# Do explanations increase the effectiveness of AI-crowd generated fake news
warnings?

Ziv Epstein,1* Nicolò Foppiani,2* Sophie Hilgard,3* Sanjana Sharma,4* Elena
Glassman, 3 David Rand5

###### Abstract

Social media platforms are increasingly deploying complex interventions to
help users detect false news. Labeling false news using techniques that
combine crowd-sourcing with artificial intelligence (AI) offers a promising
way to inform users about potentially low-quality information without
censoring content, but also can be hard for users to understand. In this
study, we examine how users respond in their sharing intentions to information
they are provided about a hypothetical human-AI hybrid system. We ask i) if
these warnings increase discernment in social media sharing intentions and ii)
if explaining how the labeling system works can boost the effectiveness of the
warnings. To do so, we conduct a study ($N=1473$ Americans) in which
participants indicated their likelihood of sharing content. Participants were
randomly assigned to a control, a treatment where false content was labeled,
or a treatment where the warning labels came with an explanation of how they
were generated. We find clear evidence that both treatments increase sharing
discernment, and directional evidence that explanations increase the warnings’
effectiveness. Interestingly, we do not find that the explanations increase
self-reported trust in the warning labels, although we do find some evidence
that participants found the warnings with the explanations to be more
informative. Together, these results have important implications for designing
and deploying transparent misinformation warning labels, and AI-mediated
systems more broadly.

## Introduction

In recent years, there has been increased societal attention and governmental
scrutiny on the spread of misinformation on social media platforms (Vosoughi,
Roy, and Aral 2018), including concerns around political manipulation (Roose
2018) and public health (Bagherpour 2020) (e.g., the COVID-19 pandemic). In an
effort to curb the spread of misinformation while avoiding direct censorship
or content moderation, platforms such as Facebook and Twitter have adopted
policies that attach warning labels to certain posts, indicating that they may
contain false or disputed information (Roth and Pickles 2020; Constine 2016;
Mosseri 2017). Previous work has shown that labeling can reduce the rate at
which users believe (Pennycook, Cannon, and Rand 2018; Pennycook et al. 2020a;
Clayton et al. 2019) and share (Yaqub et al. 2020; Pennycook, Cannon, and Rand
2018; Pennycook et al. 2020a) misinformation.

However, these studies also highlight the enormous design space of such
warning labels, whereby effectiveness could depend on factors such as the
content and source of the label (Vraga and Bode 2017), the political
affiliation of the user (Pennycook and Rand 2019), the presence or absence of
warning labels on other items (Pennycook et al. 2020a), label humorousness
(Garrett and Poulsen 2019), and the signal word (Sherman, Redmiles, and Stokes
2020).

Results from explainable machine learning (Lai and Tan 2019; Bansal et al.
2020) suggest that adding explanations to a computer recommendation may
increase the rate at which people accept that recommendation. While
explanations have been shown to increase the trust and efficacy of human-AI
systems in other domains (Lai and Tan 2019; Ribeiro, Singh, and Guestrin 2016)
(particularly when the recommendations arise from a complex, algorithmic
process (Rader, Cotter, and Cho 2018)), it is unclear how they will work in
the context of social media, where attention is scarce, users are distracted
by design (Pennycook et al. 2021), and the key outcome is the effect on actual
sharing and engagement behavior. In this work, we therefore ask whether
explaining a system that generates misinformation labels may similarly
increase user sharing discernment (the difference in sharing rates of true and
false news items). To decouple the perceptions of warning labels with the
particular accuracy of any given labeling system, we focus on a hypothetical
system that is 100% accurate, i.e., only and all known misinformation receives
a label. In particular, we study the role of explanations on warning labels
generated by a (simulated) hybrid human-AI misinformation labeling system.

We focus on a hybrid AI-crowd labeling system because it is highly practical
relative to several alternatives. Many currently deployed misinformation
labeling solutions rely on matching claims in social media posts to those
disputed by professional fact checkers (Lyons 2018). However, these expert-
based solutions cannot keep up with the scale and speed of misinformation,
since misinformation can be produced faster than it can be disproven. Indeed,
even among posts regarding known debunked claims, labeling can be inconsistent
(Zannettou 2021). Due to these inherent delays, much violating content might
never be labeled with a warning, and even those that do get labeled will only
be after the period of maximal exposure. This inability of expert-based
solutions to keep up with the volume of misinformation may also inadvertently
encourage belief in misinformation through the “implied truth effect,” whereby
users may perceive the lack of a warning label as validation that a piece of
content has been fact checked (Pennycook et al. 2020a). In addition, methods
employing professional fact-checkers suffer from a general lack of bi-partisan
trust: one study from the Pew Research Center found that 70% of Republicans
and 48% of Americans say fact-checking efforts tend to “favor one side”
(Walker and Gottfried 2019). Another proposed alternative is fully algorithmic
methods for detecting misinformation (Bozarth and Budak 2020; Abdali et al.
2021; Weinzierl, Hopfer, and Harabagiu 2021) (see Related Work for a review).
However, these methods fundamentally struggle to keep up with the the non-
stationarity of misinformation, and require ground-truth labeling. Hybrid AI-
crowd systems represent a promising alternative that avoids the scalability
problems of professional fact-checking by using non-expert human raters to
crowdsource the evaluation of information accuracy (Kim et al. 2018; Pennycook
and Rand 2019; Allen et al. 2020). These crowd ratings can then be used as
inputs for machine learning algorithms, thereby combining the accuracy of
human labels with the scale of automated systems. Platforms have already
started to adopt such techniques, e.g. Facebook’s Community Review (Silverman
2019) or Twitter’s Birdwatch (Coleman 2021; Allen, Martel, and Rand 2021).

While such a system holds much promise, its complexity may undermine its
effectiveness. Yaqub et al. (2020) suggest that warnings attributed to machine
learning-based methods may be less effective than warnings attributed to human
authorities in leading users to increase the quality of the news they share.
They find that the warning effectiveness correlates with users’ self-reported
“importance” of the sources, a measure they associate with trust in a given
source, among other factors. However, the increased efficacy of fact checker
and news media labels may also be at least partially attributable to a greater
public familiarity with—and therefore greater perceived accuracy of—these
label sources. Notably, Seo, Xiong, and Lee (2019) fail to replicate
effectiveness of a fact-checker based warning with less than perfect accuracy.

With this in mind, we use the context of a hybrid crowdsourcing-machine
learning system as we seek to understand the effectiveness of explaining
warning labels in reducing intentions to share misinformation. Our inclusion
of a baseline condition in which warning labels appear without explanation
additionally serves to help us understand users’ acceptance of such a system
more generally. As Seo, Xiong, and Lee (2019) find explanations of a label
attributed to machine learning increase user discernment without increasing
self-reported user trust, we additionally investigate whether our effect is
correlated with self-reported measures of trust. Concretely, we seek to
further understand:

  1. 1.

What is the baseline effectiveness of warning labels attributed to a hybrid
crowdsourcing-machine learning system in reducing user intentions to share
false news? Based on prior work (Seo, Xiong, and Lee 2019; Yaqub et al. 2020),
we hypothesize the hybrid warning label will also be effective.

  2. 2.

Is it possible to increase the effectiveness of labels attributed to a hybrid
crowdsourcing-machine learning system by providing a detailed explanation of
the system? Based on prior work (Seo, Xiong, and Lee 2019; Horne et al. 2019),
we hypothesize explanations may increase sharing discernment.

  3. 3.

Is increasing trust in the labeling system a necessary intermediary to
increasing effectiveness of warning labels with respect to intention to share?
We hypothesize that trust is not necessary to improve discernment (Seo, Xiong,
and Lee 2019).

  4. 4.

Do other moderators that have been investigated in prior work on
misinformation interventions due to their connection to the psychology of fake
news (Epstein et al. 2021; Pennycook and Rand 2021b, a) impact the
effectiveness of providing a detailed explanation of the warning system?

We find clear evidence that warnings attributed to human-AI labeling systems
increase sharing discernment relative to a no-warning control group. We also
find some evidence that explaining the process of how this system works
increases discernment above and beyond the warning itself. Furthermore, we
find no difference in the measures of trust between the warning labels with
and without explanation, suggesting that enhanced user trust is not the
mechanism by which explanations may increase discernment. However, we find
that certain moderators, such as the score in a cognitive reflection task and
the age of the subjects, increase the efficacy of the explanations.

## Related Work

### Explanations and Transparency

The rise of interpretable or explainable machine learning is motivated, among
other factors, by the idea that transparency in machine learning systems can
build trust in their outputs (Lipton 2018; Doshi-Velez and Kim 2017). Research
has shown that even nonsensical explanations (Lai and Tan 2019) or
explanations of incorrect predictions (Bansal et al. 2020) can increase human
acceptance of machine learning algorithms. Work on human explanations has
shown that the effectiveness of uninformative explanations may be due to
inattentiveness, as raising the stakes of a decision eliminates the
effectiveness of uninformative explanations, while retaining an effect of
informative explanations (Langer, Blank, and Chanowitz 1978).

Kizilcec (2016) studied the effect of varying levels of explanation detail on
trust in an algorithmic grading system and found that a medium level of
transparency—in which an explanation of the grading _procedure_ was
provided—tended to increase trust beyond the low transparency condition, while
providing specific details of the calculation (high transparency) decreased
trust. Kizilcec further finds that the decrease in trust is correlated with a
violation of expectations in the output of the algorithm. Kulesza et al.
(2013) used a music recommendation environment to study the effects of varying
the soundness and completeness of explanations on users’ trust in the system.
They find that high soundness and completeness together result in high trust
in the system, but that users prefer concrete feature-based explanations to
explanations of the algorithmic procedure.

Rader, Cotter, and Cho (2018) explore the effects of different types of
explanations (_What, How, Why_ and _Objective_) on users’ perceptions of the
Facebook news feed algorithm. They find that while these explanations
generally increase knowledge and awareness of the algorithmic system, they may
decrease user assessments of the system’s correctness, fairness, and
responsiveness to user controls. Liao, Gruen, and Miller (2020) developed an
Explainable AI question bank to explore the distinct affordances of different
types of explanations. They found that how explanations where helpful when
users were in a “quality-control role.”

Explanation depth is also studied in the context of debunking low quality
information. Chan et al. (2017) present a meta-analysis of debunking studies,
in which detailed debunking messages are found to be more effective than less
detailed messages. The authors hypothesize, in line with previous works, that
a debunking message must be sufficiently detailed to provide users with an
alternative account of the false phenomenon. However, Lewandowsky et al.
(2012) recommend using the simplest messages that satisfy this property. In
line with this, Martel, Mosleh, and Rand (2021) show that correction style
(including simple versus deep explanations) has minimal impact on correction
efficacy.

Figure 1: Example of a false post across the three study conditions: _the
Control condition_ (far left), _the Label condition_ (center), and _the
Explanation Label condition_ (far right).

In the context of AI-generated flags for misinformation on social media, Horne
et al. (2019) compared warning labels with explanations to those without in an
experimental design similar to ours. They however focus on article reliability
as a key outcome, while we focus on sharing. They find that warning labels
generated with AI improve perceptions of reliability, and find heterogeneity
among participants’ baseline perceptions of reliability.

### Approaches to Detecting Misinformation

In an attempt to detect misinformation at a scale necessary for social media,
several approaches have been proposed. One class involves computational
methods to detect misinformation (Bozarth and Budak 2020). These computer-
based approaches have shown remarkable success, leveraging signals such as
sharing patterns (Rosenfeld, Szanto, and Parkes 2020), text features (Granik
and Mesyura 2017), account activity (Breuer, Eilat, and Weinsberg 2020), user
stance (Weinzierl, Hopfer, and Harabagiu 2021) and visual features for website
screenshots (Abdali et al. 2021). However, the nuanced nature of truth, the
limited availability of labeled training data (Rubin, Chen, and Conroy 2015;
Bozarth, Saraf, and Budak 2020), and the non-stationarity problem whereby the
signatures of misinformation can change rapidly (e.g. with the rise of
COVID-19 misinformation) place fundamental limits of the effectiveness of
fully automated systems.

A promising alternative that avoids these concerns involves crowdsourcing the
fact-checking process. Indeed, crowdsourced fact checking could demote
platforms as centralized authorities of truth, and keep up with the fast pace
of new forms of misinformation. Kim et al. (2018) develop an algorithm for
deciding which crowd-flagged items to send to professional fact-checkers for
verification, with the goal of minimizing overall exposure to false news.
Using datasets from Weibo and Twitter, they demonstrate that this method can
be effective in reducing the spread of misinformation.

Pennycook and Rand (2019) show that laypeople can successfully distinguish
high quality media outlets from those which produce hyper-partisan and
blatantly false news. They also show layperson trust judgments of websites are
highly correlated with those of professional fact-checkers and propose a
modification to the newsfeed algorithm, whereby content from websites which
receive low crowdsourced trust judgments is downranked. Epstein, Pennycook,
and Rand (2020) build on this work by showing that when informed that their
judgments will be used to fight misinformation on social media, participants
still correctly distinguish between high and low quality outlets. This
suggests that users would not game such a crowdsourcing mechanism to advance
their partisan agendas. Allen et al. (2020) extend this work to the evaluation
of headlines, instead of websites. They show that the ratings of small
politically-balanced crowds are equally correlated with fact-checker ratings
as the fact-checkers’ ratings are correlated with one another.

### Labeling Misnformation and Credibility Indicators

The research area of misinformation labeling is rapidly evolving, as academic
and industry researchers are eager to understand the full implications of
design decisions being made on social media platforms (Morrow et al. 2020).
Clayton et al. (2019) investigate the effects of the locality and severity of
misinformation warnings. They evaluate conditions including a general
misinformation warning and advice on how to spot misinformation, with or
without specific misinformation labels appealing to fact checkers. Further,
fact checker labels vary between describing headlines as “rated false” and
“disputed.” They find that specific labels are more effective than general
warnings at reducing the perceived accuracy of false news, and that general
warnings have a spillover effect, also reducing the perceived accuracy of true
news. In the Appendix, the authors additionally state results for intention to
share, finding that users in the labeled conditions are no less likely to
share items labeled as “rated false” compared to unlabeled items.

Pennycook, Cannon, and Rand (2018) find that prior exposure to false news,
even when labeled as “Disputed by 3rd Party Fact-Checkers,” increases
perceived accuracy of those news items when later viewed without labels. While
the primary outcome of interest was perceived accuracy, the authors identify
in a secondary analysis that disputed labels decrease users’ intentions to
share false news items. Pennycook et al. (2020a) study both perceived accuracy
and sharing intentions in the presence of labels and find that the presence of
“disputed” or “false” labels decreases both accuracy and sharing for labeled
false items, but reverses for unlabeled false items, via the “implied truth
effect.” The authors find that this effect can be mitigated by explicitly
labeling some verified items as “true.”

While early research in this field often focused on accuracy perceptions as
outcome (Clayton et al. 2019; Pennycook, Cannon, and Rand 2018; Bode and Vraga
2015; Horne et al. 2019), recent work has shown that accuracy perceptions are
not a reliable proxy for intention to share (Pennycook et al. 2021), and thus
addressing the goal of reducing the spread of misinformation requires
measuring the latter.

Yaqub et al. (2020) explore the effects of appealing to different authorities
in social media misinformation labels and find that users are most likely to
change their sharing behavior in response to warning labels citing “fact
checkers” or “news media” as the source of the dispute. A condition citing
“AI” was among the least effective at reducing misinformation sharing in the
study. However, this may be due to the fact that currently deployed
misinformation labels generally appeal to fact checkers, and so users
currently have a better understanding of the accuracy they can expect from
fact checkers. It is difficult to know how users with no past experience would
have assigned importance to equally accurate label sources. Seo, Xiong, and
Lee (2019) similarly find some evidence that users are more likely to
correctly detect false news when it is identified by “fact checkers” rather
than “machine learning.” However, this result did not replicate in a follow up
study in which they removed news sources. This follow up study also tested the
effect of adding more (fabricated) detail to the “machine learning” warning
label and found that users were better able to detect false news in this
condition but displayed less self-reported trust in the system, which may be
due to the explanations being incongruous with the headlines.

## Methods

Table 1: Bayesian mixed effects model predicting sharing intent. P (bayes) is the number of posterior samples greater than (less than) zero. Rhat measures the potential scale reduction factor (PSRF, (Brooks and Gelman 1998)), which captures the variation within a given chain relative to the variation across chains (at convergence, Rhat = 1). | Estimate | Standard Error | p (bayes) | Rhat
---|---|---|---|---
veracity | 0.267 | 0.106 | 0.008∗∗ | 1.002
Label | -0.155 | 0.094 | 0.052. | 1.007
Explanation Label | -0.161 | 0.048 | 0.031∗ | 1.003
veracity $\times$ Label | 0.135 | 0.064 | 0.019∗ | 1.003
veracity $\times$ Explanation Label | 0.217 | 0 | $<0.001^{***}$ | 1.002
Intercept | 3.201 | 0.099 | 0∗∗∗ | 1.003
N=29,460 |  |  |  |
Figure 2: Posterior distributions of the main effect of veracity (e.g.
discernment in control, red), and the interactions between veracity and _the
Label condition_ and _the Explanation Label condition_ , plotted using kernel
density estimations. The blue and green distributions correspond to the
increase in discernment above and beyond the control for the _the Label
condition_ and _the Explanation Label condition_ , respectively. The coloured
points show the average values of the distributions, while the error bars
illustrate 95% credible intervals.

We recruited $N=2512$ Americans using Lucid, which uses quota matching to
provide a sample that is nationally representative on age, gender, ethnicity
and geographic region (Coppock and McClellan 2019). $N=1855$ of these
individuals passed two initial trivial quality checks at the start of the
survey and were allowed to proceed. Additionally, we only considered subjects
who self-reported using at least one social network among Facebook, Twitter,
and Instagram. Of these participants, $N=1473$ completed the discernment task,
and $N=1394$ completed the entire survey. Unless otherwise specified, we will
consider the $N=1473$ subjects who completed the discernment task as our final
sample. This final sample had a mean age of 47.87, was 54.1% female, and was
87% White. Participants were shown 20 social media “cards” during the main
discernment task, half with a false headline, half with a true headline. Each
participant saw the 20 cards in a random order, and rated their sharing
intentions (”How likely would you be to share this story on social media?”) on
a Likert scale from 1 (Extremely Unlikely) to 6 (Extremely Likely). These
headlines were selected from a larger set of headlines (Pennycook et al.
2020b) to be relevant to news when the study ran, (e.g. “stale” headlines were
pulled). The stimuli can be found online at
https://osf.io/tgwna/?view_only=efa0ae9ebb30408ca54204e0dc3f53b6.

In a between subjects design, participants were assigned to one of three
conditions: i) a control condition with no labeling (_the Control condition_);
ii) a basic labeling condition in which users are shown a statement about how
the labeling system works prior to any sharing decisions and then presented
with the 20 headlines in which false headlines contain a simple label
indicating that the claim has been labeled as false by the human-AI system
(_the Label condition_); and iii) an explanation condition which contains all
elements of the basic flagging conditions and augments each label with a
general (static) explanation of the process by which that label was generated
(_the Explanation Label condition_). See Figure 1 for examples of the false
items across conditions.

After the discernment task, all subjects were asked two factual manipulation
checks about what they had seen in the images used during the main task (Kane
and Barabas 2019). These questions ensure the treatment was deployed
successfully. The subjects who received warning labels then answered a battery
of questions related to their opinions of and preferences for the labeling
process. Subjects answered a series of multiple choice questions related to
how helpful, annoying, and informative these labels are, as well as how often
the user would like to see them on social media. Finally, they answered a
battery of six trust questions derived from Mayer, Davis, and Schoorman
(1995)’s three factors of trustworthiness: ability, benevolence and integrity
(ABI). For these six ABI questions, we find they are highly correlated
($\alpha$= 0.821), so following standard practice we use Principal Component
analysis to construct a single measure of trust that explains 65.3% of the
overall variance.

Users were then asked additional measures, such as two measures of attention
(Berinsky et al. 2021), a 6-question cognitive reflection test (CRT) that
measures the tendency to stop and think versus going with your gut (Frederick
2005; Thomson and Oppenheimer 2016), political affiliation, and basic
demographics. These covariates where chosen because prior work has looked at
how they moderate discernment (Epstein et al. 2021; Pennycook and Rand 2021a,
b), so here we investigate if they impact the effectiveness of the
explanation. Reaction times were not recorded.

For our main analysis, we fit a Bayesian mixed effects linear model predicting
sharing rate as a function of condition and headline veracity and their
interaction. Our key tests are on the interactions between veracity and the
treatment dummies (testing whether each treatment increases discernment – the
difference in sharing of true relative to false news – relative to control).
The data is analyzed at the response level, while mixed effects account for
variability across subjects and headlines. We adopt a Bayesian modelling
approach because the multilevel models with our mixed-effects structure did
not converge. We perform full Bayesian inference on the model parameters using
the R package BRMS (Bürkner 2017), which relies on Stan (Carpenter et al.
2017) to sample the posterior distribution using Markov Chain Monte Carlo. We
assigned vague and weakly-informative priors, which allow the data to ”speak
for themselves” (the model specifications are outlined in the Section 3 of the
Appendix). The significance of the coefficients extracted from the data is
used as a metric to quantify the observation of an effect. For a given
coefficient, we quantify significance with a “Bayesian p-value” (pbayes) by
counting the percent of posterior samples that are below (or above) zero. The
experimental design and analysis methods were preregistered before collecting
the data, and are available anonymized at
https://aspredicted.org/blind.php?x=2s8eh9.

## Results

The results of our main analysis are shown in Table 1. We observe a
significant interaction between _the Label condition_ and veracity, indicating
that even the simple warning label of the human-AI labeling increases
discernment (the difference in sharing of true and false news) relative to the
control (pbayes = 0.019, preregistered). We also observe a significant
interaction between _the Explanation Label condition_ and veracity, such that
explaining the label also increases sharing discernment (pbayes $<$ 0.001,
preregistered).

Figure 3: Factual manipulation check for participants in the two treatment
condtions.

This main result is summarised in Figure 2, where the marginal posterior
distributions for baseline discernment (i.e., in control) is plotted, together
with the two marginal posterior distributions representing the increase in
discernment for the warning label and the warning label with explanation
conditions, respectively.

As can be seen from the two interaction coefficients in Table 1, adding the
explanation roughly doubled the effect of the warnings on sharing discernment.
To assess whether this increase from explanations above and beyond the minimal
warning label was statistically significant, we compute the posterior
distribution of the difference between the two interaction terms. The mean
value is positive, reflecting the observed increase of discernment, but not at
a significant level (pbayes = 0.110, pre-registered). This result suggests
that an explanation may increase the efficacy of the label above and beyond
the warning label itself, but a conclusive answer would require an additional,
larger study.

### The role of trust

Next, we look at how perceived trust varies across conditions. To do so, we
perform a linear regression predicting trust, defined as the first principal
component of the ABI questions, as a function of the experimental condition
(dummy) and controlling for attentiveness. We do not observe any significant
difference in trust between _the Label condition_ and _the Explanation Label
condition_ (p=0.3293). In fact, we directionally observe that more attentive
subjects in _the Explanation Label condition_ report lower trust (p=0.09) but
the overall moderation of attentiveness is not significant either.

This suggests that, in so much as the explanation may increase the
effectiveness of the warnings, trust in the warning label is not the mechanism
by which this occurs. In addition, we find no significant differences between
the two Warning Label conditions in participant ratings of how helpful the
warning label was (p=0.253), how annoying the warning label was (p=0.345), how
well the warning label helped the user understand the labeling process
(p=0.169) or if the warning labels helped them understand what signals were
leveraged (p=0.56). We do find that participants in _the Explanation Label
condition_ said the warning labels provided new information marginally more
than in _the Label condition_ (p=0.067). This may suggest that the explanation
provides new information into the process that generated the the labels that
the user can use or act on in their sharing decisions.

### Factual manipulation check

After the main discernment task, we included manipulation checks to see what
participants thought they saw during the discernment task. These results of
these manipulation checks is shown in Figure 3. Several subjects in _the
Control condition_ report to have seen a warning label, and some participants
in both _the Label condition_ and _the Explanation Label condition_ stated
they did not (see the left pane of Figure 3). Participants who said they saw a
headline tagged with a warning message were then asked which warning labels
they saw. While participants in _the Label condition_ made up the plurality of
respondents who saw “some headlines were labeled false,” they were also more
likely to say they saw a warning label with an explanation than one without
(see the right pane of Figure 3). This suggests that our warning label design
worked up to a certain extent, but that even in a survey context — where
people are likely paying more attention than when scrolling though social
media feeds — users do not always realise they are presented with warning
labels or explanations, or they might believe they have seen warning labels
when they were not presented with them.

Figure 4: Posterior distributions for the difference in moderation of
discernment between the two treatment conditions.

### Additional moderators

Additional analyses help us determine which factors moderate the treatment
effect. We consider attention, propensity to go with one’s gut, as measured by
the critical reflection test (CRT), partisanship, age, education and gender as
possible moderators. Partisanship is coded as increasing means more
Republican. Gender is coded as 1 as female and 0 otherwise.

For each of these factors, we modify the model for the main analysis by
including this factor as a predictor, and re-run the Bayesian model. Each
moderator is scaled by subtracting the average and dividing by the standard
deviation. This ensures that the coefficients for the fixed effects are
compatible with the main analysis within statistical uncertainties.

We observe that more attentive participants, higher CRT participants, more
conservative participants, older participants, less educated participants, and
female participants share less at baseline. More attentive participants,
higher CRT participants and older participants are more discerning in their
baseline sharing. However, we do not observe any significant interactions
involving moderators, except for Education interacting with _the Label
condition_ , and attention interacting with _the Explanation Label condition_
(We also do not account for multiple comparisons here, which would render both
interactions non-significant). For statistical details, see Supplemental Table
S1. This general lack of moderation suggests that warning labels work equally
for a diverse set of users. However, this result fails to give us a sense of
the mechanisms by which warning labels increase discernment, which should be
explored in future work.

To better understand the mechanisms behind the increase in discernment due to
the explanation itself, we also measure and report the difference in
moderation of the treatment effect on discernment between _the Explanation
Label condition_ and _the Label condition_ (see Figure 4) By calculating a
credible interval for the difference between the two coefficients, we can
assess if explanations are deferentially effective for certain subgroups. We
find a significant difference between the 3-way interactions for age, CRT,
partisanship and education. This suggests that the explanations are
differentially more effective at increasing discernment among participants
with lower levels of education or CRT, who are older, and are more
conservative. We also observe a directional difference in 3-way interactions
for attention, whereby explanations are more effective for attentive users.

## Discussion

Our results suggest that providing users with warning labels created by a
hybrid AI-crowd system can increase sharing discernment relative to a no-label
control group. This suggests that labeling with human-AI warning labels has
promise. Given the pragmatic merits of this approach for detecting
misinformation at scale, our work suggests that people may indeed respond well
to such a labeling scheme.

We also found circumstantial evidence that providing more transparency via
explaining what the AI system is doing may boost the system’s efficacy above
and beyond the basic warning. This adds to the conversation about correction
explanations for debunking. Most work on corrective explanations focuses on
explanation depth at the headline or claim level (Lewandowsky et al. 2021),
but has not looked at the corrective depth of the debunking _mechanism_
itself. Ultimately, this approach is more scalable since all headlines share
the same mechanism explanation, and generating headline-level explanations can
be costly.

We also observed several significant moderators: explaining the warning labels
was more effective for participants with lower levels of education or CRT, who
are older, and are more conservative. These findings have important
implications for how future explanations are designed, and at whom they are
targeted. We also found directional moderation of attention, whereby
explaining the warning label was more effective for more attentive users. Even
in the survey context, where participants are presumably focused on the task
at hand, users did not always realize they were presented with warning labels
or explanations in the first place, or alternatively they might believe they
saw labels when they were not present. While explanations may provide an
important tool for boosting the efficacy of opaque human-AI systems, this
effectiveness could be undermined by environments where users are distracted
and thus do not even consider them. This finding suggests important avenues
for future work. First, it highlights the importance of designing warning
labels in general, and explanations in particular, that grab more attention
from users.

Further, our results suggest that (self-reported) user trust in the warning
label is not the mechanism by which explanations contribute to the efficacy of
machine recommendations. Despite a good deal of research that assumes a causal
relationship between interpretability, trust, and acceptance (Ribeiro, Singh,
and Guestrin 2016), previous works have been equivocal in proving these
relationships. Cramer et al. (2008) find that while explanations increase
acceptance of machine recommendations, they do not increase self-reported
trust or willingness to delegate to the system on a future task. Papenmeier,
Englebienne, and Seifert (2019) find that while explanations do not increase
self-reported trust, they do increase _observed_ trust, as measured by the
probability that users switch their pre-reported answers to match those of the
machine recommendation. In their experiment, this does not necessarily
increase accuracy, as the algorithm does not achieve 100% accuracy. Indeed, we
note that while achieving greater acceptance of machine recommendations is
equivalent to increasing discernment in our experiment, in general the goal of
explanations should be to increase _calibrated trust_ , that is, trust that is
appropriate for the performance of the system (Zhang, Liao, and Bellamy 2020).
Papenmeier, Englebienne, and Seifert (2019) additionally find that the
accuracy of the machine learning system has a greater effect on user trust,
both self-reported and observed, than the presence and quality of
explanations.

Our results are consistent with (Horne et al. 2019). While they consider
accuracy instead of sharing as the key outcome, our work corroborates the idea
that providing explanations to AI labels has potential to augment user
discernment. Further, our results support their findings on the heterogeneity
of the efficacy of such explanations. Our results concerning attentiveness and
explanation effect are also consistent with Langer, Blank, and Chanowitz
(1978). Studying human explanations (rather than AI), the authors found that
relevant explanations are in low-stakes problems no more effective than a
placebo explanation at increasing user acceptance of a request. However, when
the stakes were slightly higher, the quality of the explanation provided did
matter. The authors hypothesize that the higher stakes of the request induce
attentiveness where respondents were previously inattentive, thereby
increasing the importance of quality explanations. We believe this suggests
interesting future directions for better understanding the moderators of
warning label effectiveness, including evaluating users’ trust with more
measures than explicit self-reporting and understanding users’ pre-existing
experience with a given label source (e.g., fact checkers, AI, crowdsourcing)
and perceptions concerning the accuracy of that label source.

Our work has several limitations that must be considered. First, our work only
considers hypothetical sharing intentions, and therefore may suffer from
demand effects. Since previous work has shown that self-reported sharing
intentions correlates with actual sharing on Twitter (Mosleh, Pennycook, and
Rand 2020) and has replicated misinformation reducing interventions in Twitter
field experiments (Pennycook et al. 2021), we believe the use of online
surveys to be justified by providing a sandbox to test otherwise challenging
interventions. Second, in our study only false headlines were labeled by the
system. Future work should explore how discernment changes when the system
erroneously labels true items. Third, the nature of the human-AI explanation
we used, with a vetting process for users and a particular crowd-sourcing
methodology, is a specific representation from a large space of possible
systems. We chose this particular process because of its similarity to real
world systems, but future work should explore how these results generalize to
across other possible systems, such as different kinds of explanations,
differently designed labels, whether the AI labels or vets, and expert versus
non-expert crowds. Fourth, our study focused on the cultural context of
American misinformation, and recruited American participants. Future work
should explore how such findings translate to other cultural contexts. In
addition, our design cannot disentangle the effectiveness of warning labels
attributed to the human-AI system, versus any warning label at all. We chose
the human-AI system as a baseline because we are primarily focused on the role
of explanations, but more work is necessary to distinguish the effects of more
traditional warning labels (e.g. those from (Yaqub et al. 2020)) with the
specialized labels from the human-AI system. Future work might do so by
including controls for both a standard, non-attributed warning label, and the
human-AI baseline we used. Our study also relies on perceptions of a
_hypothetical_ human-AI labeling system. It is unclear if participants believe
that the warning labels actually came from a human-AI system, which may
undermine our results. Correspondingly, we did not investigate how the act of
sharing intersects the labeled content. For example, a user might be happy
that a piece of content was labeled and might want to share the warning label
itself with their friends, which in our data would be interpreted as the
warning label not working. These quandaries underscore the challenges of
understanding the nuances of genuine social media behavior in a survey
context.

Taking a step back, its important to note that our approach of design
interventions to combat misinformation is but one tactic to fight a system
with many actors and perverse incentives. Our intervention may systematically
benefit large companies by providing them “band-aid” solutions to the systemic
problems of an attention economy, rather than forcing them to consider the
underlying causes. Ultimately, we hope our work invigorates careful research
on how conventional approaches to fighting misinformation and making systems
transparent intersect with the subtle, complex and consequential psychological
dynamics of social media.

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sufficient condition for identifying causal effects. However, it is important
to note that this criterion is not necessary for causal effect identification.
To address this limitation, Perkovic (2020) introduces a graphical criterion
that is both necessary and sufficient for the identification of causal effects
in MPDAGs. In addition to the above developments, IDA algorithms and joint-IDA
Maathuis et al. (2009); Nandy et al. (2017); Perkovic et al. (2017); Witte et
al. (2020); Fang & He (2020); Liu et al. (2020) have been proposed for
identifying the total effect of a variable $A$ on the response $Y$ in an MPDAG
$\mathcal{G}$. These algorithms consider the orientation configurations of
edges connected to variable $A$ and enumerate a set of MPDAGs where the total
effect is identified. Specifically, Guo & Perkovic (2021) provide a
characterization of the minimal additional edge orientations required to
identify a given total effect.

###  Appendix I Discussion on accuracy-fairness trade-off

The trade-off between accuracy and fairness has been highlighted in numerous
studies on algorithmic fairness (Martinez & Bertran, 2019; Zhao & Gordon,
2019; Menon & Williamson, 2018; Zliobaite, 2015; Chen et al., 2018; Wei &
Niethammer, 2022). These studies highlight that improving fairness measures
often comes at the cost of reduced accuracy in machine learning models.
However, it is important to note that the existence of an accuracy-fairness
trade-off is not universal and depends on the specific data setting. Several
recent works have challenged the notion that accuracy must always decrease as
fairness increases (Dutta et al., 2020; Friedler et al., 2021; Yeom &
Tschantz, 2018). For instance, in the synthetic dataset discussed in Section
5.1, we observed an accuracy fairness trade-off, where improving fairness
measures resulted in a decrease in accuracy. However, such a trade-off was not
apparent in Figure 15d. The authors in (Wick et al., 2019; Sharma et al.,
2020; Dutta et al., 2020) provide theoretical and empirical insights into when
the trade-off exists and when it does not. These findings suggest that the
trade-off relationship between accuracy and fairness is context-dependent. It
highlights the need for further research to better understand the conditions
under which the accuracy fairness trade-off arises and identify strategies to
mitigate or overcome it.


# In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos

###### Abstract

Real-time and robust automatic detection of polyps from colonoscopy videos are
essential tasks to help improve the performance of doctors during this exam.
The current focus of the field is on the development of accurate but
inefficient detectors that will not enable a real-time application. We
advocate that the field should instead focus on the development of simple and
efficient detectors that can be combined with effective trackers to allow the
implementation of real-time polyp detectors. In this paper, we propose a
Kalman filtering tracker that can work together with powerful, but efficient
detectors, enabling the implementation of real-time polyp detectors. In
particular, we show that the combination of our Kalman filtering with the
detector PP-YOLO shows state-of-the-art (SOTA) detection accuracy and real-
time processing. More specifically, our approach has SOTA results on the CVC-
ClinicDB dataset, with a recall of 0.740, precision of 0.869, $F_{1}$ score of
0.799, an average precision (AP) of 0.837, and can run in real time (i.e., 30
frames per second). We also evaluate our method on a subset of the Hyper-
Kvasir annotated by our clinical collaborators, resulting in SOTA results,
with a recall of 0.956, precision of 0.875, $F_{1}$ score of 0.914, AP of
0.952, and can run in real time111Supported by Australian Research Council
through grants DP180103232 and FT190100525..

##  1 Introduction

Colorectal cancer is one of the most common forms of cancer [1]. The early
detection and removal of colorectal polyps before they become malignant is
known to improve long-term outcomes [2]. However, the effectiveness of a
clinician to detect polyps during a colonoscopy can be affected by many
factors, such as experience, professional background, time of day, and length
of the procedure, leading to a miss rate that has been measured to be as high
as $22\%-28\%$ [3]. The performance of clinicians can be improved with the use
of a real-time system that can automatically detect polyps during the
colonoscopy exam.

The real-time detection of colorectal polyps from colonoscopy videos is an
application that has received much attention in recent years [4, 5, 6]. Real-
time object detection has not been possible without considerable sacrifices in
accuracy until recently with the innovation of architectures such as YOLO (You
Only Look Once) [7], which shows competitive, but generally worse detection
accuracy than its more complex counterparts. The use of object trackers is a
natural way to compensate for the relatively poorer accuracy of these more
efficient detectors, but the field has not focused too much on this topic, as
evidenced by the small number of publications [8, 9].

Fig. 1: Polyp in video (5 frames apart)

This may be due to the lack of publicly available datasets with fully
annotated video sequences. Even with such constraint, we advocate that the
field must focus on the development of simple object trackers that, when
combined with efficient detectors, such as YOLO, can produce real-time robust
polyp detectors.

In this paper, we propose a simple and effective colorectal polyp tracker
based on Kalman filtering that works with relatively accurate but efficient
detectors. In particular, we combine our proposed Kalman filtering with the
PP-YOLO detector [10]. We show that our method has state-of-the-art (SOTA)
results on the CVC-ClinicDB dataset [11], with a recall of 0.740, precision of
0.869, $F_{1}$ score of 0.799, an average precision (AP) of 0.837, and can run
at a frame rate of 31.6 frames per second (fps). We also evaluate our method
on a subset of the Hyper-Kvasir [12] annotated by our clinical collaborators,
resulting in SOTA results, with a recall of 0.956, precision of 0.875, $F_{1}$
score of 0.914, an AP of 0.952, and can run at 31.6 fps.

##  2 Related Work

###  2.1 Object Detection

The current state-of-the-art methods for object detection all rely on CNNs and
can be classified as either two-stage or one-stage detectors [13]. Two-stage
detectors such as Regional Convolution Neural Networks (R-CNN) [14] first
propose regions of interest (RoIs) that may contain an object, then each is
classified separately by a CNN. In contrast, one-step detectors such as YOLO
[7], SSD (Single Shot Detector) [15], and RetinaNet [16] predict both the
bounding boxes and the classes in the same step.

###  2.2 Object Tracking

Object tracking is the task of tracking unique objects through a sequence of
video frames. Deep learning methods for this task can be categorised as
feature extraction-based or end-to-end methods [17]. Feature extraction-based
methods separate the detection and tracking tasks [17]. Detection is done with
object detection networks such as R-CNN [14], SDD [15], or YOLO [7] and the
extracted features and/or bounding boxes are used for tracking with either
classical methods such as Kernel Correlation Filters [18] or recurrent neural
networks [19]. Alternatively, end-to-end methods combine both detection and
tracking into a single step. These include Siamese networks, patch networks,
and graph-based networks [17]. Methods that use deep learning for tracking
learn both spatial and temporal patterns, and are therefore currently only
suited to domains with available annotated frame sequences. Additionally, due
to their increased complexity, they tend to be slower than other methods [5].

###  2.3 Polyp Detection

Competitive polyp detection methods are mainly based on deep learning methods
(e.g., Faster R-CNN and SSD) [20, 4]. As there is a trade-off between accuracy
and speed, many methods with SOTA accuracy are incapable of real-time
detection [20], resulting in limited clinical applicability. Hence, the field
has focused on the development of real-time detectors that retain some of the
accuracy of the SOTA approaches [10]

Such relatively poorer accuracy can be compensated by the use of temporal
information, such as a voting window [4] or a decision tree [21] that works
over successive frames to reduce the number of false positives. Even though
such methods are promising, they were evaluated with older and inaccurate
object detectors. SOTA detection methods that harness temporal information
have been proposed [20], but require a large training set containing fully
annotated video sequences, which is challenging to acquire.

Recently proposed polyp tracking methods can be categorised by whether they
use online or offline learning. Online methods consist of a trained object
detector that provides features, bounding boxes, or image patches to a tracker
that learns the representation of a specific polyp at inference time. Such
methods include RYCO [22] and AIPDT [5], both of which use discriminate
correlation filter-based trackers that learn image patches. While AIPDT can
track polyps in real time, online methods may have difficulty tracking polyps
that rapidly change appearance due to alterations in lighting or occluding
material, as they learn a narrow representation during inference.
Alternatively, offline methods can learn temporal patterns from large data
sets. A recent example of an offline method is the Spatial-Temporal Feature
Transform (STFT) [23] that uses the current frame and one or more previous
frames as inputs. STFT produces SOTA detection accuracy, but is unable to run
in real-time.

A major challenge in the field is the ability to provide a fair comparison
between these methods because some methods have only been evaluated on private
datasets [24, 20]. To mitigate this problem, we use two public datasets [11,
12] to compare our approaches to SOTA methods [16, 23, 10].

##  3 Method

Our training set is denoted by
$\mathcal{D}=\\{(\mathbf{x}_{i},\mathbf{y}_{i})\\}_{i=1}^{|\mathcal{D}|}$
containing images $\mathbf{x}\in\mathcal{X}\subset\mathbb{R}^{H\times W\times
C}$ of size $H\times W$ with $C$ colour channels, and labels
$\mathbf{y}\in\mathcal{Y}\subset\mathbb{R}^{4}$ consisting of the bounding box
of the polyp (i.e., 2-dimensional centre, and width and height). This training
set is used to train a polyp detector (e.g., PP-YOLO [10] or RetinaNet [16]),
denoted by a $\theta$-parameterised function
$f_{\theta}:\mathcal{X}\to\mathcal{B}$ that takes an image
$\mathbf{x}\in\mathcal{X}$ and produces a set of bounding boxes
$\mathcal{B}=\\{(\mathbf{y}_{b},c_{b})\\}_{b=1}^{|\mathcal{B}|}$, with
$c_{b}\in[0,1]$ denoting the probability that the model is confident in the
detection. The testing set is represented by a set of $|\mathcal{T}|$
colonoscopy videos containing $T$ frames, denoted by
$\mathcal{T}=\\{(\mathbf{x}_{i,t},\mathbf{y}_{i,t})\\}_{i=1,t=1}^{|\mathcal{T}|,T}$.
An important difference of the testing set, compared with the training set, is
that video frames may not have any polyp, so the annotation
$\mathbf{y}_{i,t}\in\mathcal{Y}$ is redefined to also contain $\emptyset$.

Our main contribution is the development of a simple and effective Kalman
filtering that runs during testing to improve the accuracy of the detector. We
describe our Kalman filtering in more detail below.

###  3.1 Kalman Filtering

Kalman filtering is a method for estimating unknown variables in a discrete
time linear dynamics system using measurements that are assumed to have
Gaussian noise [25]. We use Kalman filtering to estimate a polyp location and
size across video frames by using the output of the detector as measurements.
The state vector for frame $t\in\\{1,...,T\\}$ is defined as:

$\mathbf{s}_{t}=[x_{t},y_{t},w_{t},h_{t},\Delta x_{t},\Delta y_{t},\Delta
w_{t},\Delta h_{t}]^{T},$ (1)

where $\mathbf{y}_{t}=[x_{t},y_{t},w_{t},h_{t}]$ ($x_{t},y_{t}$ denote the
bounding box centre, $w_{t},h_{t}$ represent the width and height of the
bounding box), and $\Delta x_{t},\Delta y_{t},\Delta w_{t},\Delta h_{t}$ are
the rates of change of the bounding box centre and size, which are estimated
using the relationship described by the state transition model $\mathbf{F}$
below.

Kalman filtering assumes the system transitions from time step $t-1$ to $t$
with noise $\mathbf{w_{t}}\sim\mathcal{N}(\mathbf{0},\mathbf{Q})$ (where
$\mathcal{N}(\mathbf{0},\mathbf{Q})$ denotes the Gaussian distribution of mean
$\mathbf{0}$ and covariance $\mathbf{Q}$) according to the equation:

$\mathbf{s}_{t}=\mathbf{F}\mathbf{s}_{t-1}+\mathbf{w}_{t},$ (2)

where $\mathbf{F}$ denotes the state transition model describing how the
linear dynamic changes through time, defined as

$\mathbf{F}=\begin{bmatrix}1&0&0&0&\Delta t&0&0&0\\\ 0&1&0&0&0&\Delta t&0&0\\\
0&0&1&0&0&0&\Delta t&0\\\ 0&0&0&1&0&0&0&\Delta t\\\ 0&0&0&0&1&0&0&0\\\
0&0&0&0&0&1&0&0\\\ 0&0&0&0&0&0&1&0\\\ 0&0&0&0&0&0&0&1\\\ \end{bmatrix}.$

The relation between the system state $\mathbf{s}_{t}$ and measurement
$\mathbf{z}_{t}$ with noise
$\mathbf{v}_{t}\sim\mathcal{N}(\mathbf{0},\mathbf{R})$ is computed as:

$\mathbf{z}_{t}=\mathbf{H}\mathbf{s}_{t}+\mathbf{v}_{t},$

where $\mathbf{R}$ represents the noise covariance matrix for the
measurements, and $\mathbf{H}$ describes the measurement model, defined as:

$\mathbf{H}=\begin{bmatrix}\mathbf{I}_{4\times 4}&\mathbf{0}_{4\times
4}\end{bmatrix},$

with $\mathbf{I}_{4\times 4}$ denoting a $4\times 4$ identity matrix, and
$\mathbf{0}_{4\times 4}$ a $4\times 4$ zero matrix. The filtering process
consists of two steps: a prediction step and an update step. The prediction
step estimates the _a priori_ state $\mathbf{\hat{s}^{\prime}_{t}}$ and error
$\mathbf{\hat{P}^{\prime}_{t}}$:

$\displaystyle\begin{split}k_{predict}(\mathbf{\hat{s}}_{t-1})=\mathbf{\hat{s}^{\prime}}_{t}&=\mathbf{F}\mathbf{\hat{s}}_{t-1},\\\
\mathbf{\hat{P}^{\prime}}_{t}&=\mathbf{F}\mathbf{\hat{P}}_{t-1}\mathbf{F}^{T}+\mathbf{Q},\end{split}$
(3)

where $\mathbf{\hat{s}^{\prime}}_{t}$ is the _a priori_ state estimate,
$\mathbf{\hat{P}^{\prime}}_{t}$ denotes the predicted _a priori_ estimate
covariance, and $\mathbf{\hat{P}}_{t-1}$ represents _a posteriori_ estimated
covariance from step $t-1$. The update step uses the best matching measurement
from the detector, $\mathbf{z}_{t}=\mathbf{y}_{b}$, as determined by data
association (5) to estimate the _a posteriori_ state $\mathbf{\hat{s}}_{t}$
and error $\mathbf{\hat{P}^{\prime}}_{t}$. Before this is done, the Kalman
gain, $\mathbf{K_{t}}$, is calculated and used to scale the effect of the
measurement given its estimated accuracy:

$\displaystyle\begin{split}\mathbf{K_{t}}&=\mathbf{\hat{P}^{\prime}}_{t}\mathbf{H}^{T}(\mathbf{H}\mathbf{\hat{P}^{\prime}}_{t}\mathbf{H}^{T}+\mathbf{R})^{-1},\\\
\mathbf{\hat{P}_{t}}&=(\mathbf{I}-\mathbf{K_{t}}\mathbf{H})\mathbf{\hat{P}^{\prime}}_{t},\\\
\mathbf{\hat{s}}_{t}&=\mathbf{\hat{s}^{\prime}}_{t}+\mathbf{K_{t}}(\mathbf{z}_{t}-\mathbf{H}\mathbf{\hat{s}^{\prime}}_{t}).\end{split}$
(4)

The noise covariances $\mathbf{Q}$ and $\mathbf{R}$ are difficult to estimate,
so following [25], we assume that noise in state transition model is
independent, with $\mathbf{Q}=\mathbf{I}_{8\times 8}\times 0.01$, where
$\mathbf{I}_{8\times 8}$ is an $8\times 8$ identity matrix; and
$\mathbf{R}=\mathbf{0}_{4\times 4}$ (i.e., a $4\times 4$ zero matrix).

###  3.2 Data Association

At each image of the video, there can be multiple polyp detections, so it is
important that tracked polyps are not confused with one another. Hence, our
method also relies on data association to enable multi-target tracking. Data
association links detected bounding boxes (from the detector) with tracked
bounding boxes (from the Kalman filter). Assuming that the detections from
$t^{th}$ frame produces $\mathcal{B}=f_{\theta}(\mathbf{x}_{t})$, where
$\mathcal{B}=\\{\mathbf{y}_{b},c_{b}\\}_{b=1}^{|\mathcal{B}|}$, we first run a
non-max suppression, where we only keep the highest confidence detections
among bounding boxes that have an overlap difference score larger than
$\alpha$. This score between bounding boxes $\mathbf{y}_{i}$ and
$\mathbf{y}_{j}$ is computed with:

$s(\mathbf{y}_{i},\mathbf{y}_{j})=\sqrt{\lvert
4w_{i}h_{i}-4w_{i}h_{j}\rvert}+\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}},$
(5)

where $(x_{i},y_{i})$ denotes the bounding box centre and $(w_{i},h_{i})$
represents the bounding box width and height (similarly for
$(x_{j},y_{j},w_{j},h_{j})$). The non-max suppressed detections are stored in
the set $\tilde{\mathcal{B}}_{t}$. Then, we use Kalman prediction from (3) to
estimate the location of tracked polyps from the previous frame
$\mathcal{P}_{t-1}=\\{\mathbf{s}_{p},c_{p}\\}_{p=1}^{|\mathcal{P}_{t-1}|}$, as
in
$\tilde{\mathcal{P}}_{t}=\\{\\{k_{predict}(\mathbf{s}_{p}),c_{p}\\}:\forall\\{\mathbf{s}_{p},c_{p}\\}\in\mathcal{P}_{t-1}\\}$
Next, we associate the detections from $\tilde{\mathcal{B}}_{t}$ with polyps
in $\tilde{\mathcal{P}}_{t}$ by matching detections with overlap difference
score (5) smaller than $\beta$ and detection confidence larger than $\phi$.
These matched detections are then used in the Kalman update in (4) to update
the list of tracked polyps in $\tilde{\mathcal{P}}_{t}$. If a tracked polyp
has not been updated within $\epsilon$ time steps, it is discarded.
$\mathcal{P}_{t}$ then consists of the updated polyps, as well as unmatched
detections from $\tilde{\mathcal{B}}_{t}$ with confidence larger than
$\gamma$.

##  4 Experimental Setup

###  4.1 Datasets

We use two public datasets to test our system: Hyper-Kvasir and CVC-ClinicDB
datasets. The Hyper-Kvasir [12] dataset is a broad gastrointestinal endoscopy
dataset with examples of many different pathological findings. It has 1000
colonoscopy images of colorectal polyps annotated with bounding boxes, as well
as 72 colonoscopy videos that contain colorectal polyps. These videos are not
labelled frame-by-frame, so we select six to be annotated with the supervision
of a gastroenterologist. The videos are selected so there is no overlap with
the 1000 annotated images. This is done by using a MobileNet [26] model
trained on the ImageNet dataset [27] and then calculating the cosine
similarity between the features of each image-frame pair. This method is
validated on known matches between images and videos.

Two separate configurations are used for training, the first is image-based,
which uses 800 of the Hyper-Kvasir images for training, and the remaining 200
for validation. The second is video-based and uses 4 of the Hyper-Kvasir
videos for training.

Testing is done with the 2 remaining Hyper-Kvasir videos and the CVC-ClinicDB
dataset [11], which is composed of 612 annotated colorectal polyp images.
While CVC-ClinicDB is not strictly a video database, it comprised of many
short colonoscopy sequences, which allows for methods like Kalman filtering
and STFT [23] to take advantage of temporal information.

###  4.2 Detectors and Training

We test the application of our Kalman filtering to three different types of
polyp detectors: RetinaNet [16], PP-YOLO [10] and STFT [23]. While two
versions of RetinaNet and PP-YOLO are trained exclusively on either the image
or video data sets, STFT can only be trained with the video data set. PP-YOLO,
RetinaNet and STFT are trained for 60,000, 8,000 and 35,000 iterations
respectively, with a batch sizes of 5222The authors of PP-YOLO use a batch
size of 24, but due to hardware limitations, we were restricted to 5, which
may cause the model to not converge optimally., 4 and 1, and learning rates of
0.000625, 0.0001 and 0.000125. All methods are optimised with SGD with a
momentum factor of 0.9. The 4 Hyper-Kvasir videos in the training set are used
to estimate the parameters of the data association for Kalman filtering, where
average precision (AP – explained below in Sec. 4.3) is used to select the
following parameter values for PP-YOLO and STFT: $\alpha=700$, $\beta=295$,
$\epsilon=17$, $\phi=0.17$, and $\gamma=0.0575$. For RetinaNet we instead use
$\alpha=700$, $\beta=295$, $\epsilon=1$, $\phi=0.8$, and $\gamma=0.09$.

###  4.3 Evaluation

The detection methods are quantitatively assessed with precision, measured
with $P=\frac{TP}{TP+FP}$, and recall $R=\frac{TP}{TP+FN}$. True positives are
determined by the intersection-over-union (IoU); the area of the intersection
of the predicted bounding box and the ground truth bounding box divided by the
area of their union. For this application an IoU $\geq 0.2$ is considered to
be a true positive. We also evaluate the detectors with average precision
(AP), which is the average value of the precision as a function of the recall
in the interval $[0,1]$. It is defined with $P_{n}$ and $R_{n}$ being the
precision and recall at the $n^{th}$ threshold as [28]:

$\displaystyle AP=\sum_{n}(R_{n}-R_{n-1})P_{n}.$

We also calculate the $F_{1}$ score; the harmonic mean of the precision and
recall, defined by $F_{1}=2\times\frac{P\times R}{P+R}$. Additionally, the
Free-response receiver operating characteristic (FROC) curve is displayed by
plotting recall against the number of false positives per minute of video
(assuming 25 frames per second) as the classification threshold is changed
[29]. To assess the efficiency of the methods we report the amount of time
they need to process a single image and the number of frames per second (fps)
they can process.

The performance of the system is evaluated on the entire CVC-ClinicDB dataset
and the 2 annotated test videos from the Hyper-Kvasir dataset. The computer
used in this evaluation was equipped with Intel i7-6700K, 16GB memory, and
Nvidia GTX 1070 8GB.

##  5 Results and Discussion

Table 1: Results on CVC-ClinicDB

Method | Precision | Recall | $F_{1}$ | $AP$ | Inference Time (SD) | Frame Rate
---|---|---|---|---|---|---
Image Trained
RetinaNet | 0.836 | 0.897 | 0.865 | 0.942 | 113.03 (5.43) | 8.8
RetinaNet + Kalman | 0.900 | 0.936 | 0.918 | $\mathbf{0.977}$ | 114.74 (5.96) | 8.7
PP-YOLO | 0.869 | 0.873 | 0.871 | 0.904 |  $\mathbf{30.58}$ (0.19) | $\mathbf{32.7}$
PP-YOLO + Kalman | $\mathbf{0.948}$ | $\mathbf{0.948}$ | $\mathbf{0.948}$ | 0.963 | 31.60 (0.53) | 31.6
Video Trained
RetinaNet | 0.390 | 0.600 | 0.294 | 0.189 | 113.03 (5.43) | 8.8
RetinaNet + Kalman | 0.390 | 0.600 | 0.473 | 0.453 | 114.74 (5.96) | 8.7
STFT | 0.509 | 0.608 | 0.554 | 0.545 | 529.10 (33.10) | 1.89
STFT + Kalman | 0.737 | 0.796 | 0.765 | $\mathbf{0.868}$ | 531.54 (34.75) | 1.88
PP-YOLO | 0.467 | $\mathbf{0.827}$ | 0.597 | 0.654 | $\mathbf{30.58(0.19)}$ | $\mathbf{32.7}$
PP-YOLO + Kalman | $\mathbf{0.869}$ | 0.740 | $\mathbf{0.799}$ | 0.837 | 31.60 (0.53) | 31.6

Fig. 2: Precision-recall curves of video trained methods on CVC-ClinicDB Fig.
3: FROC curves of video trained methods on CVC-ClinicDB

Table 1 shows the precision, recall, $F_{1}$ and $AP$ improvements brought by
the application of Kalman filtering to the RetinaNet, STFT and PP-YOLO
detectors on the CVC-ClinicDB dataset. It can be seen that for almost all
cases, the use of our Kalman filtering improves precision, recall, $F_{1}$ and
$AP$ for all detectors with negligible computational cost. The prevision and
recall improvement on CVC-ClinicDB can be seen in Fig. 2 for all recall
values, and the FROC curve in Fig. 3 shows the clear advantage of using Kalman
filtering for all detectors.

Table 2: Results On Hyper-Kvasir Videos

Method | Precision | Recall | $F_{1}$ | $AP$ | Inference Time (SD) | Frame Rate
---|---|---|---|---|---|---
Image Trained
RetinaNet | 0.839 | 0.907 | 0.872 | 0.919 | 113.03 (5.43) | 8.8
RetinaNet + Kalman | 0.840 | $\mathbf{0.969}$ | 0.900 | $\mathbf{0.965}$ | 114.74 (5.96) | 8.7
PP-YOLO | 0.870 | 0.950 | 0.908 | 0.936 | $\mathbf{30.58(0.19)}$ | $\mathbf{32.7}$
PP-YOLO + Kalman | $\mathbf{0.875}$ | 0.956 | $\mathbf{0.914}$ | 0.952 | 31.61 (0.53) | 31.6
Video Trained
RetinaNet | 0.326 | 0.462 | 0.382 | 0.310 | 113.03 (5.43) | 8.8
RetinaNet + Kalman | 0.626 | 0.419 | 0.502 | 0.421 | 114.74 (5.96) | 8.7
STFT | 0.409 | 0.920 | 0.566 | 0.474 | 529.10 (33.10) | 1.89
STFT + Kalman | $\mathbf{0.685}$ | 0.918 | $\mathbf{0.785}$ | $\mathbf{0.787}$ | 531.54 (34.75) | 1.88
PP-YOLO | 0.269 | 0.210 | 0.236 | 0.137 | $\mathbf{30.58(0.19)}$ | $\mathbf{32.7}$
PP-YOLO + Kalman | 0.608 | 0.236 | 0.340 | 0.196 | 31.60 (0.53) | 31.6

Fig. 4: Precision-recall curves of video trained methods on Hyper-Kvasir Fig.
5: FROC curves of video trained methods on Hyper-Kvasir

Similarly, Table 2 shows the results of RetinaNet, STFT and PP-YOLO (with and
without Kalman filtering) on Hyper-Kvasir. Again, the computational cost of
Kalman filtering is negligible, where PP-YOLO with and without Kalman
filtering is the only approach with real-time processing. The prevision and
recall improvement on Hyper-Kvasir is displayed in Fig. 4, and the FROC curve
in Fig. 5 shows that Kalman filtering helps to increase the recall for all
values of false positives. Note that for all methods, the only approaches that
can produce real-time analysis is PP-YOLO with and without Kalman filtering.

##  6 Conclusion

For detecting and tracking colorectal polyps, Kalman filtering always improves
detection accuracy with negligible additional computational cost. The
difference in performance on CVC-ClinicDB and Hyper-Kvasir shows the need for
public datasets that better represent realistic operating conditions for
colorectal polyp detection systems. To show that these systems perform well
enough to be widely deployed, they will need to be evaluated on datasets that
are representative of their deployed environment.

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