Machine learning models are increasingly used for decisions that directly afect people's lives. These models are often opaque, meaning that the people afected cannot understand how or why the decision was made. However, according to the General Data Protection Regulation, decision subjects have the right to an explanation. Counterfactual explanations are a way to make machine learning models more transparent by showing how attributes need to be changed to get a diferent outcome. This type of explanation is considered easy to understand and human-friendly. To be used in real life, explanations must be practical, which means they must go beyond a purely theoretical framework. Research has focused on defining several objective functions to compute practical counterfactuals. However, it has not yet been tested whether people perceive the explanations as such in practice. To address this, we contribute by identifying properties that explanations must satisfy to be practical for human subjects. The properties are then used to evaluate the practicality of two counterfactual explanation methods (CARE and WachterCF) by conducting a user study. The results show that human subjects consider the explanations by CARE (a multi-objective approach) to be more practical than the WachterCF (baseline) explanations. We also show that the perception of explanations difers depending on the classification task by exploring multiple datasets.
Machine learning (ML) models are increasingly used for automated decision-making impacting
people’s lives [
https://github.com/ninaspreitzer (N. Spreitzer)
a given audience [
RQ: How practical are counterfactual explanations for human subjects? Q1 What properties must counterfactual explanations serve to be practical for human subjects? Q2 How do human subjects perceive counterfactual instances proposed by CARE compared to WachterCF? Q3 How does the perception of counterfactual explanations difer depending on the classification task? 1https://languages.oup.com/google-dictionary-en/ The remainder of this paper is structured as follows. In Section 2, we present related work, including an overview of proposed methods to compute counterfactual explanations and conducted user studies. Section 3 outlines the methodology we follow to explore our research questions. In Section 4 we specify desired properties for practical explanations for human subjects. Section 5 details the experimental setup of our user study, and Section 6 highlights the most important findings. Finally, we conclude the paper with a discussion by reflecting on the results and limitations, as well as outlining future work.
In this section, we first outline proposed methods for computing counterfactual explanations. Secondly, we elaborate on limitations of the use of counterfactual explanation methods in practice. This is followed by a discussion of related work addressing these limitations by introducing new objectives to achieve practicality. Finally, we discuss related user studies conducted to evaluate counterfactual explanations.
In recent years, many methods for generating counterfactual explanations have been proposed
[20]. A complete literature review of the proposed methods exceeds the scope of this paper;
Figure 1 provides an overview. We distinguish between approaches that are optimization-based
[21][22][19][
In their work, Barocas et al. [27] show that the computation of counterfactual explanations often
relies on widely overlooked assumptions that are necessary for counterfactual explanations to
be accepted in real life. It is assumed that the highlighted feature changes always correspond to
actual actions. The main problem with this is that the features are not independent, as the actions
are likely to afect other features simultaneously. The authors also state that most computed
counterfactual explanations only rely on the distribution of training data and overlook that not
everything that can be considered equivalent in the training data is automatically equivalent
for individual data subjects. In other words, specific suggestions can be practical for some
people but may not be practical for others. Laugel et al. [
Researchers have responded to this criticism by developing counterfactual frameworks with
diferent objective functions to satisfy specific properties [
Warren et al. [30] have examined how well users are able to predict model outcomes after looking at counterfactual explanations, and self-reported satisfaction and trust in the explanations. Our user study adds to their findings by assessing user perceptions of practicality properties grounded in social sciences [31]. Förster et al. [28] examined the coherency of counterfactual explanations by testing if users consider an instance as real (taken from the training data) or fake (computed counterfactual instance). Additionally, the users were asked if they thought the instance was suitable to explain the outcome. The classification task explored in their study was housing price prediction. The user study design that we present examines classification tasks that afect human decision subjects. Our work can contribute to better understand user perception of other desiderata and to what extent it depends on the classification task.
To answer the research questions, we first formalize our evaluation of practicality by defining a set of properties. In the user study, we evaluate the practicality properties by assessing how human subjects perceive counterfactual explanations generated by CARE compared to explanations generated by WachterCF. We explore this for two classification tasks using diferent datasets.
To determine what properties counterfactual explanations must satisfy to be practical, we review existing literature and examine the requirements of ML explanations. Based on that, we define a set of properties that make counterfactual explanations practical for humans. We focus on properties that afect how human subjects perceive counterfactual explanations. Thus, we examine the relation between explanation and user perception rather than the relation between explanation and model behavior (i.e. robustness). The defined properties serve as a benchmark for the practicality of generated counterfactuals.
The remaining sub-questions are answered by performing a user study with human subjects. The study aims to compare the perception of explanations provided by CARE with explanations provided by WachterCF. We use the Wilcoxon Sign Ranked test [32] to determine if the results are significantly diferent. The test is non-parametric, meaning the answers do not need to follow a normal distribution and tests the diference between paired samples.
To measure the practicality of counterfactual instances, we formulate questions mapped to practicality properties. We compare the methods indirectly, meaning we first evaluate explanations computed by CARE and then ask the same questions for explanations following WachterCF. The answers are given without direct comparison. Only one question at the end asks directly which method is preferred. This question helps to see if the personal opinion matches the findings.
Before conducting a user study, the target group to be studied must be defined. Since any individual could be afected by automated decision-making, we use convenience sampling [ 33] by collecting information from a conveniently available pool of respondents. This type of sampling is prone to bias, which will be discussed in Section 7. One distinction we make in the analysis is whether participants are familiar with machine learning methods because we believe there may be a potential diference in the perception of explanations depending on the prior knowledge of participants. Our objective was to gather enough responses to test for statistical significance. For this reason, we aimed for 100 respondents, preferably evenly distributed in terms of machine learning literacy.
To examine diferent classification tasks, we use two separate datasets. In the user study, the two scenarios were evenly distributed among the participants. Both datasets used can be accessed through the UCI machine learning repository [34]. The Adult Income dataset [35] is used to classify whether an individual is likely to have an income of more than 50k per year. The Student Performance dataset [36] is used for predicting what final grade an individual is likely to have for a school subject. For the user study, we simplify the classification task to predict if a student will pass or fail the subject. Both decisions are based on personal information about the individuals.
The preparation of the Adult dataset follows Zhu [37] by dropping columns that are not needed and grouping categorical values to end up with a more concise set of possible values. For the student dataset, we dropped columns to make the number of possible feature changes similar to the adult dataset to ensure a fair comparison. An overview of the final datasets can be found in the Appendix. For the remaining steps until the computation of the counterfactual explanation, we follow Rasouli et al. [19]. The numerical features of both datasets are standardized and categorical features are converted to ordinal encoding and are further one-hot encoded. The data sets are split into 80% training and 20% test set, and only the training set is used for creating the classification model. We follow Rasouli et al. [ 19] in the choice of model: a multi-layer neural network with two hidden layers, each including 50 neurons. The resulting model for the Adult Income classification task has an F1-score of 0.81. The model for the Student Performance dataset has an F1-score of 0.71.
With this setup, we create counterfactual instances using WachterCF and CARE. CARE provides the user with the possibility of defining constraints for actionability. We pre-define these constraints following Rasouli et al. [19] by setting gender and race as fix (immutable value) and age as ge (can only be greater or equal to the current value). Additionally, the CARE framework also allows the end-user to set the number of how many counterfactual explanations should be given for a single instance. We chose to set this number for five as we believe that five diferent suggestions is both comprehensible and noticeable diferent from providing only one counterfactual suggestion.
Considering that the recipient of the explanation is a human subject, it is crucial to make the
explanations human-friendly. In addition to other objective functions, the explanations must be
presented in a way such that people understand them [38], and the goodness of an explanation
depends on its audience [
Miller [31] summarizes human-friendly characteristics. Depending on the context, some features may be more important than others [38]. Concerning counterfactual explanations, we have defined the following set of properties that lead to practical explanations. This set is then used as a benchmark to evaluate how humans perceive counterfactual explanations in practice. • Contrastiveness: Humans are not interested in why an event happened but rather in why that event happened instead of another. In the context of counterfactual explanations, we measure contrastiveness by how well the user understands what needs to change to get the opposite outcome. • Selectivity: Generally, humans do not expect a complete cause of an event. Humans are used to selecting a smaller set of causes and treating it as a complete explanation. Therefore, counterfactual explanations can provide selectivity by changing only a subset of features as well as providing diferent suggestions. • Social: The explaining method is part of an interaction between the end-user and the system explaining. As a result, the social environment, the target audience, and the use case need to be considered. For counterfactual explanations, this means that the proposed changes should be made realistically in the given use case of the afected person. • Truthful: A human-friendly explanation needs to make sense. In other words, the user must perceive the counterfactual suggestions to reach the other result as plausible. • Consistent with prior beliefs: As described by the confirmation bias [ 39], people tend to ignore information that is inconsistent with their prior beliefs. Applied to counterfactual explanations, this means that end-users are more likely to consider explanations that suggest changes that are expected in advance.
The following section describes how the defined practicality properties are measured and how the user study is designed.
Table 1 provides an overview of our user study questions and the possible responses. We specify questions to measure how well the counterfactual instances satisfy the practicality properties. We map the questions shown to the properties as we believe they represent expectations of counterfactual explanations with respect to those properties. Question 1 is asked at the beginning, followed by Questions 2-8 are asked once for each counterfactual method. Finally, Question 9 which explanation is preferred. To make the scenario and questions more understandable, we call the person represented by the input data Charlie. 2 How surprised are you with the suggested changes in attributes to get the outcome of “earning above 50k” / “passing the course”? 3 How well does the method explain to you what Charlie needs to change to get “earning above 50k” / “passing the course”? 4 Based on the explanation, what attribute(s) would you consider as most important to change the model outcome? 5 In your opinion, the amount of five diferent suggestions / one suggestion is ___ to explain the model outcome. 6 In your opinion, the variation of attributes in the suggestion(s) is ___ to explain the model outcome. 7 Do you think Charlie could realistically act upon the suggestions to change the model outcome to “earning above 50k” / “passing the course”? 8 Do you think the suggestions make sense in order to retrieve the model outcome to “earning above 50k” / “passing the course”? 9 Which method would you prefer as an explanation for the outcome of the ML model? Measurement Multiple Choice: List of features Likert Scale: 1. Not at all 7. Very surprised Likert Scale: 1. Not at all 7. Very well Multiple Choice: List of features Single Choice: Too little/ Enough/ Too many Single Choice: Too little/ Enough/ Too much Likert Scale: 1. Not at all 7. Fully Likert Scale: 1. Not at all 7. A lot Single Choice: Method A Method B
Property Consistency prior beliefs Consistency prior beliefs Contrastiveness Consistency prior beliefs Selectivity Selectivity Social Truthful
The questionnaire is designed using the survey tool Qualtrics2 and send out digitally. The study starts by introducing ML models, how they are used for automated decision-making, and how counterfactual instances help to provide explanations. Next, the scenario and the datasets are explained, including an illustrative counterfactual instance, similar to Figure ??. Then, the participants are asked what properties they expect to change to retrieve the other outcome. This is followed by showing the first counterfactual explanation, which is computed by CARE. The participants are asked to answer questions 2-8 concerning the CARE explanation. After answering the questions, they are shown the second explanation computed by WachterCF and are asked the same questions. Finally, the last question asks which method is preferred and gives an opportunity to leave a comment. Figure 2 illustrates how the first explanation calculated by CARE is shown to the participants. We considered several options for the order of showing explanations from the two diferent methods. The final user study shows each participant the CARE explanations first followed by the WachterCF explanation. Another option would be to randomize the order of explanation methods shown between participants. The second option has the advantage of reducing bias introduced by the order. We chose the fixed order (CARE followed by WachterCF) with the reasoning that our focus is on evaluating CARE (a method intended for practicality) against the baseline WachterCF method. By showing CARE ifrst we ensure that CARE is evaluated a priori without the influence of another counterfactual explanation. If CARE is not perceived to be more practical than the WachterCF explanation shown second should obtain similar responses.
The instance shown to participants refers to individuals who initially received a negative prediction (less than 50k per year or a negative final grade) and are given a counterfactual instance as an explanation. We randomly select twenty instances from the Adult Income test data and ten instances from the Student Performance test data. The number difers because the Adult Income dataset contains more instances than the Student Performance dataset. The instances are evenly distributed among users and randomly assigned. Furthermore, counterfactual explanations are presented in a visually appealing manner, since we want participants to focus on the content of the explanation.
We have collected 135 responses. 69 participants received the Adult Income dataset, and 66 received the Student Performance dataset. Additionally, 70 responses indicate that they are familiar with ML models, while the remaining 65 imply not being that familiar with ML models. We analyze the results the following way. First, we compare the responses of explanations computed by CARE compared to the ones computed by WachterCF. These results are used primarily to answer the second sub-question. Secondly, we examine the responses for both datasets separately to answer our last sub-question if the perception difers between diferent classification tasks. Finally, we investigate whether there is a diference in responses among the respondents that are or are not familiar with machine learning models.
We start by analyzing all questions that include Likert scales (Questions 2, 3, 7 & 8) and singlechoice answers (Questions 5 & 6). Then we examine questions that ask for selecting features (Questions 1 & 4), followed by looking into the participants’ preference of the two methods (Question 9).
Figure 3 shows a stacked bar chart for Questions 2,3,5,6, and 7, comparing the responses of WachterCF and CARE. Red colors represent negative responses, yellow colors represent neutral responses, and green colors represent positive responses. Looking at the graphs, it is noticeable that the counterfactual explanations calculated with WachterCF received more negative responses than those calculated with CARE. We use a Wilcoxon Sign Ranked test to evaluate the significance of this result. This test is chosen as we assess the diference in answers of matched samples. The null hypothesis states that the mean is the same. We aim to reject this hypothesis by showing that the means of the responses difer significantly. The resulting P values are all smaller than 0.01 and thus confirm that the responses for WachterCF are significantly diferent from the responses for CARE. From this, we can conclude that the counterfactual instances computed by CARE are considered more practical. Taking those results into account, we map back to the defined set of practicality properties.
• Consistent with prior beliefs: Question 2 assesses if the explanations are consistent with the participant prior beliefs by asking if the explanation is surprising to them. The results show that CARE provides less surprising explanations than WachterCF. • Contrastiveness: Question 3 shows that suggestions by CARE provide a better explanation to the user of what needs to be changed to get the diferent model outcome, which serves as an estimation for being contrasting. • Selectivity: Questions 5 & 6 aim to evaluate the selectivity by asking participants to assess the number of suggestions and variation of features. CARE is rated to select a better subset of features than WachterCF. • Social: Question 7 evaluates a social perspective, showing that CARE serves more realistic suggestions considering the specific use case. • Truthful: Question 8 shows that suggestions by CARE are perceived to make more sense than suggestions by WachterCF, which maps to being truthful.
Question 1 indicates the prior beliefs of the participants by asking what features they expect to change to retrieve the desired outcome before seeing any explanation. Question 4 elaborates on what features are considered important after seeing counterfactual explanations. We analyze the responses by computing a percentage of agreement, which shows how many of the features considered as most important after seeing the explanation were also selected in Question 1. For example, if Age and Working Hours are considered as the most important features, but only Age is chosen to be expected in Question 1, the percentage of agreement is 50%. By comparing the overall percentage of agreement of WachterCF and CARE, we can see that explanations provided by CARE seem to show a higher percentage of agreements than ones by WachterCF. However, according to the Wilcoxon Sign Ranked test we do not have enough evidence to show a significant diference for this comparison.
Question 9 directly asks the participants what method is preferred as an explanation. Out of the 135 answers, 113 selected CARE over WachterCF, and only 22 choose WachterCF as the preferred method. Therefore, we can conclude that humans subjectively prefer CARE over WachterCF.
The responses show that the perception of WachterCF and CARE difers depending on the classification task. To further assess whether this diference influences the obtained results, we perform the same analysis as in section 6.1 but split the responses according to the classification task assessed by the users. To test for significance between the responses to the diferent methods we use again a Wilcoxon Sign Ranked test. The Student Performance data shows a similar pattern as the overall results shown in Figure 3. CARE is seen as more practical compared to WachterCF and all diferences are statistically significant. On the contrary, the responses to the Adult Income dataset do not show the same results. Figure 4 shows the diferent responses to the two counterfactual explanations for instances from the Adult Income dataset. Only Questions 5 & 6 show a significant diference in answers, which ask to indicate the perception regarding the practicality property Selectivity. All other questions do not difer significantly. Question 7 even shows a slight tendency in favor to WachterCF. We propose three reasons for this. First, when looking at the actual explanations, WachterCF often only changes the attribute Age in both datasets. If so, Age decreases for the Student Performance dataset, but increases for the Adult Income dataset. This shows that a higher age leads to the outcome of earning more than 50k per year. Decreasing the age is not an actionable explanation, but getting older is happening without any active changes. Therefore, seeing this as an explanation gives satisfaction to the user. On the other hand, the explanations for the Student Performance dataset show that WachterCF also includes decreasing age as it does not include any preference settings, which leads to impracticality. Another reason might be that the student performance dataset contains more attributes to be modified than the adult income dataset. More attributes may lead to more complexity in computing practical counterfactual explanations. Another reason is that the decision of the Student Performance dataset is about whether a student is likely to pass or fail a class, which is a high-impact decision. In contrast, the purpose of the Adult Income is to decide whether a person is likely to earn more or less than 50k per year, which is not a critical decision. Therefore, participants may be more demanding regarding the practicality of the explanations when students’ lives are directly afected.
Lastly, we examine whether there is a diference in responses when comparing participants who have indicated to be familiar with machine learning models to those who are not. To test this, we divide the responses for each question into two groups indicating whether the user is familiar with machine learning models or not. To test if the diferences are significant we use the Mann-Whitney U because the answers come from independent samples. Of the 15 questions (seven quantitative questions for each method and one comparison question), only Question 7 shown for the WachterCF explanation difers significantly ( = 0.002 ), which asks whether the suggestion can be realistically implemented. Participants with a technical background responded more positively than those with a non-technical background. One possible reason is that Question 7 ofers a relatively large leeway for interpretation compared to other questions. Participants with a technical background might better understand what we are trying to address with this question. In contrast, people with a non-technical background might interpret the question diferently.
In this section, we discuss the limitations and weaknesses of the methodological design and reflect on the results of the user study transitioning to possible future work.
One limitation of the user study is that the user study participants were selected through convenience sampling [33]. This type of sampling is prone to bias: One possible bias in the target group is that participants are most likely to be highly educated. This could lead to a judgment that may not represent other social groups. Furthermore, the user study design does not represent a realistic scenario. Participants judge cases of people they do not know and to whom they have no emotional attachment. If the participants or people close to them would be afected by model outcomes, the expectation of explanations may be higher. Another aspect of this is that the datasets have been preprocessed before conducting the user study. This may not be the case in a real-life scenario; therefore, the quality of counterfactual explanations could sufer.
This paper shows that the responses to the majority of the questions difer depending on the scenario the counterfactual instance is used for. This could indicate that the perception of explanations difers according to the context of the ML decision. An explanation might be considered practical for a specific use case but impractical for a diferent one by the same user. This may indicate that the practicality of counterfactual explanation depends not only on the counterfactual method, but also on the type of ML task (e.g. the decision), the data used to decide, and the complexity of this decision (e.g. how many features are used to decide). Further research is necessary to determine what conclusions can be drawn about this.
We gain insights that counterfactual explanations with the more recent method are overall perceived as more practical than with the original method, but also that responses difer depending on the use case of the decision. Future research should examine how the perception difers depending on diferent types of decisions and the number of features used. In addition, it is important to see how the results would have changed when users themselves are afected by the decision rather than evaluating an explanation for a stranger. Another area to explore further is how user perceptions of diferent explanation methods compare, such as feature attribution methods (like LIME [40]) or causal explanations [30].
People have a right to explanation when afected by a ML decision. This helps them to understand better why and how the model made this particular decision. Counterfactual explanations are a way to provide transparency by showing which attributes must change in what way to achieve a diferent outcome. Research has focused on developing various frameworks for computing counterfactuals. However, counterfactual explanations must be practical to be used by human subjects in practice. To answer our research question about the practicality of two counterfactual methods, we first define the following properties to measure practicality: Contrastiveness, Selectivity, Social, Truthful, and Consistent with prior beliefs of the user. To test how people perceive the explanations, we conduct a user study to compare CARE (a more recent multi-objective approach) against the original method WachterCF (as a baseline) for two diferent scenarios. The overall responses show that people perceive explanations computed with CARE as significantly more practical than those computed with WachterCF. Furthermore, we find our results difered between the Adult Income dataset and the Student Performance dataset, which indicates that the perception might difer depending on the use case of the explanation.
We are grateful to the participants of the user study. And we thank Emma Beauxis-Aussalet (Vrije Universiteit Amsterdam) for the thoughtful feedback which improved the findings of this paper.
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Column Age Working Hours Gender Race 3. Please note that this racial distinction is taken from previous research and was chosen for simplicity.
We are aware that it does not properly reflect all ethnicities. Gender Discrete