=Paper=
{{Paper
|id=Vol-2582/paper2
|storemode=property
|title=Position: We Can Measure XAI Explanations Better with Templates
|pdfUrl=https://ceur-ws.org/Vol-2582/paper2.pdf
|volume=Vol-2582
|authors=Jonathan Dodge,Margaret Burnett
|dblpUrl=https://dblp.org/rec/conf/iui/DodgeB20
}}
==Position: We Can Measure XAI Explanations Better with Templates==
https://ceur-ws.org/Vol-2582/paper2.pdf
Position: We Can Measure XAI Explanations Better
with Templates
Jonathan Dodge Margaret Burnett
dodgej@eecs.oregonstate.edu burnett@eecs.oregonstate.edu
Oregon State University Oregon State University
ABSTRACT Explanation Satisfaction is defined as the degree to which
This paper argues that the Explainable AI (XAI) research commu- users feel that they understand the AI system or process being
nity needs to think harder about how to compare, measure, and explained to them. Compared to goodness, satisfaction is a
describe the quality of XAI explanations. We conclude that one (or contextualized, a posteriori judgment of explanations.”
a few) explanations can be reasonably assessed with methods of the In this paper, we will use ExpG and ExpS to refer to Hoffman’s
“Explanation Satisfaction” type, but that scaling up our ability to concepts of Explanation Goodness and Satisfaction, respectively.
evaluate explanations requires more development of “Explanation Now, consider the three highlighted properties in each definition:
Goodness” methods.
• Contextualization: ExpS is defined relative to a task, while
CCS CONCEPTS ExpG is not.
• Human-centered computing → User studies. • Actor: ExpS is measured from the perspective of a user per-
forming a task, while ExpG is from the perspective of re-
KEYWORDS searchers (ideally dispassionate bystanders, but often the
designers themselves).
Explainable AI, Evaluating XAI Explanations, Empirical Studies,
• Timing: Because ExpS is defined relative to a task, it must
Heuristic Evaluations
be measured after the task is completed, while ExpG can be
ACM Reference Format: measured anytime.
Jonathan Dodge and Margaret Burnett. 2020. Position: We Can Measure
XAI Explanations Better with Templates. In Proceedings of the IUI workshop The main thesis of this paper will be that we, as a research
on Explainable Smart Systems and Algorithmic Transparency in Emerging community, need to think harder about how to compare, measure,
Technologies (ExSS-ATEC’20). Cagliari, Italy, 5 pages. and describe the ExpG of explanation templates (clarified later in
Section 5), as a potentially strong complement to ExpS.
1 INTRODUCTION To develop the main thesis, we attempt to argue several points:
As AI plays an ever-increasing role in our lives, society needs a
• Background: Most current research has focused on ExpS.
variety of tools to inspect them. Explanations have emerged to fill
• Tasks: ExpS is easier to operationalize, but incurs a great deal
that role, but measuring their quality continues to prove challenging.
of experimental noise.
Hoffman et al. [10] offers two terms to describe mechanisms for
• Benefits: ExpS’s usefulness is hampered by participants’ lim-
measuring the quality of an explanation, quoted at length here,
ited exposure to the system.
with highlighting added to assist this paper’s discussion.
• Scope: ExpG affords the opportunity to consider a wider range
“Explanation Goodness: Looking across the scholastic and of behaviors, making ExpG mechanisms particularly well
research literatures on explanation, we find assertions about suited to reasoning about explanation templates.
what makes for a good explanation, from the standpoint of
statements as explanations. There is a general consensus on Through these points, we hope to provoke thought about how
this; factors such as clarity and precision. Thus, one can look explanation designers can better validate their design decisions via
at a given explanation and make an a priori (or decontextual- ExpG for explanation templates. This is of particular importance
ized) judgment as to whether or not it is “good.” ... In a proper because a great many design decisions are never evaluated via
experiment, the researchers who complete the checklist [eval- ExpS mechanisms.
uation of the explanation] with reference to some particular
AI-generated explanation, would not be the ones who created 2 BACKGROUND: MOST CURRENT
the XAI system under study.” RESEARCH HAS FOCUSED ON EXPS
“Explanation Satisfaction: While an explanation might be How have past researchers evaluated XAI design decisions? Most
deemed good in the manner described above, it may at the have used ExpS mechanisms, but a few have used ExpG mechanisms.
same time not be adequate or satisfying to users-in-context. For both types, an important criterion is rigor. The dangers of
departure from rigorous processes for validating design decisions
ExSS-ATEC’20, March 2020, Cagliari, Italy can be potentially severe if it devolves into “I methodology” [24], i.e.,
Copyright © 2020 for this paper by its authors. Use permitted under Creative designers relying solely on their own views and assumptions about
Commons License Attribution 4.0 International (CC BY 4.0).
what their users will need and how they will use the functionalities
the designers decide to provide.
ExSS-ATEC’20, March 2020, Cagliari, Italy Jonathan Dodge and Margaret Burnett
100%
2.1 Research that uses ExpS mechanisms 80%
Through extensive literature review, Hoffman et al. [10] identified a
Average % Accuracy
60%
group of existing ExpS methods for mental model elicitation (their 40%
Table 4). Among them, many are essentially qualitative and focus 20%
on things people say (e.g. Think Aloud or Interview techniques). 0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14
We felt the “Retrospection Task” [18] and the “Prediction Task” [20]
looked to be the most well suited for quantitative study, and chose Figure 1: (Source: Anderson et al. [4]) Percentage of partic-
to use them for Anderson et al.’s empirical studies [4]. Approaching ipants in four explanation treatments (4 colors) correctly
the problem from another angle, Dodge et al. [8] investigated several predicting the AI’s action at 14 decision points. This image
aspects of perceptions of fairness and explanations in a decision shows: 1. No treatment was a clear winner. 2. Some decisions
support setting. were easy enough that all participants predicted correctly—
Other researchers have used ExpS to understand a wide va- even those without explanations. 3. Conversely, others were
riety of effects in explanation. Providing explanation has been hard enough that few participants predicted correctly, even
shown to improve mental models [15, 16]. Of particular impor- with explanations. 4. There is no evident learning effect (de-
tance to moderating the effects of explanation is the explanation’s cision points are shown sequentially over time).
soundness and completeness [17]; most easily described with
the phrase “the whole truth (completeness) and nothing but the
truth (soundness)” about how the system is really working. Note computing to another scientist. Then imagine the same person
that neither soundness nor completeness are binary properties, but giving the same explanation to a child1 . The explanation itself
a smooth continuum—with 100% soundness or completeness not could be high quality, it just was not appropriate for that audi-
always achievable. Explanation has also been shown to increase ence and needed reformulation. Thus, empirically measuring the
satisfaction (here we mean in the colloquial sense, the user’s self- explanation’s quality is entangled with many factors beyond the
reported feeling) [2, 12], and understanding—particularly in low explanation itself.
expertise observers [27]. Several different kinds of explanation have Second, there is a great deal of variability in the state/action
also been shown to improve user acceptance via setting appropriate space (as we observed in [4]). This leads some choices to be easy,
expectations (e.g. by showing an accuracy gauge) [14]. There are causing all treatments to have nearly 100% participant prediction
many other researchers studying explanations using ExpS mech- accuracy—even those without explanation (e.g. the 5th decision
anisms, and we refer the reader to Abdul et al. [1] for a recent point in Figure 1). In contrast, others are much harder, and all
literature review. treatments had nearly 0% prediction accuracy (e.g. the 4th decision
point in Figure 1). As a result of these floor and ceiling effects, some
2.2 Research that uses ExpG mechanisms of the variation between treatments is obscured.
One XAI tool for ExpG is the checklist proposed by Hoffman et Third, it is difficult to assign “partial credit” for participants’ pre-
al. [10]’s Appendix A, composed of 8 yes/no questions that re- dictions. In Figure 1’s case, participants faced a choice of 4 options,
searchers and designers ask themselves (e.g. “The explanation of leading random guessing to be right 25% of the time. However, AI
the [software, algorithm, tool] is sufficiently detailed.”). Other ap- is regularly used in domains with much larger action spaces, so the
proaches include Amershi et al. [3]’s guidelines for interactive AI, probability a participant picks right can be vanishingly small. As
Kulesza et al. [15]’s design principles for explanatory debugging, a result, it seems natural to think about which answers might be
and Wang et al. [25]’s guidelines to match human reasoning pro- considered better than others. To do so, one might consider simi-
cesses with XAI techniques. However, most XAI research is not larity in the action space (actions that look similar) or in the value
explicit about usage of these or other ExpG mechanisms. space (actions that produce similar consequences), but either way it
is a challenge to design rigorously.
3 TASKS: EXPS IS “EASY” TO
4 BENEFITS: EXPS’S USEFULNESS IS
OPERATIONALIZE, BUT NOISY
HAMPERED BY LIMITED EXPOSURE
ExpS sings a siren song of sorts; it appears simple to evaluate, one
must simply define a task and criteria to measure performance at Consider that in-lab user studies are typically designed to be exe-
that task. Easy enough, right? Wrong. cuted within a 2-hour window for a variety of reasons (e.g. reliabil-
We have been using one of the XAI tasks which we felt would ity). As a result, the amount of participant exposure to the system is
be most well suited for quantitative study, the “Prediction Task”, actually quite low. As an example, in Anderson et al.’s study [4] we
proposed by Muramatsu et al. [20]. However, we have run into a showed 14 decision points to participants over the available 2 hours.
number of challenges using it in our XAI studies, some of which In that paper, we point to this limited exposure as a possible reason
are apparent in Figure 1, taken from Anderson et al. [4]. that we did not observe any learning effect (evident in Figure 1).
First, participants’ ability to perform the task (predict an AI’s Other challenges also surround exposing participants to the
next actions) in a domain are moderated by a number of other system when performing a ExpS evaluation. In particular, which
things, such as their need for cognition, interest in the task, domain decision points do we show to participants? Because this agent has
experience, etc. To illustrate the effect of variability in explana- 1 https://www.youtube.com/watch?v=OWJCfOvochA conducts a similar exercise,
tion consumers, imagine a scientist effectively describing quantum though Dr. Gershon changes the explanation for 5 different audiences.
Position: We Can Measure XAI Explanations Better
with Templates ExSS-ATEC’20, March 2020, Cagliari, Italy
***Prediction: likely to reoffend
The training set contained 8
individuals matching this one.
2 of them reoffended (25%)
***Prediction: likely to reoffend
The training set contained 151
individuals matching this one.
93 of them reoffended (61%)
Figure 2: Left: Two example explanations provided by the system used by Dodge et al. [8]. The black text is static and the added
red highlights show text that will be based on calculations about the input—intended to show how explanation templates get
filled in. It demonstrates how the ExpS results vary based on the input (e.g. the top explanation is far less convincing). Right:
Histogram of the matching percentages underlined in Figure 2, for the classifiers trained on raw and processed data. These
histograms show how differently the two classifiers behaved, but also show an interesting result—namely how often case-based
explanation self-refutes (by providing low %s), or does not substantiate any claim (by giving near 50%, in a binary classification
setting). However, this insight might not have been observable for a user under the ExpS formulation, as users typically only
work with a small number of explanations and self-refuting ones are rare.
been training for 30,000 episodes, scrutinizing the training data in types, which they term “Case-based explanations”, is demonstrated
its entirety would be a daunting task. After training is complete, in the left side of Figure 2 as we used them in Dodge et al. [8] to
one could imagine presenting test cases, some of which could be explain an AI system’s judicial sentencing recommendations. Note
handcrafted. One way to select test cases to present to assessors is a that the templates shown in this paper are for textual explanations,
recent approach to solving the problem of which decision points to but the idea extends naturally to other types of explanations, like
show by Huang et al. [11]. Their approach measures the “criticality” the visual explanations in Mai et al.’s Figures 1 and 2 [19].
of each state, and chooses the ones where the agent perceived its
choice to matter the most.
Note again, the large variability in state/action space, which we
commented on in Section 3. When combined with limited exposure, 5.2 Why consider explanation templates?
participants are essentially gazing into a vast expanse of behavior An explanation template can be combined with appropriate soft-
through a tiny peephole. ware infrastructure and a test set to generate a large set of ex-
planation instances. We argue that examining the distribution of
5 SCOPE: EXPG CAN CONSIDER A WIDER thousands of explanations generated in this way can be more illu-
minating than seeing individual ones (e.g., Figure 2, right).
RANGE OF BEHAVIORS, VIA TEMPLATES
Although ExpG mechanisms can be used on any explanation
One great advantage of ExpG is, it supports explanation templates. template one desires, consider using a case-based explanation tem-
plate. One way to produce these explanations is by finding training
5.1 What is an explanation template? examples “near” the input, then characterize how well the labels of
The earliest evidence we could find for what we call “explanation the resultant set match the label of the input (Figure 2, left). If we
templates” is from Khan et al. [13], and we think studying them can run an explanation generator on the whole test set, we can create a
help address some of the problems discussed earlier in this paper. histogram from those matching percentages, shown on the right
Explanation templates operate on a different granularity than an side of Figure 2.
explanation. If an explanation describes or justifies an individual However, this introduces an issue with explanation soundness.
action, the explanation template is like the factory that creates the To see why, note that many instances fall near 100%—these are
explanation. the explanations a user might find “convincing”. However, there
We have built templates inspired by Binns et al. [5], who used a is also a good chance the explanation finds that around 50% of
wizard of oz methodology to generate multiple types of explanation the nearby training examples matched the input’s label—these are
for a decision support setting. The decision they were trying to the explanations that do not substantiate any claim (note that the
explain was an auto insurance quote ([5]’s Figure 2). One of their classifier is binary). Even worse, there are instances that fall near
ExSS-ATEC’20, March 2020, Cagliari, Italy Jonathan Dodge and Margaret Burnett
0%—these are the self-refuting explanations2 along the lines of “This ***Prediction: likely to reoffend
instance was labelled an A because all the nearby training examples
The training set contained 80 individuals matching this one.
were B’s.”. 20 of them reoffended (25%)
In this circumstance, the lack of soundness arises from the fact
that the explanation uses nearest neighbors while the underlying ***Prediction: likely to reoffend
classifier does not. Note also that the fix for these two problems is
different. When the explanation lacks evidence, one should go find The training set contained 20 individuals matching this one
who also reoffended.
more evidence. But when the explanation self-refutes, one must
figure out why the contradiction exists.
Note that if we were evaluating these explanations with ExpS, the Figure 3: Two alternative designs for a case-based explana-
result would depend strongly on whether the provided explanation tion template. The top shows one used in Dodge et al. [8],
was “convincing” or “self-refuting”—but the explanation template is while the bottom illustrates strawman proposal for the dis-
the same in both cases, only the input changed. On the other hand, cussion in this paper. Note how the alternative fails to ac-
ExpG allows us to consider the wider scope—that the template is knowledge nearby training examples that did not match the
capable of generating explanations which will occasionally refute input’s predicted label. This will have the effect of decreas-
itself —and decide if that is acceptable. ing completeness, but the alternative explanation will not
To continue with the example of case-based self-refutation, sup- self-refute (as the example shows).
pose a member of the research team proposed an alternative ex-
planation template that avoids refuting itself3 . To do so, we adjust
the static text and the calculation that fills in the variable parts, as design decisions is large enough that we cannot hope to evaluate
illustrated in Figure 3. Note that the new proposal only highlights them all with ExpS mechanisms.
counts on things that match, which has the effect of essentially Given this, we suspect many XAI design decisions are never
ignoring nearby training examples that do not match the input’s assessed, unless the XAI designers use ExpG—strictly due to the
predicted label. impracticality of doing ExpS at the scale needed for full coverage. In
The advantage of this proposal is that this alternative will not a sense, explanation is a user interface, and user interface designers
refute itself. But the advantage came at a cost: according to known have long used a wide variety of techniques relating to ExpG (e.g.
taxonomies, it brings a decrease in completeness, as the explanation design guidelines [21, 22], cognitive dimensions [9], cognitive walk-
is telling less of the “whole truth.” throughs [26], etc). Fortunately, there are some promising works
So did the overall ExpG go up or down? We think most would that demonstrate the use of these approaches (e.g., [3, 15, 23]).
argue down... but we cannot measure how much. This example
Hypothesis: Studying one (or a few) explanations is well-
exposes a critical weakness in the ExpG approach: the vocabulary
suited to ExpS oriented methods, but the template level may
and calculus currently available to us cannot adequately describe
require ExpG methods.
and measure the implications of a single design decision.
This suggests that, to create XAI systems according to rigorous
5.3 Scalability: Many design decisions are only science—especially XAI systems that generate explanations with
validated with ExpG a template—we must develop improvements in the rigor and mea-
surability of ExpG mechanisms. These will bring outsize benefit to
So where does this weakness leave us?
designers and researchers as compared to improvements in ExpS.
During a full design cycle, XAI designers face many design deci-
sions, of which only a few can be evaluated with ExpS. To illustrate,
6 ACKNOWLEDGMENTS
consider that case-based explanation as originally proposed by
Binns et al. [5] would be implemented by showing the single near- This work was supported by DARPA #N66001-17-2-4030. We would
est neighbor. That approach could be extended by showing the k like to acknowledge all our co-authors on the work cited here, with
nearest neighbors for a number of different k. Or, it could be im- specific highlights to Andrew Anderson, Alan Fern, Q. Vera Liao,
plemented by showing whatever neighbors lie within some feature Yunfeng Zhang, Rachel Bellamy, and Casey Dugan—research work
space volume—which is the approach used by Dodge et al. [8] and does not happen in a vacuum, nor do ideas.
illustrated in the left side of Figure 2. The space of these possible
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