A practical prototyping tool to design explainable AI for non-technical end-users.
|End-User-Friendly Explanatory Forms|
|• Feature-based explanation|
|• Example-based explanation|
|• Rule-based explanation|
|• Contextual information|
Even for the same user and task, end-users’ needs for explanation, i.e.: the trigger point or motivation to check the explanation of an AI system, may vary from time to time based on different contexts or usage scenarios. We summarized ten potential explanation goals from prior works 1 as follows:
Calibrate trust: trust is a key to establish human-AI decision-making partnership. Since users can easily distrust or overtrust AI, it is important to calibrate the trust to reflect the capabilities of AI systems 2 3.
Ensure safety: users need to ensure safety of the decision consequences.
Detect bias: users need to ensure the decision is impartial and unbiased.
Resolve disagreement with AI: the AI prediction is unexpected and there are disagreements between users and AI.
Expected: the AI’s prediction is expected and aligns with users’ expectations.
Differentiate similar instances: due to the consequences of wrong decisions, users sometimes need to discern similar instances or outcomes. For example, a doctor differentiates whether the diagnosis is a benign or malignant tumor.
Learn: users need to gain knowledge, improve their problem-solving skills, and discover new knowledge
Improve: users seek causal factors to control and improve the predicted outcome.
Communicate with stakeholders: many critical decision-making processes involve multiple stakeholders, and users need to discuss the decision with them.
Generate reports: users need to utilize the explanations to perform particular tasks such as report production. For example, a radiologist generates a medical report on a patient’s X-ray image.
Trade-off multiple objectives: AI may be optimized on an incomplete objective while the users seek to fulfill multiple objectives in real-world applications. For example, a doctor needs to ensure a treatment plan is effective as well as has acceptable patient adherence. Ethical and legal requirements may also be included as objectives.
The following are fine-grained details from our user study findings on end-users’ requirements in different explanation goal scenarios.
The process of calibrating trust involves multiple factors and their complicated interactions. We summarize the following key emerged themes participants requested to calibrate their trust toward AI.
Performance Trust towards AI is fundamental to incorporate AI’s opinion into the critical decision-making process, and many explanation goals below are built on trust.
Since end-users usually do not have complete computational and domain knowledge to judge AI’s decision process, model performance becomes an important surrogate to establish trust.
“Even if AI tells me how it reaches its decision, I cannot judge whether it’s correct since it is a medical analysis and requires professional medical knowledge. I just know the accuracy and that’ll be fine.”
Prior work identified two types of performance: stated and observed performance 4, and they were both mentioned in the interview. Stated performance or accuracy is performance metrics tested on previous hold-out test data, and it was mentioned by most participants as a requirement to build model trust.
“I understand maybe AI is learning from past examples, and it may be finding patterns in the data that might not be easy to explain. So I’m less concerned about how it’s getting there. I think I do have a trust that is doing it right, as long as there’s something you can test after how accurate it’s been. “
Compared to assessing the performance metrics, some participants tended to test AI by themselves and get hands-on observed performance to be convinced. This requires users to have a referred ground truth from their own judgment or reliable external sources.
“(My own test driving experience) is way more useful than watching a (test driving) video, because you shouldn’t trust everything. The video might be just made to wait for you to buy the car. So talking from a customer perspective, I would like to try it myself, because I also sell things. So I would always like to try it myself instead of watching a video.”
Feature The important features that AI was based on are the next frequently mentioned information.
“I would like to know the list of criteria that the AI chose the price based on, and which one weighs more.”
The ability to discriminate similar instances This information was requested by several participants to demonstrate AI’s capability.
”(Decision tree) It’s showing me that it’s picking from a few similar ones, not just like a random ray of blue, purple, green birds. It’s not random, it’s a calculated response. More of that would help me trust AI.”
“Typical example seems to be pretty good at picking up on differences. Similar example I can see that it’s got a good variety of similar birds. So I found these ones make me trust it more.
Dataset The dataset size that AI was trained on is another surrogate mentioned by some participants to enhance trust.
“To me what artificial intelligence does is just collecting a lot of data, and tries to make sense for behavioral patterns. So I would actually trust it, because I think it’s just based on data, it is a more accurate measurement of what market rate is for house prices.”
“If I know that the AI comes from a large database, it seems like the database is actually the experience that AI has. So the larger it (dataset) gets, the more experienced AI would be, so I can trust it more.”
External information This is another surrogate mentioned by participants to judge if AI is trustworthy. The external information could include:
Peer reviews, endorsement, and company credit.
“Since I’m not really a tech person, so I’m not sure how I look at it in a technical way. So that’s why I just really depend on the company’s reputation, and also how people feel about the website.”
Authority approval and liability.
“I trust more if the government themselves kind of stands behind it, getting some sort of government approval helps it a little bit more. So if there’s some health authority like Health Canada or FDA support gives it more legitimacy.”
“For me personally, I would prefer if an actual person is there in the end, at least in the beginning stage. So if somebody is there to just say, ‘hi, I’m so and so’, and then AI takes control. Then we still know that there is somebody who’s liable in the end for whatever happens.’’
In our user study, the top three most selected forms for the need to calibrate trust were performance (20/40), output (20/40), and feature attribute (17/40).
To ensure safety and reliability of the AI system in critical tasks (the autonomous driving vehicle task in our study), participants frequently mentioned checking AI’s performances in test cases, expecting the testing to cover a variety of scenarios to show the robustness of safety. Although it is impossible to enumerate a complete list of potential failure cases in testing, extreme cases or potential accidents were the main concerns and focuses of end-users.
“Potential crashes or just like someone speeding or a pedestrian jumping out of nowhere.”
“There is likely to be someone running around, so it needs to show me the extreme cases. ...I need to see something like FMEA, failure modes and effects analysis, just to be like, ‘okay this is how it works.’ because I know nothing is foolproof. There are always to be something, but to what extent.”
Similar to the need to calibrate trust, alongside the above stated performance, a few participants required observed performance to emotionally accept AI as an emerging technology.
“definitely I would want to be in one car. I think information is not helpful, it’s not an intellectual factual thing, it’s emotionally not acceptable. It (AI) is new and I have to learn to trust it.”
Regarding the specified information to present in the performance testing, participants would like to check the objects detected by AI (feature attribute, 9/13):
“It shows how it detects the important objects and how it makes decision”
“See if (the feature attributes) align with my own judgment of feature importance.”
Performance (6/13) were also favourable to check the metrics summary of performance. A specified performance analysis in different test scenarios may also help as a safety alert by revealing the weakness of the system.
“Let’s say I’m driving on a rainy day, then I know that I should be a lot more careful than when I’m with the car in a normal condition.”
Similar example (7/13) were preferred since it showed “what’s the condition or what kind of decision the car gonna make”, although participants did not focus on its similarity nature, but rather assumed it can showcase a variety of cases including the extreme cases. Several participants chose decision tree (6/13) because it “gave me an overview of how the car makes decision”.
Participants were concerned about population bias 5, or distribution shift where AI models are applied to a different population other than the training dataset. Such concern is more prominent when a prediction is based on users’ own personal data, and when users are in minority subgroups. Participants wanted to compare and see if their own subgroup is included in the training data.
“I know I’m in a class, they talked about how a lot of studies haven’t been done specifically on women, even though they (diabetes) affect men and women differently. That is probably something I would want to know about, like if it gave me this result and then it had a little note that explained the research was done more on that demographic, so it may be more true for that demographic, but they’re just trying to, what’s the word, extrapolate to this group where I sit.”
Unlike the common bias and fairness problem in AI where the protected features should not affect the prediction, in our Health task on diabetes prediction, the protected features (age, gender, ethnicity) do lead to a difference in diabetes outcome (referred as explainable discrimination in 5). Participants who were aware of this point required AI to account for such differences among subgroups.
“I know some ethnic groups just by genetic makeup could be more predisposed to diabetes. In order for it (AI) to arrive at this decision, I would think that it has maybe like a sample size of different people with different ethnicities to try to figure out. I would think there’ll be years and years of research has already been done of the different groups, different ages that would then be factored in by AI. If I can see it (AI) is using that information, I’ll be a lot more comfortable to actually using the AI’s recommendation.”
In cases where the AI task is not related to personal information (in our study the self-driving car task), participants required AI to be able to detect objects and perform equally in all potential biased conditions.
“Now we are operating in night time, or different weather, but they (the self-driving cars) still have to be able to see the signs and identify the objects.”
A fine-grained performance (12/24) analysis based on protected-feature-defined subgroups 5 can help users to identify potential biases.
“I would want to see the certainty and what the prediction error can potentially be for my demographic versus other groups. If it (the prediction error) is quite low, then I would probably worry less about that.”
Participants chose similar + typical example (12/24, means out of the 24 card-selection responses on Bias, 12 selected either similar or typical example) to help inspect the data and model, and to compare with other similar instances to confirm their subgroup is included in the model.
“You would want to know what the data that it’s being drawn from, is it similar to you?”
Feature attribute (12/24) was also chosen since participants wanted to check if AI could still detection important features in minority conditions.
“I want to see how well AI is performing at night to see what it detected.”
When AI’s predictions did not align with participants’ own expectations, most participants would “question AI” and “the contradiction may let me confuse”. Some may lose trust with AI thus would not go further to check its explanations, if they were confident about their own judgment. Some would check “a trusted second opinion”, or refer to human experts.
But for the majority of participants, explanations were needed “to know why” and to resolve conflicts.
“I’m feeling conflicted because it’s giving me two different information, my own personal belief and AI. So in order to convince me that AI does know what it’s talking about, you need to go through the mental validation step [pointing to the ranked explanatory cards]. So by the time I go through this (explanatory cards) and I come out of it, I am extremely convinced.”
Explanations help to identify AI’s flaws and reject AI, or to check the detailed differences and to be convinced and correct user’s own judgment, although “it might be harder to persuade me”. Specifically, participants “try to understand what makes a difference (between AI and my prediction)”, which is similar to the need to differentiate. To show why the predictions are different, many participants required a list of key features.
“Because AI cannot think like a human, so the reason that I ask for the criteria list is trying to think how similar to me is AI’s thinking. So maybe AI is thinking better, or is seeing a wider range, so it’s checking things that I’ve never thought about.”
In case AI made errors, seeing what AI is based on can facilitate user’s “debugging’’ process. Although end-users cannot debug the algorithmic part, they may be able to debug the input to see if AI “have the complete information” as users have. Furthermore, if some key input information is lacking in AI’s decision, the system needs to allow users to provide feedback by inputting more information, or
“correct the error” for AI.
Feature attribute: 28/61
similar example: 25/61
decision tree: 23/61
In contrast, when the prediction matched participants’ expectations, participants “will trust the AI more” , and the motivation to check explanations was “not as strong as the previous one (unexpected purpose)”. Some participants stopped at the prediction, willing to accept the “black-box’’ AI and may “not even waste my time (checking explanations)”.
A few participants still wanted to check further explanations for the following motivations:
To boost user’s confident.
“Even in this (expected) scenario, it would be nice to have some bullet points, like the reasons behind it the estimation being accurate, because if someone says that you’re charging me way too much, I can have point by point reasons explaining to you why this house worth this price, it actually kind of as a confidence boost to think you are not overcharging or undercharging.”
“If diabetes already runs in my family, (and AI predicts my risk of diabetes is 80%), it would probably make me more confident about the software. So I might want to ask for more information about which aspects of my health records were the most important for making this decision? Coz then maybe that can help me with my future activities and changing things in the future.”
Feature attribute: 13/34
similar example: 12/34
typical example: 10/34
To facilitate end-users’ need to differentiate similar instances, AI is required to first have the ability to discern similar instances.
“Depends on how good it is...So I think you would have to improve how AI picks up the birds, like maybe these are the same color birds, but maybe they have slightly different characteristics. So if AI can pick that up, then I think it would be better.”
In case of doubtful prediction, participants expected AI to indicate how certain it is to the prediction.
“I would expect AI if it doesn’t know, it would give choices. So it would say 100% or 99% that’s an indigo bunting, and 89% it thinks it’s a finch.”
Based on that, AI needs to be able to “pinpoint unique features that made them really different from each other”. In addition, the interface may also need to support users’ own comparison.
“AI can tell you what the differences are. I guess it could be some list of the beak is longer for this and that. But I think visually bringing the differences up side by side, and then I can directly compare what the differences are.”
Rule (12/14) and counterfactual example (10/14) were the most preferable forms. Participants chose rule since “you could write that you differentiated the bird’s tail were long or short, or beak thin or thick”. The counterfactual examples
“identify where specifically to look”, and “describe the change, the progress” .
Using AI for user’s personal learning, improving problem solving skills, and knowledge discovery, “depends on how reliable it (AI) really is”. And participants expected AI to “receive human feedback to correct its error and improve itself”.
To facilitate learning and knowledge discovery, “just looking at (input) pictures and (output) names isn’t enough”, and participants expected a wide range of explanations depending on the particular learning goal, such as “more details to systematically learn, go over that same bird, ...a mind map to build a category of birds by one feature” ,“the specific characteristic about this bird, and how can I differentiate this bird from other birds”. Other learning features mentioned by participants include: referring to external “respectable source”, supporting personalized learning for unfamiliar terms, and “collecting information about how well I’m doing on it, like if I guess wrong, does it record that? to see if I’m progressing”.
Rule-based explanations (rule: 12/14, decision flow chart: 10/14, decision tree: 8/14) were more favourable for the need to learn, since they showed “a learning process. It has like how you could recognize a bird. So help me to learn some new knowledge”. Same as in Report, participants would prefer to see
“the graphics and text combined”: “It combines text and pictures, and they are relevant to each other. It’s kind of a multi-modal learning”.
Participants intuitively seek explanations to improve the predicted outcome, when predictions are related to personal data (in our study, the House and Health tasks). However, they tended to unwarrantedly assume the explanations were causal (causal illusion 6, i.e., believe there is a causal connection between the breakdown factors and the outcome), even though the cause-effect relationship has not been confirmed, and AI largely relies on correlation for prediction 7. Only a few participants required more solid evidence to support the explanations on improving the predicted outcome, especially when the action was related to critical consequences (personal health outcomes).
“I presume the recommendation (on improvement actions from AI) is also has been backed up by Health Canada, because I think I would tend to follow the recommendations if I know there’s definitely medical support behind it.”
“I would definitely want to know like what can I do to mitigate those risk factors or to address those things so that I can decrease the risk. I would really like to know if it had an explanation of how reliable each source was. Coz I know some studies, they might seem like a correlation, but it doesn’t mean it’s a direct cause. So I would really love it if it could potentially explain how powerful those studies are suggesting.”
Regarding the specific requirements on the explanations for improvement, participants were looking for controllable features and ignoring the features that cannot be changed.
“I can not change my age, but I’m able to reduce my weight”
Knowing the controllable features has a positive psychological effect to give users a sense of control, and vice versa.
“If I’m afraid of getting diabetes, and assume that I’m going to sentence, it feels like there’s nothing I can do about it. But when I see this one (feature attribute), I think, ‘oh geez, maybe there are other factors here that I can do something about.’ So this may make me more positive about doing something about my condition.”
“I know it (feature interaction) is comparing my house area and my number of rooms with other houses. I can understand ‘okay if I increase my room number, the price will be increased that much.’ But the problem is I cannot change any of them (the house features). It just gives me the feeling of disappointment.”
To counterpoise the unchangeable features, users may intuitively apply counterfactual reasoning to compare different feature adjustment settings.
“If I make any change in my house appliance and renew, then I can still reach the same price as if my house was bigger”
Counterfactual example (18/26) and feature shape (13/26) were the top two selected forms. While counterfactual example provides how to achieve the target outcome change by adjusting the input features (counterfactual reasoning), feature shape and feature interaction allow users to adjust features and see how that leads to outcome change.
To communicate with other stakeholders, some participants chose to communicate verbally about their opinions without mentioning AI. Others preferred to present stakeholders with more evidence by bringing AI’s additional information explicitly to the discussion. For the latter case, the other stakeholders need to establish basic understanding and trust towards AI before discussing AI’s explanations.
“I’d sit down and get my family together and explain about the artificial intelligence thing.”
“I would try to get some evidence from it (AI) that I could take to the doctor to get them to buy into it.”
To do so, most participants chose to present AI’s performance information to build trust.
“As long as the backstage is accurate and then I can just provide accuracy to my wife and she’ll be able to get that. Trustworthy is the most fundamental.”
Different audiences and explanation goals of communication may require distinct explanations, as described by one participant:
“I’m pretty sure my husband or my mother has a different way to decide or they want to know different things.”
In addition, in the Health task, we asked participants to communicate with family members or doctors about their diabetes predictions. Since the requested explanation covered a wide range of contents, we did not identify any distinct differences in the communicating contents between the two audiences.
A formal summary or report from AI may facilitate the communication with other stakeholders, as requested by many participants.
“A written report from AI that I would be able to reference to, in order to talk to my family about that. It would feel a little bit more official rather than just, ‘oh, this is what somebody said’, there’s no real evidence, whereas this sort of creates that paper trail.’’
While output (21/46) and performance (17/46) provide AI’s result and help to build trust, feature attribute (27/46) and decision tree (17/46) show the breakdown factors and internal logic behind the prediction.
The content of reports may largely depend on the specific purpose and readers of the report. In our study, participants frequently mentioned the report should include “key identifying features”, “list of distinguishing characteristics or what makes it unique” , or “a summary of factors that were part of the input led to the diabetic prediction”. Users also mentioned including supporting information to back up the decisions, such as the training dataset size of the predicted class, and the decision certainty level .
Rule(12/14), decision flow chart(7/14), and feature attribute(5/14) are the most frequently selected explanation forms.
Rule descriptions can conveniently generate text reports.
“I have to write the explanation”
“You can not only by looking at the images and get some explanation. You need some more specific description.”
In addition, adding image to the text “would be complementary” to each other, and the format of image + text were more favourable by many participants.
“Rule is just describing and writing. It doesn’t really show you a visual on how to compare them.”
“Feature attribute and decision flow chart (presented in image format on bird recognition task) highlights what rule is saying, this knowledge complements your statement.”
Usually it is the human user rather than AI to trade-off among multiple objectives in AI-assisted decision-making tasks. Thus when multiple objectives get conflicted (in our study, they are scenarios when car drives autonomously and passenger gets a car sick; and AI predicts diabetes and uses it to determine insurance premium), AI was required to allow users to take over or to receive users’ inputs.
“It’s the most important thing I would want to do is to allow me to stop, or asking to slow down if I’m feeling sick.”
Explanations are required if the multiple objectives conflict and need to trade-off. And users could use such explanations to defend for or against certain objectives.
“I think it’s like a defensive thing, like if I’m expecting that they’re going to cause an increase in my payments or whatever they’re going to deny me (health insurance) coverage, I would be trying to find out what it’s based on for the opposite reason maybe to discredit it.”
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making ↩
Understanding the Effect of Accuracy on Trust in Machine Learning Models ↩
Illusions of causality: how they bias our everyday thinking and how they could be reduced ↩