres? |  [Pauses for a really long time, then turns its lights off and does not respond.]
Action Execution: Incorrect Action |  I was at home, in my living room, alone. I was trying to find out how long Taco Bell was open. |  Voice Assistant, when does the Taco Bell on Glenwood close. |  ”Taco Bell is open until 1am”. [Upon driving to Taco Bell, I realized it closed at 11:30pm.]
Table 2. Table of voice assistant failures users submitted, including the
context for the failure, what the user said, and what the voice assistant
said.

####  4.2.2. Crowd Worker Characteristics

We used Amazon Mechanical Turk to recruit the crowd workers. In total, 107
crowd workers contributed to our dataset. We required workers to have the
following qualifications: a HIT Approval Rate over 98%, over 1000 HITs
approved, AMT Masters, from the United States, over the age of 18, and voice
assistant users on at least a weekly basis. The plurality of users were in the
age range of 35-44 ($n=46$), followed by 25-34 ($n=32$), and 45-54 ($n=16$),
with the rest falling in 55-64 ($n=8$), 18-24 ($n=1$), and 1 preferring not to
answer. Fifty-eight crowd workers were men, 44 were women, 1 preferred not to
answer, and 1 identified as both a man and a woman. They used commercial voice
assistants such as Amazon Alexa ($n=59$), Google Assistant ($n=62$), and
Apple’s Siri ($n=40$), with many using some combination of the three ($n=47$).
91 crowd workers were native English speakers, and 13 were not. The plurality
identified as White ($n=58$), and 39 identified as Asian. Three crowd workers
did not provide any demographic information. The task took 15-20 minutes to
complete on average, and they received $5.00 USD compensation.

####  4.2.3. Final Dataset

In total, our finalized dataset contained 199 failures from 107 users across
12 different types of failures according to the taxonomy based on Hong et al.
(2021), as updated in Table 1. The failures we received most often were due to
misunderstanding ($n=38$), missed trigger ($n=25$), and noisy channel
($n=22$). Users least often submitted failures for truncation ($n=7$),
overcapture ($n=7$), and delayed triggers ($n=8$). Most crowd workers
submitted failures saying that they happened “rarely when I use it” ($n=87$)
or “sometimes when I use it” ($n=84$). Example failures across the 12
categories can be found in Table 2.

On average, the highest frequency of failures occurred for no understanding
($m=2.15$, sometimes when I use it, $sd=0.67$) and action execution: incorrect
($m=2.00$, sometimes when I use it, $sd=0.88$). The rest of the failure
sources had an average reported frequency between 1.0 (rarely when I use it)
and 2.0 (sometimes when I use it). The lowest frequency failures were due to
delayed triggers ($m=1.25$, $sd=0.46$) and ambiguity ($m=1.39$, $sd=0.78$).

We then used 60 of the failures from our dataset in our survey to quantify the
impact of different failures on user trust. This is outlined in more detail in
the following section. This dataset has been open
sourced111https://www.kaggle.com/datasets/googleai/voice-assistant-failures
for researchers to use to answer future research questions related to voice
assistant failures in the future.

##  5\. Interview and Survey Methods

Once we had gathered and categorized our dataset of voice assistant failures,
we were ready to answer our research question: how do voice assistant failures
impact user trust? To do so, we first conducted exploratory interviews with 12
people to gather their thoughts, feelings, and behaviors after experiencing
voice assistant failures. We used these findings and the failures collected in
the dataset to then design and execute a survey. This quantified how various
voice assistant failures impact users’ trust, as measured by their perceptions
of the voice assistant’s ability, benevolence, integrity, and their
willingness to use it for future tasks. Here, we describe the methods for both
the interviews and survey, and we follow this by jointly presenting the
results from both studies.

###  5.1. Interview Methods

####  5.1.1. Interview Procedure

Interviews began with questions about why the participants chose to start
using voice assistants and what types of questions they frequently would ask
of them. We asked for common times and places they would use their voice
assistants to understand their general experience with voice assistants.

Once these were established, we asked participants to tell us about a time
they were using their voice assistant and it made a mistake, in as much detail
as they could recall. We asked what they had been trying to do and why, if
others were present, and if anything else was happening in their environment.
We probed for users’ feelings once the failure occurred, and their perceptions
about the voice assistant’s ability to understand them and give them accurate
information. We asked participants what they did in the moment to respond to
the failure. Finally, we asked questions about their use of the voice
assistant in the aftermath, including how much they trusted it and if they
changed any of their behaviors to mitigate future failures. All interviews
were conducted remotely.

####  5.1.2. Interview Participants

During recruitment, we asked participants to submit their demographic
information, how frequently they used voice assistants and on what types of
devices. We additionally required participants to write a short (1-3 sentence)
summary of a time they encountered a failure while using their voice
assistant. We selected participants based on demographic distribution and the
level of detail they included regarding the failure.

All of our 12 participants lived in the United States. They used voice
assistants at least 1-3 times a week ($n=2$), with the majority reporting
using a voice assistant every day ($n=8$), and the rest ($n=2$) using it 4-6
times a week. The majority of participants used a voice assistant on their
mobile device ($n=11$), and five of these participants also used a voice
assistant smart home device. One participant only used a voice assistant smart
home device. Participants reported using common commercial voice assistants
such as Amazon Alexa ($n=2$), Google Assistant ($n=7$), and Apple’s Siri
($n=8$). Participants’ ages ranged from 18 to 50, with the plurality ($n=5$)
in the age range of 18-23. 3 of our participants were 41-50, 2 were 31-40, and
2 were 24-30. Six of our participants identified as women, five participants
identified as men, and one participant identified as non-binary. Three
participants identified as Asian, three identified as White, three identified
as Black or African American, two identified as Hispanic, Latino, or Spanish
origin, and one identified as both White and Black or African American. All of
our participants spoke English as a native language. Participants were
compensated with a $50 gift card and each interview lasted roughly 30 minutes.

####  5.1.3. Interview Analysis

Interviews were transcribed in their entirety by an automated transcription
service and analyzed via a deductive and inductive process (Creswell and Poth,
2016). We used deductive analysis to assess which types of failures these
participants experienced. To ground our deductive analysis, we used the same
codebook as we did for the dataset, as demonstrated in Table 1. We first
identified instances in which participants were discussing distinct failures,
and then applied our codebook to these instances. We used cues such as what
was happening in their environment, and when appropriate, users’ own
perceptions of why the failure occurred. We began by first identifying if
failures belonged in which of the four failure types: attention, perception,
understanding, or response. First, to determine if there was an attention
failure, we investigated if there was evidence that the voice assistant
accurately responded to an activation phrase, as indicated by visual or
auditory cues, or otherwise by the participant’s narrative. Second, we
evaluated if there was an error in perception, based on the participants’
assumption of if the voice assistant accurately parsed the input from the
participant, our own assessment from their narrative, or other audio/visual
cues. Next, assuming that the input was correctly parsed, we sought to
understand if the voice assistant accurately understood the semantic meaning
of the input (understanding failures), using the same process. Finally,
assuming all else had been correctly understood, we assigned response
failures, indicating that the voice assistant either did not take action or
took the incorrect action in response to an accurately understood command.
Once a failure type was determined, we then further specified the failure
sources as noted in Table 1. We resolved disagreements both asynchronously and
in meetings, through discussion and comparison, over the course of several
weeks.

While conducting this analysis, we also inductively identified themes related
to these failures’ impact on future tasks and recovery strategies. To conduct
this analysis, two researchers reviewed the twelve transcripts in their
entirety, and one additional researcher reviewed five of these transcripts to
further broaden and diversify themes. These researchers met over the course of
several weeks to compare notes and themes, ultimately creating four different
themes through inductive analysis. Of these themes, we report two due to their
novelty, specifically as related to future task orientation and recovery
strategies.

###  5.2. Survey Methods

To quantify our findings from interviews, we developed a survey to explore
users’ trust in voice assistants following each of the twelve different types
of failures from our taxonomy, as well as their willingness to use voice
assistants for a variety of tasks in the aftermath.

####  5.2.1. Procedure

The survey contained a screener, the core task, and a demographic section. We
required participants be over 18 years old, use their voice assistant in
English, and use a voice assistant with some regularity to participate. If
participants passed the screener, they were required to review and agree to a
digital consent form to continue.

The core task stated, “The following questions will ask you what you think
about the abilities of a voice assistant, given that the voice assistant has
made a mistake. Imagine these mistakes have been made by a voice assistant you
have used before. Please consider each scenario as independent of any that
come before or follow it. This survey will take approximately 20 minutes.”
Participants were then presented with 12 different failure scenarios, and they
were asked to rate their trust in two separate questions.

The first question measured trust in voice assistants as a confidence score
across three dimensions: ability, benevolence, and integrity. These were
selected because prior work on trust has determined these elements explain a
large portion of trustworthiness (Ma et al., 2017; Mayer et al., 1995). In the
context of voice assistants, ability refers to how capable the voice assistant
is of accurately responding to users’ input. Benevolence refers to how well-
meaning the product is. And finally, integrity represents that it will adhere
to ethical standards.

We asked participants to rate their confidence in voice assistants’ ability,
benevolence, and integrity, as a percentage on a scale of 0-100, with steps of
10, to replicate how prior work has conceptualized trust (Ma et al., 2017).
This was captured in response to the following statements:

  * •

(Ability) This voice assistant is generally capable of accurately responding
to commands.

  * •

(Benevolence) This voice assistant is designed to satisfy the commands its
users give.

  * •

(Integrity) This voice assistant will not cause harm to its users.

The second question evaluated users’ trust in the voice assistant to complete
tasks that required high, medium, and low trust. To select these tasks, we ran
a small survey on Mechanical Turk with 88 voice assistant users. We presented
12 different questions, which first gave an example voice assistant failure
(one for each failure source), and then asked “How much would you trust this
voice assistant to do the following tasks:” give a weather forecast, play
music, edit a shopping list, text a coworker, and send money. Users could
choose that they would trust it completely, trust it somewhat, or not trust it
at all.

There was not a significant difference in how much people trusted the voice
assistant to play music compared to forecast the weather ($Z=2.06,p=0.078$).
There was also not a significant difference in how much people trusted the
voice assistant to edit a shopping cart or text a coworker ($Z=1.39,p=0.21$)
as determined by pairwise comparisons, using $Z$-tests, corrected with Holm’s
sequential Bonferroni procedure on an ANOVA of an ordinal mixed model. We
found that there were significant differences between playing music, texting a
coworker, and transferring money, with users having the most trust in the
voice assistant playing music after a failure, less trust in texting a
coworker, and still less in transferring money. Therefore, we selected playing
music, texting a coworker, and transferring money to represent low, medium,
and high levels of trust required. Therefore, after asking about ability,
benevolence, and integrity, we asked participants how much they trusted their
voice assistants to execute the following tasks: play music, text a coworker,
and transfer money. These questions were displayed on a linear scale of 1 (“I
do not trust it at all”) to 5 (“I completely trust it”), with steps of 1.

We completed the survey with an open-ended, optional question for participants
to share anything else they would like to add. The survey concluded with
demographic questions regarding gender, race, ethnicity, whether they were
native English speakers, what type of voice assistants they used, and their
general trust tendency as control variables. General trust tendency was
measured based on responses to the following: “Generally speaking, would you
say that most people can be trusted, or that you need to be very careful in
dealing with people?” The options ranged from 1 (need to be very careful in
dealing with people) to 5 (most people can be trusted). The questionnaire used
for the survey has been submitted as supplementary materials.

####  5.2.2. Materials from our Dataset

To present each of the twelve failure sources in our survey, we drew from the
dataset we had created. We selected five failures from each of the twelve
categories. We required that these failures had been coded by two of the team
members who were in agreement (see dataset examples in Table 2). We used
random selection to determine which of the five possible failures was
presented to each user for each failure source. These are denoted in the
dataset “Survey” column.

####  5.2.3. Participants

We recruited participants from Amazon Mechanical Turk. We first ran a small
pilot ($n=27$) in which we determined that participants completed the survey
in roughly 20 minutes on average, and we set the compensation rate at $9 USD.
After removing participants who did not pass the attention check or straight-
lined, meaning they responded to every question with the same answer, we had a
total of 268 participants. These participants were required to have the
following qualifications: AMT Masters, with over 1000 HITs already approved,
over 18 years old, live in the United States, an approval rate greater than
97%, and they must not have participated in any of our prior studies.

| Ability | Benevolence | Integrity
---|---|---|---
| $F$ | df | residuals | $p$ | $F$ | df | residuals | $p$ | $F$ | df | residuals | $p$
(Intercept) | 9844.95 | 1 | 422.35 | <0.001 | 8436.32 | 1 | 355.86 | <0.001 | 10153.41 | 1 | 325.54 | <0.001
General Trust | 3.07 | 4 | 286.67 | 0.017 | 1.78 | 4 | 299.1 | 0.133 | 4.69 | 4 | 311.17 | 0.001
Failure Type | 17.17 | 3 | 2656.78 | <.001 | 8.87 | 3 | 2711.23 | <.001 | 20.56 | 3 | 2772.09 | <.001

Table 3. Voice assistant failure types significantly impacted users trust in
voice assistants, across ability, benevolence, and integrity when controlling
for their baseline trust tendencies, based on an ANOVA of three linear mixed
models. Failure type was encoded as a categorical variable, and general trust
was encoded as an ordinal value. Participant ID was a random, categorical
variable.

The plurality of our participants were in the age range of 35-44 ($n=106$),
followed by 25-34 ($n=68$), 45-54 ($n=52$), 55-64 ($n=33$), with 2-4
participants in each of the age brackets of 18-24, 65-74, and 75+. 134 of our
participants identified as men, 132 identified as women, and 2 identified as
non-binary genders. The majority of our participants were White ($n=210$), 21
participants were Black, and 15 were Asian. The rest of our participants
identified as mixed race or preferred not to answer.

##  6\. Results: Trust in Voice Assistants after Failures

In interviews, we found that participants reported failures across all four
failure types and ten of the twelve failure sources. The only two failure
sources that were not mentioned in interviews were missed triggers and delayed
triggers in the attention failure type. To understand which types of failures
most significantly impacted user trust, we analyzed how various failures
impacted users’ confidence in their voice assistant’s ability, benevolence,
and integrity. We used six mixed-linear regression models with log-normalized
confidence in either ability, benevolence, or integrity as the numeric
dependent variable. Note that there are two different levels at which we
conduct the analysis. The first is at the four broad “failure types” level
(attention, perception, understanding, and response). Then we drill down to
the detailed 12 “failure sources” nested within each failure type. Therefore,
for each dimension of trust, we encoded failure type or failure source, as
well as general trust tendency, as independent variables, so there were two
regression models per dimension of trust. Failure type and failure source were
encoded as categorical variables, and general trust tendency was encoded as an
ordinal value. In all models, PID was encoded as a random, categorical
variable.

An ANOVA on the regression models revealed that failure type (attention,
perception, understanding, response) does significantly impact perceptions of
ability ($F(3,2656.78)=17.17,p<.001$), benevolence
($F(3,2711.23)=8.87,p<0.001$), and integrity ($F(3,2772.09)=20.56,p<.001$)
when controlling for general trust tendency (see Fig. 2 and Table 3). We found
that the failure type “Response” (which includes action execution: inaction
and action execution: incorrect action) more significantly deteriorated user
trust in voice assistants across ability ($m=43.6,\beta=-0.155$, $p<.001$),
benevolence ($m=52.5,\beta=-0.072$, $p=0.013$), and integrity
($m=57.3,\beta=-0.124$, $p<.001$), compared with failures due to “Attention”
(which includes missed triggers, spurious triggers, and delayed triggers).
Attention failures had a mean trust in ability of $49.9$, benevolence of
$56.0$, and integrity of $67.7$ on the scale of $0$-$100$%. We also found that
failures due to perception significantly reduced users’ confidence in voice
assistant’s ability ($m=44.8,\beta=-0.122,p<.001$) and benevolence
($m=53.6,\beta=-0.047,p=.05$), but had no measurable effect on integrity
($m=61.4,\beta=-0.014,p=0.484$) compared with attention failures. Failures due
to understanding maintained higher user confidence in benevolence
($m=57.9,\beta=0.054,p=0.031$) and integrity ($m=65.0,\beta=0.063,p=0.003$),
but had no measurable effect on ability ($m=50.2,\beta=0.015,p=0.592$)
compared with attention failures.

Figure 2. Average scores across the three dimensions of trust (ability,
benevolence, and integrity) by failure type. Participants expressed higher
confidence across three trust dimensions after encountering attention and
understanding failures, compared to perception and response. Error bars
display the confidence interval.

This figure contains a bar graphs, the y-axis is average confidence on a scale
of 0 to 100. The x-axis is the trust dimensions of ability, benevolence, and
integrity. The graph displays the confidence in each trust dimension by
failure type: attention, perception, understanding, and response. We see that
people are consistently more confident in their voice assistant after failures
due to attention and understanding compared to perception and response.

Figure 3. Average scores across the three dimensions of trust (ability,
benevolence, and integrity) by failure type. Participants expressed higher
confidence across three trust dimensions after encountering failures due to
ambiguity and spurious triggers than they are of other failure types,
especially missed triggers and overcapture failures.

This figure contains a bar graphs, the y-axis is average confidence on a scale
of 0 to 100. The x-axis is the trust dimensions of ability, benevolence, and
integrity. The graph displays the confidence in each trust dimension by the 12
failure sources. We see that people tend to be far more confident in the voice
assistant after failures due to spurious triggers and ambiguity compared to
other failure types. This is less apparent for integrity (which has a flatter
shape, less variation).

Overall, response failures had the lowest average scores across ability,
perception, and integrity. The most drastic difference between these
categories is between failures due to understanding, which generally
maintained the highest levels of trust in ability, benevolence, and integrity,
as shown in Fig. 2. Therefore, in the analysis to follow that evaluates
changes in trust across failure source, response: incorrect action has been
chosen as the reference variable, and all betas reported are in reference to
this category. Below, we explore in more detail how users across both
interviews and the survey responded to failures across the various failure
sources.

###  6.1. Attention Failures

Attention failures are any failures in which a voice assistant does not
accurately respond to an attempt for activation. These were the least commonly
reported failures across interviews. In the survey, failures due to missed
triggers were particularly harmful to users’ confidence in voice assistants’
ability ($m=41.2,\beta=-0.10,p=0.05$) and benevolence
($m=47.1,\beta=-0.228,p<0.001$). However, the impact on integrity was positive
compared to the reference value (action execution: incorrect action)
($m=61.8,\beta=0.194,p<0.001$). None of the interview participants reported
failures due to missed triggers. As all of the failures had a favorable impact
on integrity compared to the reference value, we refrain from reporting it
throughout the rest of the results. See Fig. 3 and Table 4 for more details.

Only P7 and P12 reported experiencing attention failures in interviews, and
they were both spurious triggers. As shown in Table 1, these are failures in
which the voice assistant activates in the absence of an activation phrase. P7
reported that,

| Ability | Benevolence | Integrity
---|---|---|---
| $betas$ | se | $Z$ | $p$ | $betas$ | se | $Z$ | $p$ | $betas$ | se | $Z$ | $p$
(Intercept) | 3.56 | 0.046 | 77.413 | <0.001 | 3.796 | 0.048 | 79.083 | <0.001 | 3.754 | 0.045 | 83.422 | <0.001
General Trust | 0.23 | 0.096 | 2.396 | 0.017 | 0.111 | 0.111 | 1 | 0.317 | 0.339 | 0.106 | 3.198 | 0.001
Missed Trigger | -0.10 | 0.048 | -1.958 | 0.05 | -0.228 | 0.043 | -5.302 | <0.001 | 0.194 | 0.037 | 5.243 | <0.001
Spurious Trigger | 0.36 | 0.046 | 7.761 | <0.001 | 0.236 | 0.041 | 5.756 | <0.001 | 0.212 | 0.037 | 5.73 | <0.001
Delayed Trigger | 0.16 | 0.046 | 3.391 | 0.001 | 0.043 | 0.042 | 1.024 | 0.306 | 0.249 | 0.036 | 6.917 | <0.001
Truncation | 0.16 | 0.046 | 3.522 | <0.001 | 0.126 | 0.042 | 3 | 0.003 | 0.263 | 0.037 | 7.108 | <0.001
Overcapture | -0.13 | 0.047 | -2.809 | 0.005 | -0.193 | 0.042 | -4.595 | <0.001 | 0.133 | 0.037 | 3.595 | <0.001
Noisy Channel | 0.14 | 0.046 | 2.978 | 0.003 | 0.076 | 0.042 | 1.81 | 0.07 | 0.326 | 0.036 | 9.056 | <0.001
Transcription | -0.06 | 0.047 | -1.213 | 0.225 | -0.101 | 0.042 | -2.405 | 0.016 | 0.093 | 0.037 | 2.514 | 0.012
No Understanding | 0.06 | 0.047 | 1.17 | 0.242 | -0.043 | 0.042 | -1.024 | 0.306 | 0.28 | 0.037 | 7.568 | <0.001
Misunderstanding | -0.017 | 0.046 | -0.37 | 0.711 | -0.023 | 0.042 | -0.548 | 0.584 | 0.202 | 0.037 | 5.459 | <0.001
Ambiguity | 0.46 | 0.046 | 9.913 | <0.001 | 0.302 | 0.042 | 7.19 | <0.001 | 0.361 | 0.036 | 10.028 | <0.001
No Action | -0.003 | 0.047 | -0.064 | 0.949 | -0.09 | 0.042 | -2.143 | 0.032 | 0.186 | 0.037 | 5.027 | <0.001
Table 4. The results of three mixed-linear regression models, demonstrating
how voice assistant failures impact users’ trust in voice assistants’ across
ability, benevolence, and integrity. Reference failure source: Incorrect
Action.

> “I feel like in conversation if I have it plugged in and there’s like
> multiple people in the room, and they’re talking or whatever, I think
> sometimes it may hear an [activation phrase] where it’s not. And if that’s
> happened, where it’s activated like once or twice completely out of nowhere,
> and that hasn’t upset me or anything, but it’s, it was just like, I didn’t
> say [an activation phrase]. Why are you activating? What’s happening? Why
> are you doing this?”

P7 additionally said they were working and“It must have heard an [activation
phrase] somewhere in there. And then it started speaking while I was trying to
do my [job], and I had to like stop and be like, hey, stop.” They said, “It
would really piss [me off].” Similarly, P12 reported that these types of
failures were “irritating but funny at the same time.” They said they were
funny “because sometimes like, when you’re usually calling [the voice
assistant] she’ll take a longer time to respond, but when you’re not talking
to it, it automatically pops up…Like, I’m not talking to you, but you could
answer me when I’m talking to you.” As demonstrated in Fig. 3 and Table 4,
failures due to spurious triggers had a more favorable relative impact on
users’ impressions of trust in the voice assistant’s ability
($m=57.9,\beta=0.36,p<0.001$) and benevolence ($m=64.2,\beta=0.236,p<0.001$).
Overall, it appears that these are one of the least detrimental failures to
users’ trust.

Similarly, failures due to delayed triggers were favorable to users
perceptions of ability ($m=49.3,\beta=0.16,p=0.001$) relative to the reference
variable (response: incorrect action). Delayed trigger failures are defined as
failures in which the voice assistant experiences latency when activating, to
the point of potentially, but not necessarily, providing a correct response
too late to be useful. They had no measurable effect on benevolence
($m=55.6,\beta=0.043,p=0.306$). None of the participants reported a failure
due to a delayed trigger in interviews.

###  6.2. Perception Failures

Users reported failures across all four failure types reported in Table 1,
including truncation, overcapture, noisy channel, and transcription.
Perception failures indicate that the voice assistant did not accurately
capture the users’ input. Transcription was by far the most common failure
source, contrasted with only one failure recorded per truncation, overcapture,
and noisy channel.

Truncation failures indicate that the voice assistant stopped listening to
input too early, and only acted on some of the user’s intended input. P12
reported that “I use [a voice assistant] to send messages and stuff, and
sometimes it would write the text for some of the words, but not all of the
words. So it takes me longer than expected to send a message, because it will
take a little bit of the words and not fully listen.” They said, “it’s
aggravating, very annoying.” Truncation failures had a favorable relative
impact on perceptions of ability ($m=48.7,\beta=0.16,p<0.001$) and benevolence
($m=58.1,\beta=0.126,p=0.003$). As shown in Fig. 3, these maintained higher
relative trust compared to other failures in perception.

Overcapture failures indicate that the voice assistant has listened beyond the
point that a user has given their input. As P8 said, sometimes, “it doesn’t
know when to search for what I said and just keeps listening without taking
action, even though it shows it is listening.” They tried to make sense of
this failure, saying “I find that on different devices, the reaction time for
it [is different].” They said that, “This is wasting my time. Which is only
logically two to three minutes,” but they said, “if you keep messing with it,
it makes it worse.” Failures due to overcapture were particularly harmful to
users’ confidence in voice assistants’ ability ($m=38.5,\beta=-0.10,p=.05$)
and benevolence ($m=47.0,\beta=-0.228,p<0.001$), with the overall lowest means
compared to all other failure types.

There was one instance in which a user thought that the failure they
experienced was because of noise in the background, indicative of noisy
channel failures. P9 said, “Sometimes…I’ll try to use a feature where it tries
to identify like a song…and it just won’t be able to pick it up, and it’ll
just give me a message, like ‘Sorry, I could not understand that.’” They said,
“I get that it was loud…I would think that it would, it should be able to
understand. So I feel like that is a little annoying.” However, they said the
failure did not impact how they thought about the voice assistant’s accuracy
or ability, saying that “it’s pretty accurate for the most part, for other
things.” Noisy channel failures were considered to more favorably impact user
perceptions of ability ($m=49.2,\beta=0.14,p=0.003$), with no measurable
impact on benevolence ($m=57.9,\beta=0.076,p=0.07$). As shown in Fig. 3, they
achieved similar levels of trust as failures due to truncation.

Nine of our participants mentioned failures relating to transcription of their
input, indicating that they did not believe the voice assistant accurately
captured what they had said. These failures varied from not understanding the
name of a musical group (P7), incorrectly transcribing a text message (P2),
incorrectly transcribing a sequence of numbers (P4), not understanding angry,
slurred, or mumbled speech (P3, P5, P9), and not understanding accents (P8) or
other languages (P6, P9). P7 said it caused a “tiny little bit of frustration”
when it did not understand the musician they were requesting. However, they
“don’t really demerit [the voice assistant] for that in particular because
it’s so good at everything else that it does.” However, when it came to using
the voice assistants in other languages such as Spanish or French, “there has
not been a successful time where it’s been it’s been able to play that
different song in a different language” (P9). This led the participant to
think, “that it – it just has no ability to understand me in a different
language” (P9). Failures due to transcription did not have a measurable impact
on perceptions of ability in the survey ($m=42.3,\beta=-0.06,p=0.225$)
relative to the reference variable, however they impacted trust more so than
other failure sources within perception as shown in Fig. 3. Transcription
failures did negatively impact perceptions of benevolence
($m=51.0,\beta=-0.101,p=0.016$).

###  6.3. Understanding Failures

We found that participants submitted failures across all categories of
understanding failures, as described below.

Failures due to no understanding resulted in a complete inability to map the
input to an action or response. P6 said, “I was trying to plan a vacation…It
was my friend’s bachelorette party…And I was like, [Voice Assistant], where’s
Lake Havasu? How far is it?…And she’s like, ‘Sorry. I didn’t understand what
you’re saying.’” This led P6 to question, “Why do I even use you?” However,
they said that, “for timers, it works really well.” No understanding failures
did not significantly impact trust relative to the reference variable, in
terms of ability ($m=45.3,\beta=0.06,p=0.24$) or benevolence
($m=52.5,\beta=-0.043,p=0.306$).

Misunderstanding failures occurred when the voice assistant mapped the user’s
input to an action that was partially, but not fully, accurate to their
intent. For example, P4 explained that when they ask their voice assistant “to
‘Take me home.’ It usually directs me to my home, but on occasion, it shows me
search results for the phrase ‘Take me home.’” Similarly, P1 explained how
when using a voice assistant for online shopping, sometimes it would “pull up
the wrong item or, like, the wrong location.” They said they felt
“disappointed and frustrated.” Misunderstanding failures did not measurably
impact perceptions of ability ($m=42.4,\beta=-0.017,p=0.711$) or benevolence
($m=52.9,\beta=-0.023,p=0.584$) relative to the reference variable.

Failures due to ambiguity were situations in which one could see several
reasonable interpretations of one’s intent from the captured input, but the
system failed to navigate the ambiguity. For example, P10 said, “I was trying
to get to Pizza Hut and…it kept on telling me one in the nearby city instead
of the one that’s I believe like 10 minutes away from me. So I asked a couple
of times, and then it didn’t work, and that’s when I just pulled out my phone
and then just looked it up myself and left.” They said that they were “a bit
baffled, since normally, like when I ask [a voice assistant] for something, I
get the response I would expect.” As demonstrated in Fig. 3 and Table 4,
failures due to ambiguity were more favorable to users’ impressions of the
voice assistant’s ability ($m=62.6,\beta=0.46,p<0.001$) and benevolence
($m=67.9,\beta=0.302,p<0.001$). Overall, these failures maintained the highest
level of user trust.

###  6.4. Response Failures

There were two possible types of response failures. These included incorrect
action, in which the system gives information that is incorrect, or no action,
in which a voice assistant fails to respond at all.

Incorrect action failures were times when the command seemed to be accurately
understood, but the information provided in response was incorrect. For
example, P1 said that sometimes they would use “the voice assistant to give me
the best route to get to the location.” While it would usually accurately
respond to this command, sometimes, “it will give me a really like roundabout
way, like really time-consuming way.” As shown in Fig. 3, failures due to
incorrect action resulted in a relatively average perception of ability
($m=44.0$) and benevolence ($m=54.3$), and the lowest perception of integrity
($m=53.6$).

Multiple users experienced failures due to no action, in which the voice
assistant completely fails to respond to the input. P2 said, “I did have a
couple times that was also frustrating…I would say ‘Reply’ [to a text
message]. And I would talk and nothing would get sent. And like, my hands are
literally covered in stuff because I’m rolling these cookies out, and I had to
stop what I’m doing, go back to my phone, and actually like manually text.”
Another participant experienced failures due to no action, saying that “This
morning where I woke up. I said, [Voice Assistant], what’s the weather
outside? And it loaded for the first few seconds…and then after a couple of
seconds, it said, ‘There was an error. Please try again in a few minutes.’ I
wait one or two seconds, then I’ll ask it again, and it gives me the
information” (P10). This participant said that because the information has
been “accurate,” they “would still trust it to a very high degree.” Failures
due to no action had no measurable relative impact on ability
($m=43.2,\beta=-0.003,p=0.949$) and had a slight but significant negative
impact on benevolence ($m=50.7,\beta=-0.09,p=0.032$) compared to incorrect
action.

##  7\. Responses to Failures and Future Use of Voice Assistants

Users described a variety of strategies for mitigating failures, given that
they did occur. In some cases, users described completely stopping their use
of a voice assistant for a particular task. For example, after encountering a
truncation failure while using the voice assistant to send a text message, P12
said that they either “have to redo it, or I just, like, don’t do it at all.”
Eventually, P12 said that they stopped encountering that failure because they
“barely use it” for that same task anymore. So while some users felt like they
“don’t sweat it too much” (P5) when a voice assistant failed at a task, others
felt like they would use it “not as much” (P2) for those same tasks.

We found that the pattern of continuing to use a voice assistant in general
but excluding the tasks that resulted in a failure, at least for a short
period of time, was consistent across many different types of failures,
including transcription, misunderstanding, and ambiguity. For example, P2 said
that they needed to be careful using a voice assistant, because sometimes they
would say a name and “it would come up [with] a different name.” They said
that following an incident like that,

| Playing a Song | Texting a Coworker | Transferring Money
---|---|---|---
| $\beta$ | $se$ | $Z$ | $p$ | $\beta$ | $se$ | $Z$ | $p$ | $\beta$ | $se$ | $Z$ | $p$
Ability | 0.048 | 0.003 | 16.000 | <0.001 | 0.064 | 0.003 | 21.333 | <0.001 | 0.076 | 0.005 | 15.2 | <0.001
Benevolence | 0.043 | 0.003 | 14.333 | <0.001 | 0.028 | 0.003 | 9.333 | <0.001 | 0.017 | 0.005 | 3.4 | 0.001
Integrity | 0.019 | 0.002 | 9.500 | <0.001 | 0.024 | 0.003 | 8 | <0.001 | 0.03 | 0.004 | 7.5 | <0.001
General Trust | -0.146 | 0.108 | -1.352 | 0.176 | -0.094 | 0.134 | -0.701 | 0.483 | -0.192 | 0.218 | -0.881 | 0.378
Table 5. The results of three mixed-ordinal regressions modeling user trust in
the voice assistant to execute the task based on their perceptions of the
voice assistant’s ability, benevolence, and integrity. We did not include cut
point calculations and state 1 calculations in the table for ease of
interpretability.

> “I would still use [the voice assistant]. I think what would happen though
> is like you kind of build up that trust…So the next couple times I would go
> into my contacts and hit the button myself, you know, and then like if I was
> walking to my car and get my keys in one hand, and it’s been a while. So,
> you know, let me try this again. Like I think that’s something where you
> kind of have to like, build the trust back up and give it another try. At
> least that’s what I do.”

P12 echoed this, saying, “Let’s say you’re opening Spotify or something like
that. I think it will probably go on command, rather than sending a
message…different tasks, you know, it has a different trust level.” P5 had a
similar sentiment, saying “I think the problem with the most voice assistant
is, if I tried to give it a complex search query, it doesn’t really understand
me, or it gets frustrating and I just I’m going to go ahead and type in
whatever it is I’m looking for.” Even when failures were mitigated in the
moment, users remained wary of using their voice assistants for the same
tasks.

Interestingly, sometimes users would continue to use their voice assistant for
the same general task following a failure, but they would make slight changes
to their use. For example, P1 encountered a misunderstanding failure while
trying to shop for a sweater online, and they started to “rely on it a little
less, and do more searching on my own.” They said that “for future reference,
I would just remember to not use it to do certain tasks and do certain tasks
on my own, [especially] when I look for an item that’s difficult to find.”
However, in the meantime, “I would just ask for other tasks.” For P1, this
included “looking for other items other than this sweater. I would tell her to
search for like grocery items and do some comparison shopping online.”
Shopping for different items was distinct enough to maintain this user’s
trust. P7 experienced a similar situation, in which they encountered a
transcription error, which they mitigated by spelling the name of “hyerpop duo
100 gecs” as “G-E-C-S.” They said this correct helped so that “[the assistant
did] understand what I was saying.” Even though they had experienced a failure
for that particular artist, they “continue to do that [use it to play songs]
to this day. It’s a very good music player,” but they are “a little weary when
it comes to certain musicians that I feel that…[the voice assistant] would
have trouble understanding.”

Users often made sense of the failures based on the perceived task complexity.
P12 thought that the task that they had the highest trust in was “to open like
apps,” followed by “calling somewhere.” They explained that, “I want to put
that as number one, but sometimes, like the way the contact name is, is not
registered. Like, you know the way for you to say it, it’s not how like the
voice [assistant] says it.” P2 similarly evaluated the voice assistant,
saying, “the best thing is picking up website information.” However, they
similarly said “to get more personalized messages, contacts, and that sort of
thing, you have to be really careful what you say and how you say it.”

Because of these findings, we hypothesized that users’ trust in voice
assistants after failures would affect their willingness to use it for
different tasks to differing degrees. As shown in Table 5, we used three
mixed-ordinal regressions to model trust in these three tasks, with scores for
confidence in the voice assistant’s ability, benevolence, and integrity as the
independent variables. Trust in the voice assistant to play a song, text a
coworker, and tranfer money was encoded as an ordinal value. Confidence in the
voice assistant’s ability, benevolence, and integrity were encoded as
numerical values. General trust tendency was encoded as a numerical value and
PID was encoded as a random categorical value. We found that user perceptions
of voice assistant ability, benevolence, and integrity positively correlated
with their willingness to use the voice assistant for future tasks. Overall,
people were moderately trusting of their voice assistant to play a song
($m=3.29,sd=1.30$), less trusting of their voice assistant to text a coworker
($m=2.34,sd=1.18$), and least trusting of their voice assistant to transfer
money ($m=1.56,sd=0.91$).

In particular, perceptions of ability had a stronger effect on people’s
willingness to use the voice assistant to play a song ($\beta=0.048,p<.001$)
compared with benevolence, which also significantly impacted willingness to
use the voice assistant to play songs, but to a slightly lesser degree
($\beta=0.043,p<.001$). Integrity was even less influential, though still
significantly positively correlated without how much people trusted their
voice assistant to play a song ($\beta=0.019,p<.001$). This pattern was
repeated for texting a coworker and transferring money as well, with ability
being most strongly positively correlated with people’s willingness to trust
the voice assistant to execute these tasks, followed by benevolence, and then
integrity.

##  8\. Discussion

With interviews, a survey, and a crowdsourced voice assistant failures
dataset, we conducted a mixed-method study of voice assistant failures and how
they impact user trust in and future intended use of voice assistants. As the
underlying technology for voice assistants continues to improve in accuracy
and ability, and its applications become increasingly high stakes to human
health and well-being (Sezgin et al., 2020; Mehandru et al., 2022; Yang et
al., 2021; De Melo et al., 2020), we discuss our findings with the goal of
improving user trust and long-term engagement in voice assistants.

Our users consistently relied on their voice assistants to find information
and execute tasks across varying levels of complexity. Similar to prior work
(Luger and Sellen, 2016), those who wanted to use a voice assistant
consistently for tasks which might result in failures have developed complex
mental models of which tasks they can trust their voice assistants with.
Unlike prior work (Luger and Sellen, 2016), people often did not necessarily
entirely abandon the use of their voice assistant after it failed at complex
tasks, even after repeated failures. Many users considered the accuracy of
their voice assistants so consistently high that they could forgive failures
and continue engaging with those tasks after a short period of time. While
trust in the complex tasks was being repaired, many participants continued
using their voice assistants for tasks they considered more simple, such as
information retrieval and playing music.

We find that failures that lead users to feel like they have wasted time, such
as those due to missed triggers and overcapture, tend to lead to more
deteriorated perceptions of ability and benevolence. This is contrasted with
scenarios in which users have more understanding of why the voice assistant
failed, such as those due to ambiguity and transcription, which users
generally felt like they could work around or anticipate. However, if the
failure due to transcription was believed to be due to using the device in
another language, this caused abandonment of the voice assistant in that
language. Similarly, users did not feel like they lost out on the advantages
of using voice assistants when spurious trigger failures occurred, so they
were less damaging to perceptions of ability. The single most damaging failure
source to voice assistant integrity was action execution: incorrect action, as
participants were more skeptical of the claim that the voice assistant would
not cause harm following these failures.

Prior work has pointed to ways that trust can be repaired when failures do
occur. Cuadra et al. (2021b) showed that when a voice assistant proactively
attempts to acknowledge a failure and repair trust, this increased people’s
perception of its intelligence. Additionally, Mahmood et al. (2022) has found
that failure mitigation strategies such as apologies were effective in
restoring perceptions of likability and intelligence of a voice assistant
after a failure. Xiao et al. (2021) demonstrated that situating the voice
assistant as a learner, and helping users understand when to give feedback to
the voice assistant, improved users’ perceptions of the voice assistant.
Fischer et al. (2019) encourages voice assistant responses to support
progressivity of the conversation, especially when the response does not help
the user. Our work shows that users naturally repair trust with their voice
assistants by relying on it for different tasks following a failure, or the
same task but on a different topic, such as online shopping for different
items or playing music by other artists than those that caused a failure.

Quantitatively, we established that certain types of failures are more
critical than others. This insight can be used to help prioritize the failure
recovery strategies across HCI and NLP that are most effective for regaining
trust. For example, self-repair for voice assistants such as Cuadra et al.
(2021b) employed may be most useful in situations where the voice assistant
has failed because of a missed trigger or overcapturing users’ input. In
addition, we can also try to identify the specific components in the voice
assistant technology stack that cause critical failures, and leverage
techniques in NLP robustness to improve how these models perform during user
interactions. For example, noisy channel and transcription failures can be
modeled as small perturbations to the input, which is well researched
(Ebrahimi et al., 2018; Belinkov and Bisk, 2017). Reliable transcription may
also be important to address by speech recognition modules, especially for low
resource languages (Magueresse et al., 2020).

Our open-sourced dataset has also provided concrete and comprehensive example
failures (199 real-world sourced examples with context, query, and response)
for future researcher to reuse to develop failure mitigation strategies, along
with a refined taxonomy for classifying voice assistant failures, as supported
by prior work. While prior work (Hong et al., 2021) was useful in helping NLP
practitioners anticipate and plan for failures across many types of NLP
technologies, our dataset specifically addresses failures that occur with
voice assistants. We anticipate this will allow future researchers to use
human-centered example failures when conducting research related to voice
assistant failures, trust, and mitigation strategies.

## Limitations

There are a few methodological limitations of our study, which we detail here.
First, our dataset collection and interviews relied on retrospectives and
recalling failures, rather than observing them in situ. This subjects our data
to recall bias, and our results should be interpreted in this light. For
instance, none of the interview participants recalled failures due to missed
triggers or delayed triggers in interviews, although missed triggers were
considered relatively damaging to perceptions of ability and benevolence in
the survey. Additionally, our survey relied on collecting users’ feedback
regarding hypothetical scenarios. Future work may build on our findings by
using our dataset to systematically introduce failures and capture the
resulting impact on user trust via ESM or diary study. Our sample of
participants were also frequent voice assistant users, which indicates that
they likely forgave errors more easily than other populations (Luger and
Sellen, 2016). Additionally, we did not address the use of conversational
agents through interfaces other than voice, such as embodied conversational
agents or text-based conversational agents. As embodied and text interfaces
have more potential affordances with which users can judge and interact with
the system (Bickmore and Cassell, 2001, 2005), the impact of failures may not
perfectly generalize to these use cases.

##  9\. Conclusion

In conclusion, through a mixed-method study, we found that voice assistant
users experience a multitude of failures, ranging from a voice assistant
incorrectly triggering to responding in a way that does not address users’
needs. These different types of failures do differentially impact users’
trust, which in turn affects intention to use their voice assistants for tasks
in the future. In particular, we find that failures due to spurious triggers
and ambiguity are less detrimental to user trust than failures due to
incorrect action execution, missed triggers, or overcapture. We additionally
find that people rebuild their trust in voice assistants through simple tasks,
such as playing a song, before resuming using their full voice assistant
functionality after a failure has occurred. We also contribute a dataset of
199 failures, to help future researchers and practitioners build on our work.
By further working to understand, prevent, and repair voice assistant
failures, we hope to build voice assistant users’ trust in these devices and
allow them to benefit from the increasing and varied functionality they
provide.

## References

  * (1)
  * Aneja et al. (2020) Deepali Aneja, Daniel McDuff, and Mary Czerwinski. 2020\.  Conversational Error Analysis in Human-Agent Interaction. In _Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents_. Association for Computing Machinery, New York, NY, USA, Article 3, 8 pages.  https://doi.org/10.1145/3383652.3423901
  * Artstein et al. (2018) Ron Artstein, Jill Boberg, Alesia Gainer, Jonathan Gratch, Emmanuel Johnson, Anton Leuski, Gale Lucas, and David Traum. 2018\.  The Niki and Julie Corpus: Collaborative Multimodal Dialogues between Humans, Robots, and Virtual Agents. In _Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)_. European Language Resources Association (ELRA), Miyazaki, Japan.  https://aclanthology.org/L18-1463
  * Bahmanziari et al. (2003) Tammy Bahmanziari, J Michael Pearson, and Leon Crosby. 2003\.  Is trust important in technology adoption? A policy capturing approach.  _Journal of Computer Information Systems_ 43, 4 (2003), 46–54.
  * Belinkov and Bisk (2017) Yonatan Belinkov and Yonatan Bisk. 2017.  Synthetic and natural noise both break neural machine translation.  _arXiv preprint arXiv:1711.02173_ (2017).
  * Bickmore and Cassell (2001) Timothy Bickmore and Justine Cassell. 2001.  Relational Agents: A Model and Implementation of Building User Trust. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ (Seattle, Washington, USA) _(CHI ’01)_. Association for Computing Machinery, New York, NY, USA, 396–403.  https://doi.org/10.1145/365024.365304
  * Bickmore and Cassell (2005) Timothy Bickmore and Justine Cassell. 2005.  _Social Dialongue with Embodied Conversational Agents_.  Springer Netherlands, Dordrecht, 23–54.  https://doi.org/10.1007/1-4020-3933-6_2
  * Braun et al. (2019) Michael Braun, Anja Mainz, Ronee Chadowitz, Bastian Pfleging, and Florian Alt. 2019\.  At Your Service: Designing Voice Assistant Personalities to Improve Automotive User Interfaces. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ (Glasgow, Scotland Uk) _(CHI ’19)_. Association for Computing Machinery, New York, NY, USA, 1–11.  https://doi.org/10.1145/3290605.3300270
  * Brown et al. (2020) Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020\.  Language models are few-shot learners.  _Advances in neural information processing systems_ 33 (2020), 1877–1901.
  * Candello et al. (2019) Heloisa Candello, Claudio Pinhanez, Mauro Pichiliani, Paulo Cavalin, Flavio Figueiredo, Marisa Vasconcelos, and Haylla Do Carmo. 2019\.  The Effect of Audiences on the User Experience with Conversational Interfaces in Physical Spaces. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ (Glasgow, Scotland Uk) _(CHI ’19)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3290605.3300320
  * Clark (1996) Herbert H. Clark. 1996\.  _Using Language_.  Cambridge University Press, Cambridge, UK.  https://doi.org/10.1017/CBO9780511620539
  * Clark et al. (2019) Leigh Clark, Nadia Pantidi, Orla Cooney, Philip Doyle, Diego Garaialde, Justin Edwards, Brendan Spillane, Emer Gilmartin, Christine Murad, Cosmin Munteanu, Vincent Wade, and Benjamin R. Cowan. 2019\.  What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ (Glasgow, Scotland Uk) _(CHI ’19)_. Association for Computing Machinery, New York, NY, USA, 1–12.  https://doi.org/10.1145/3290605.3300705
  * Condliffe (2017) Jamie Condliffe. 2017\.  AI Voice Assistant Apps are Proliferating, but People Don’t Use Them. MIT Technology Review.  https://www.technologyreview.com/2017/01/23/154449/ai-voice-assistant-apps-are-proliferating-but-people-dont-use-them/.
  * Cox (2020) Toby A. Cox. 2020\.  Siri and Alexa Fails: Frustrations With Voice Search.  https://themanifest.com/digital-marketing/resources/siri-alexa-fails-frustrations-with-voice-search
  * Creswell and Poth (2016) John W Creswell and Cheryl N Poth. 2016.  _Qualitative inquiry and research design: Choosing among five approaches_.  Sage publications, Thousand Oaks.
  * Cuadra et al. (2021a) Andrea Cuadra, Hansol Lee, Jason Cho, and Wendy Ju. 2021a.  Look at Me When I Talk to You: A Video Dataset to Enable Voice Assistants to Recognize Errors.  _arXiv preprint arXiv:2104.07153_ (2021).
  * Cuadra et al. (2021b) Andrea Cuadra, Shuran Li, Hansol Lee, Jason Cho, and Wendy Ju. 2021b.  My Bad! Repairing Intelligent Voice Assistant Errors Improves Interaction.  _Proceedings of the ACM on Human-Computer Interaction_ 5, CSCW1 (2021), 1–24.
  * De Melo et al. (2020) Celso M De Melo, Kangsoo Kim, Nahal Norouzi, Gerd Bruder, and Gregory Welch. 2020.  Reducing Cognitive Load and Improving Warfighter Problem Solving With Intelligent Virtual Assistants.  _Frontiers in psychology_ 11 (2020), 554706.
  * Devlin et al. (2018) Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018.  Bert: Pre-training of deep bidirectional transformers for language understanding.  _arXiv preprint arXiv:1810.04805_ (2018).
  * Dietvorst et al. (2015) Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015\.  Algorithm aversion: people erroneously avoid algorithms after seeing them err.  _Journal of Experimental Psychology: General_ 144, 1 (2015), 114\.
  * Dzindolet et al. (2002) Mary T Dzindolet, Linda G Pierce, Hall P Beck, and Lloyd A Dawe. 2002.  The perceived utility of human and automated aids in a visual detection task.  _Human factors_ 44, 1 (2002), 79–94.
  * Ebrahimi et al. (2018) Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018\.  HotFlip: White-Box Adversarial Examples for Text Classification. In _Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)_. Association for Computational Linguistics, Melbourne, Australia, 31–36.  https://doi.org/10.18653/v1/P18-2006
  * Ekinci and Dawes (2009) Yuksel Ekinci and Philip L Dawes. 2009.  Consumer perceptions of frontline service employee personality traits, interaction quality, and consumer satisfaction.  _The service industries journal_ 29, 4 (2009), 503–521.
  * Fischer et al. (2019) Joel E. Fischer, Stuart Reeves, Martin Porcheron, and Rein Ove Sikveland. 2019.  Progressivity for Voice Interface Design. In _Proceedings of the 1st International Conference on Conversational User Interfaces_ (Dublin, Ireland) _(CUI ’19)_. Association for Computing Machinery, New York, NY, USA, Article 26, 8 pages.  https://doi.org/10.1145/3342775.3342788
  * FitzGerald et al. (2022) Jack FitzGerald, Shankar Ananthakrishnan, Konstantine Arkoudas, Davide Bernardi, Abhishek Bhagia, Claudio Delli Bovi, Jin Cao, Rakesh Chada, Amit Chauhan, Luoxin Chen, Anurag Dwarakanath, Satyam Dwivedi, Turan Gojayev, Karthik Gopalakrishnan, Thomas Gueudre, Dilek Hakkani-Tur, Wael Hamza, Jonathan J. Hüser, Kevin Martin Jose, Haidar Khan, Beiye Liu, Jianhua Lu, Alessandro Manzotti, Pradeep Natarajan, Karolina Owczarzak, Gokmen Oz, Enrico Palumbo, Charith Peris, Chandana Satya Prakash, Stephen Rawls, Andy Rosenbaum, Anjali Shenoy, Saleh Soltan, Mukund Harakere Sridhar, Lizhen Tan, Fabian Triefenbach, Pan Wei, Haiyang Yu, Shuai Zheng, Gokhan Tur, and Prem Natarajan. 2022\.  Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems. In _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_ (Washington DC, USA) _(KDD ’22)_. Association for Computing Machinery, New York, NY, USA, 2893–2902.  https://doi.org/10.1145/3534678.3539173
  * Gopalakrishnan et al. (2019) Karthik Gopalakrishnan, Behnam Hedayatnia, Qinlang Chen, Anna Gottardi, Sanjeev Kwatra, Anu Venkatesh, Raefer Gabriel, and Dilek Hakkani-Tür. 2019.  Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations. In _Proc. Interspeech 2019_. 1891–1895.  https://doi.org/10.21437/Interspeech.2019-3079
  * Gupta et al. (2021) Aditya Gupta, Jiacheng Xu, Shyam Upadhyay, Diyi Yang, and Manaal Faruqui. 2021.  Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering. In _Findings of ACL_.
  * Hayes et al. (2018) Paige Hayes, Jason Wagner, Mark McCaffrey, and Matt Hobbs. 2018.  Consumer intelligence series: Prepare for the Voice Revolution.  https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/voice-assistants.html
  * Hong et al. (2021) Matthew K. Hong, Adam Fourney, Derek DeBellis, and Saleema Amershi. 2021.  Planning for Natural Language Failures with the AI Playbook. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ (Yokohama, Japan) _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, Article 386, 11 pages.  https://doi.org/10.1145/3411764.3445735
  * Hunter (2022) Tatum Hunter. 2022\.  Siri and Alexa are getting on their owners’ last nerves. The Washington Post.  https://www.washingtonpost.com/technology/2022/03/07/voice-assistants-wrong-answers/.  (Accessed on 06/08/2022).
  * Khaziev et al. (2022) Rinat Khaziev, Usman Shahid, Tobias Röding, Rakesh Chada, Emir Kapanci, and Pradeep Natarajan. 2022\.  FPI: Failure Point Isolation in Large-scale Conversational Assistants. In _Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track_. Association for Computational Linguistics, Hybrid: Seattle, Washington + Online, 141–148.  https://doi.org/10.18653/v1/2022.naacl-industry.17
  * Lahoual and Frejus (2019) Dounia Lahoual and Myriam Frejus. 2019.  When Users Assist the Voice Assistants: From Supervision to Failure Resolution. In _Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems_ (Glasgow, Scotland Uk) _(CHI EA ’19)_. Association for Computing Machinery, New York, NY, USA, 1–8.  https://doi.org/10.1145/3290607.3299053
  * Lee et al. (2018) Chia-Hsuan Lee, Szu-Lin Wu, Chi-Liang Liu, and Hung-yi Lee. 2018\.  Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension.  _Proc. Interspeech 2018_ (2018), 3459–3463.
  * Lee et al. (2021) One-Ki Daniel Lee, Ramakrishna Ayyagari, Farzaneh Nasirian, and Mohsen Ahmadian. 2021.  Role of interaction quality and trust in use of AI-based voice-assistant systems.  _Journal of Systems and Information Technology_ 23, 2 (2021), 154–170.
  * Liu et al. (2019) Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019.  Roberta: A robustly optimized bert pretraining approach.  _arXiv preprint arXiv:1907.11692_ (2019).
  * Luger and Sellen (2016) Ewa Luger and Abigail Sellen. 2016.  ”Like Having a Really Bad PA”: The Gulf between User Expectation and Experience of Conversational Agents. In _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_ (San Jose, California, USA) _(CHI ’16)_. Association for Computing Machinery, New York, NY, USA, 5286–5297.  https://doi.org/10.1145/2858036.2858288
  * Ma et al. (2017) Xiao Ma, Jeffrey T. Hancock, Kenneth Lim Mingjie, and Mor Naaman. 2017.  Self-Disclosure and Perceived Trustworthiness of Airbnb Host Profiles. In _Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing_ (Portland, Oregon, USA) _(CSCW ’17)_. Association for Computing Machinery, New York, NY, USA, 2397–2409.  https://doi.org/10.1145/2998181.2998269
  * Madsen and Gregor (2000) Maria Madsen and Shirley Gregor. 2000.  Measuring human-computer trust. In _11th australasian conference on information systems_ , Vol. 53. 6–8.
  * Magueresse et al. (2020) Alexandre Magueresse, Vincent Carles, and Evan Heetderks. 2020\.  Low-resource languages: A review of past work and future challenges.  _arXiv preprint arXiv:2006.07264_ (2020).
  * Mahmood et al. (2022) Amama Mahmood, Jeanie W Fung, Isabel Won, and Chien-Ming Huang. 2022.  Owning Mistakes Sincerely: Strategies for Mitigating AI Errors. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 578, 11 pages.  https://doi.org/10.1145/3491102.3517565
  * Mayer et al. (1995) Roger C Mayer, James H Davis, and F David Schoorman. 1995\.  An integrative model of organizational trust.  _Academy of management review_ 20, 3 (1995), 709–734.
  * Mehandru et al. (2022) Nikita Mehandru, Samantha Robertson, and Niloufar Salehi. 2022\.  Reliable and Safe Use of Machine Translation in Medical Settings. In _2022 ACM Conference on Fairness, Accountability, and Transparency_ (Seoul, Republic of Korea) _(FAccT ’22)_. Association for Computing Machinery, New York, NY, USA, 2016–2025.  https://doi.org/10.1145/3531146.3533244
  * Miller et al. (2020) John Miller, Karl Krauth, Benjamin Recht, and Ludwig Schmidt. 2020\.  The Effect of Natural Distribution Shift on Question Answering Models. In _Proceedings of the 37th International Conference on Machine Learning_ _(ICML’20)_. JMLR.org, Article 641, 12 pages.
  * Myers et al. (2018) Chelsea Myers, Anushay Furqan, Jessica Nebolsky, Karina Caro, and Jichen Zhu. 2018.  Patterns for How Users Overcome Obstacles in Voice User Interfaces. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ (Montreal QC, Canada) _(CHI ’18)_. Association for Computing Machinery, New York, NY, USA, 1–7.  https://doi.org/10.1145/3173574.3173580
  * Naik et al. (2018) Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018.  Stress test evaluation for natural language inference.  _arXiv preprint arXiv:1806.00692_ (2018).
  * Nasirian et al. (2017) Farzaneh Nasirian, Mohsen Ahmadian, and One-Ki Daniel Lee. 2017\.  AI-Based Voice Assistant Systems: Evaluating from the Interaction and Trust Perspectives. In _Americas Conference on Information Systems_.
  * Paek and Horvitz (2013) Tim Paek and Eric J Horvitz. 2013.  Conversation as action under uncertainty.  _arXiv preprint arXiv:1301.3883_ (2013).
  * Pinsky (2021) Yury Pinsky. 2021\.  Loud and clear: AI is improving Assistant conversations.  https://blog.google/products/assistant/loud-and-clear-ai-improving-assistant-conversations/.  (Accessed on 12/12/2022).
  * Rajpurkar et al. (2018) Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018.  Know what you don’t know: Unanswerable questions for SQuAD.  _arXiv preprint arXiv:1806.03822_ (2018).
  * Rajpurkar et al. (2016) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016.  Squad: 100,000+ questions for machine comprehension of text.  _arXiv preprint arXiv:1606.05250_ (2016).
  * Reddy et al. (2019) Siva Reddy, Danqi Chen, and Christopher D Manning. 2019\.  Coqa: A conversational question answering challenge.  _Transactions of the Association for Computational Linguistics_ 7 (2019), 249–266.
  * Robertson and Díaz (2022) Samantha Robertson and Mark Díaz. 2022.  Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat. In _2022 ACM Conference on Fairness, Accountability, and Transparency_ (Seoul, Republic of Korea) _(FAccT ’22)_. Association for Computing Machinery, New York, NY, USA, 2223–2238.  https://doi.org/10.1145/3531146.3534638
  * Saha et al. (2022) Tulika Saha, Saichethan Reddy, Anindya Das, Sriparna Saha, and Pushpak Bhattacharyya. 2022.  A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony. In _Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_. Association for Computational Linguistics, Seattle, United States, 2436–2449.  https://doi.org/10.18653/v1/2022.naacl-main.174
  * Salem et al. (2015) Maha Salem, Gabriella Lakatos, Farshid Amirabdollahian, and Kerstin Dautenhahn. 2015.  Would You Trust a (Faulty) Robot? Effects of Error, Task Type and Personality on Human-Robot Cooperation and Trust. In _Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction_ (Portland, Oregon, USA) _(HRI ’15)_. Association for Computing Machinery, New York, NY, USA, 141–148.  https://doi.org/10.1145/2696454.2696497
  * Seymour and Van Kleek (2021) William Seymour and Max Van Kleek. 2021.  Exploring Interactions Between Trust, Anthropomorphism, and Relationship Development in Voice Assistants.  _Proceedings of the ACM on Human-Computer Interaction_ 5, CSCW2 (2021), 1–16.
  * Sezgin et al. (2020) Emre Sezgin, Yungui Huang, Ujjwal Ramtekkar, and Simon Lin. 2020.  Readiness for voice assistants to support healthcare delivery during a health crisis and pandemic.  _NPJ Digital Medicine_ 3, 1 (2020), 1–4.
  * Smith et al. (2022) Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, et al. 2022\.  Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model.  _arXiv preprint arXiv:2201.11990_ (2022).
  * Sun and Zhang (2018) Yueming Sun and Yi Zhang. 2018.  Conversational Recommender System. In _The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval_ (Ann Arbor, MI, USA) _(SIGIR ’18)_. Association for Computing Machinery, New York, NY, USA, 235–244.  https://doi.org/10.1145/3209978.3210002
  * Thoppilan et al. (2022) Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022\.  LaMDA: Language Models for Dialog Applications.  _arXiv preprint arXiv:2201.08239_ (2022).  https://arxiv.org/abs/2201.08239
  * Tolmeijer et al. (2021) Suzanne Tolmeijer, Naim Zierau, Andreas Janson, Jalil Sebastian Wahdatehagh, Jan Marco Marco Leimeister, and Abraham Bernstein. 2021.  Female by Default? – Exploring the Effect of Voice Assistant Gender and Pitch on Trait and Trust Attribution. In _Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems_ (Yokohama, Japan) _(CHI EA ’21)_. Association for Computing Machinery, New York, NY, USA, Article 455, 7 pages.  https://doi.org/10.1145/3411763.3451623
  * Vaswani et al. (2017) Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017.  Attention is All you Need. In _Advances in Neural Information Processing Systems_ , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc.  https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
  * Velkovska and Zouinar (2018) Julia Velkovska and Moustafa Zouinar. 2018.  The illusion of natural conversation: interacting with smart assistants in home settings. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_.
  * Wang et al. (2021) Xuezhi Wang, Haohan Wang, and Diyi Yang. 2021.  Measure and Improve Robustness in NLP Models: A Survey.  _arXiv preprint arXiv:2112.08313_ (2021).
  * Wei et al. (2021) Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021.  Finetuned language models are zero-shot learners.  _arXiv preprint arXiv:2109.01652_ (2021).
  * Winter and Carusi (2022) Peter Winter and Annamaria Carusi. 2022.  ‘If You’re Going to Trust the Machine, Then That Trust Has Got to Be Based on Something’:: Validation and the Co-Constitution of Trust in Developing Artificial Intelligence (AI) for the Early Diagnosis of Pulmonary Hypertension (PH).  _Science & Technology Studies_ 35, 4 (2022), 58–77.
  * Xiao et al. (2021) Ziang Xiao, Sarah Mennicken, Bernd Huber, Adam Shonkoff, and Jennifer Thom. 2021.  Let Me Ask You This: How Can a Voice Assistant Elicit Explicit User Feedback?  _Proc. ACM Hum.-Comput. Interact._ 5, CSCW2, Article 388 (oct 2021), 24 pages.  https://doi.org/10.1145/3479532
  * Yang et al. (2021) Samuel Yang, Jennifer Lee, Emre Sezgin, Jeffrey Bridge, Simon Lin, et al. 2021\.  Clinical advice by voice assistants on postpartum depression: cross-sectional investigation using Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana.  _JMIR mHealth and uHealth_ 9, 1 (2021), e24045.
  * Yin et al. (2019) Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach. 2019\.  Understanding the Effect of Accuracy on Trust in Machine Learning Models. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ (Glasgow, Scotland Uk) _(CHI ’19)_. Association for Computing Machinery, New York, NY, USA, 1–12.  https://doi.org/10.1145/3290605.3300509
  * Zhang et al. (2018) Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018\.  Personalizing dialogue agents: I have a dog, do you have pets too?  _arXiv preprint arXiv:1801.07243_ (2018).