02/26/2020 – Bipasha Banerjee – Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment

February 25, 2020 bipashab 1 Comment

Summary

The paper highlights one of the major problems that the current digital world faces, algorithmic bias, and fairness in AI. They point out that often ML models are trained on data that in itself is bias and, therefore, may result in amplification of the existing bias. This often results in people not trusting AI models. This is a good step towards explainable AI and making models more transparent to the user. The authors used a dataset that is used for predicting the risk of re-offending, and the dataset is known to have a racial bias. Global and local explanations were taken into account across four types of explanations styles, namely, demographic-based, sensitivity based, influence based, and case-based. For measuring fairness, they considered racial discrimination and tried to measure case-specific impact. Cognition and an individuals’ prior perception of fairness of algorithms were considered as measures of individual difference factors. Both qualitative and quantitative methods were taken into account during the evaluation. They concluded that a general solution is not possible but depends on the user profile and fairness issues.

Reflection

The paper by Dodge et al. is a commendable effort towards making algorithms and their processing more clear to humans. They take into account not only the algorithmic fairness but also the humans’ perception of the algorithm, various fairness problems, and individual differences in their experiments. The paper was an interesting read, but a better display of results would make it easier for the readers to comprehend.

In the model fairness section, they are considering fairness in terms of racial discrimination. Later in the paper, they do mention that the re-offending prediction classifier has features such as age included. Additionally, features like gender might play an important role too. It would be interesting to see how age and other features as a fairness issue perform on maybe other datasets where such biases are dominant.

The authors mentioned that a general solution is not possible to be developed. However, is it possible for the solution to be domain-specific? For example, if we change the dataset to include other features for fairness, we should be able to plug in the new data without having to change the model.

The study was done using crowd workers and not domain experts who are well knowledgeable with the jargon and are used to being unbiased. Humans are prone to be biased with/without intentions. However, people who are in the legal paradigm like judges, attorneys, paralegals, court reporters, law enforcement officers are more likely to be impartial because either they are under oath or years’ of practice and training in an unbiased setup. So, including them in the evaluation and utilizing them as expert crowd workers might yield better results.

Questions

A general solution for a domain rather than one size fits all?
Only racial discrimination is considered as a fairness issue. Are other factors only used as a feature to the classifier? How would the model perform on a varied dataset with other features like gender as a fairness issue?
The authors have used the dataset for the judicial system, and they mentioned their goal was not to study the users. I am curious to know how they anonymize the data, and how was the privacy and security of individuals handled here?

02/26/20 – Fanglan Chen – Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment

February 25, 2020 Fanglan Chen 1 Comment

Summary

Dodge et al.’s paper “Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment” presents an empirical study on how people make fairness judgments of machine learning systems and how different styles of explanation impact their judgments. Fairness issues of ML systems attract research interests during recent years. Mitigating the unfairness in ML systems is challenging, which requires the good cooperation of developers, users, and the general public. The researchers state that how explanations are constructed have an impact on users’ confidence in the systems. To further examine the potential impacts on people’s fairness judgments of ML systems, they conduct empirical experiments involving crowdsourcing workers on four types of programmatically generated explanations (influence, demographic-based, sensitivity, and case-based). Their key findings include: 1) some explanations are considered more fair, while others have negative impact on users’ trust of the algorithm in regards of fairness; 2) varied fairness issues (model-wide fairness and case-specific fairness) can be detected more effectively through an examination of different explanation styles; 3) individual differences (prior positions and judgment criteria of algorithmic fairness) lead to how users react to different styles of explanation.

Reflection

This paper shines light on a very important fact that bias in ML systems can be detected and mitigated. There is a growing attention to the fairness issues in AI-powered technologies in the machine learning research community. Since ML algorithms are widely used to speed up the decision making process in a variety of domains, beyond achieving good performance, they are expected to produce neutral results. There is no denying the fact that algorithms rely on data, “garbage in, garbage out.” Hence, it is incumbent to feed the unbiased data to these systems upon developers in the first place. In many real-world cases, race is actually not used as an input, however, it correlates to other factors that make predictions biased. That case is not as easy as the cases presented in the paper to detect but still requires effort to be corrected. A question here would be in order to counteract this implicit bias, should race be considered and used to calibrate the relative importance of other factors?

Besides the bias introduced by data input, there are other factors that need to be taken into consideration to deal with the fairness issues in ML systems. Firstly, machine bias can never be neglected. The term bias in the context of the high-stakes tasks (e.g. future criminal prediction) is very important because a false positive decision could have a destructive impact on a person’s life. This is why when an AI system deals with the human subject (in this case human life), the system must be highly precise and accurate and ideally provide reasonable explanation. Making a person’s life harder to live in a society or impacting badly a person’s life due to a flawed computer model is never acceptable. Secondly, the proprietary model is another concern. One thing should be kept in mind that many high-stacks tasks such as future criminal prediction is a matter of public matter and should be transparent and fair. That does not mean that the ML systems used for those tasks need to be completely public and open. However, I believe there should be a regulatory board of experts who can verify and validate the ML systems. More specifically, the experts can verify and validate the risk factors used in a system so that the factors could be widely accepted. They can also verify and validate the algorithmic techniques used in a system so that the system incorporates less bias.

Discussion

I think the following questions are worthy of further discussion.

Besides model unfairness and case-specific disparate impact, are there any other fairness issues?
What are the benefits and drawbacks of global and local explanations in supporting fairness judgment of AI systems?
Are there any other style or element of explanations that may impact fairness judgement you can think about?
If an AI system is not any better than untrained users at predicting recidivism in a fair and accurate way, why do we need the system?

02/26/2020 – Palakh Mignonne Jude – Explaining Models: An Empirical Study Of How Explanations Impact Fairness Judgment

February 25, 2020 Palakh Mignonne Jude 1 Comment

SUMMARY

The authors of this paper attempt to study the effect explanations of ML systems have in case of fairness judgement. This work attempts to include multiple aspects and heterogeneous standards in making the fairness judgements that go beyond the evaluation of features. In order to perform this task, they utilize four programmatically generated explanations and conduct a study involving over 160 MTurk workers. They consider the impact caused by different explanation styles – global (influence and demographic-based) as well as local (sensitivity and case-based) explanations, fairness issues including model unfairness and case-specific disparate impact, and the impact of individual difference factors such as cognitive style and prior position. They authors utilized the publicly available COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) data set for predicting risk of recidivism which is known to have racial bias. The authors developed a program to generate different explanation versions for a given data point and conducted an online survey style study wherein the participants were made to judge the fairness of a prediction based on a 1 to 7 Likert scale and had to justify the rating given by them.

REFLECTION

I agree that ML systems are often seen as ‘black boxes’ and that this truly does make gauging fairness issues difficult. I believe that this study conducted was indeed very useful in throwing light upon the need for more well-defined fairness judgement methodologies involving humans as well. I feel that the different explanation styles taken into account in this paper – influence, demographic-based, sensitivity, and case-based were good and helped cover various aspects that could contribute in understanding the fairness of the prediction. I found it interesting to learn that the local explanations helped to better understand discrepancies between disparately impacted cases and non-impacted cases whereas the global explanations were more effective in exposing case-specific fairness issues.

I also found interesting to learn that different regions of the feature space may have varied levels of fairness and fairness issues. Having not considered the fairness aspect of my datasets and the impact this would have on the models I build, this made me realize that it would indeed be important to have more fine-grained sampling methods and explanation designs in order to judge the fairness of ML systems.

QUESTIONS

The participants involved in this study comprised of 78.8% self-identified Caucasian MTurk workers. Considering that the COMPAS dataset being considered in this study is known to have racial bias, would changing the percentage of the African American workers involved in these studies have altered the results? The study focused on workers living in the US, perhaps knowing the general judgement of people living across the world from multiple races may have also been interesting to study?
The authors utilize a logistic regression classifier that is known to be relatively more interpretable. How would a study of this kind extend when it comes to other deep learning systems? Could the programs used to generate explanations be used directly? Has any similar study been performed with these kinds of more complex systems?
As part of the limitations of this study, the authors mention that ‘the study was performed with crowd workers, rather than judges who would be the actual users of this type of tool’. How much would the results vary if this study was conducted with judges? Has any follow-up study been conducted?

02/26/20 – Lulwah AlKulaib- Interpretability

February 25, 2020 Lulwah AlKulaib 1 Comment

Summary

Machine learning (ML) models are integrated in many departments nowadays (for example: criminal justice, healthcare, marketing, etc.). The universal presence of ML has moved beyond academic research and grew into an engineering discipline. Because of that, it is important to interpret ML models and understand how they work by developing interpretability tools. Machine Learning engineers, practitioners, and data scientists have been using these tools. However, due to the minimal evaluation of the extent to which these tools achieve interpretability, the authors study the use of two interpretability tools to uncover issues that arise when building and evaluating models. The interpretability tools are: InterpretML implementation of GAMs and the SHAP Python package. They conduct a contextual inquiry and survey197 data scientists to observe how they use interpretability tools to uncover common issues that arise when building and evaluating ML models. Their results show that data scientists did utilize visualizations produced by interpretability tools to uncover issues in datasets and models. Yet, the availability of these tools has led to researchers over-trust and misuse of them.

Reflection

Machine learning is now being used to address important problems like predicting crime rates in cities to help police distribute manpower, identifying cancerous cells, predicting recidivism in the judiciary system, and locating buildings that are subject to catching on fire. Unfortunately, these models have been shown to learn biases. Detecting these biases is subtle, especially to beginners in the field. I agree with the authors that it is troublesome when machine learning is misused, whether intently or due to ignorance, in situations where ethics and fairness are eminent. Lacking models explainability can lead to biased and ill-informed decisions. In our ethics class, we went over case studies where interpretability was lacking and caused representing racial bias in facial analysis systems [1], biasing recidivism predictions [2], and textual gender biases learned from language [3]. Some of these systems were used in real life and have affected people’s lives. I think that using a similar analysis to the one presented in this paper before deploying systems into practice should be mandatory. It would give developers better understanding of their systems and help them avoid making biased decisions that can be corrected before going into public use. Also, informing developers on how dependable are interpretability tools and when to tell that they’re over trusting them, or when are they misusing them is important. Interpretability is a “new” field to machine learning and I’ve been seeing conferences adding sessions about it lately. I’m interested in learning more about interpretability and how we can adapt it in different machine learning modules.

Discussion

Have you used any of the mentioned interpretability packages in your research? How did it help in improving your model?
What are case studies that you know of where machine learning bias is evident? Were these biases corrected? If so, How?
Do you have any interpretability related resources that you can share with the rest of the class?
Do you plan to use these packages in your project?

References

https://splinternews.com/predictive-policing-the-future-of-crime-fighting-or-t-1793855820
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Bolukbasi, T., Chang, K. W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in neural information processing systems (pp. 4349-4357).

02/26/2020 – Ziyao Wang – Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning

February 25, 2020 Ziyao Wang 2 Comments

As machine learning models are deployed in variety domains of industry, it is important to design some interpretability to help model users, such as data scientists and machine learning practitioners, better understand how these models work. However, there have been little researches focused on the evaluation of the performance of these tools. The authors in this paper did experiments and surveys to fill this gap. They interviewed 6 data scientists from a large technology company to find out the most common issues faced by data scientists. Then they conducted a contextual inquiry towards 11 participants based on the common issues using the InterpretML implementation of the Gams and the SHAP python package. Finally, they made a survey of 197 data scientists. With the experiments and surveys, the authors highlighted the misuse and over-trust problem and the need for the communication between members of HCI and ML communities.

Reflection:

Before reading this paper, I hold the view that the interpretability tools should be able to cover most of the data scientists’ need. However, now I have the view that the tools for interpretation are not designed by the ML community, which will result in the lack of accuracy of the tools. When data scientists or machine learning practitioners want to use these tools to learn how the models operate, they may face problems like misuse or over-trust. I don not think this is the users’ fault. Tools are designed for make users feel more convenient when doing tasks. If the tools will make users confuse, the developers should make change to the tools to give users better user experiences. In this case, the authors suggested that the members of HCI and ML communities should work together when developing the tools. This need the members to leverage their strength so that the designed tools can let users understand the models easily while the tools are user-friendly. Meanwhile, comprehensive instructions should be written to explain how the users can use the tools to understand the models accurately and easily. Finally, both the efficiency and accuracy of both the tools and the implementation of models will be improved.

From data scientists and machine learning practitioners’ point of view, they should try to avoid to over-trust the tools. The tools cannot fully explain the models and there may be mistakes. The users should always be critic to the tools instead of fully trusting them. They should read the instructions carefully, understand how to use the tools and what the tools are used for, what is the models being used for and how to use the models. If they can consider thoughtfully when using these tools and models, instead of guessing the meaning of the results from the tools, the number of misuse and over-trust cases will be decreased sharply.

Questions:

How to design a proposed interactive interpretability tool? What kinds of interactions should be included?
How to design a tool that can make users to dig the models conveniently instead of letting them use the models without knowing how the models work?
How to design tools which can leverage the strength of mental models mostly

02/26/2020 – Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment – Yuhang Liu

February 25, 2020 yuhang Liu 1 Comment

This paper mainly explores the injustice of the results of machine learning. These injustices are usually reflected in gender and race, so in order to make the results of machine learning better serve people, the author of the paper conducted an empirical study with four types of programmatically generated explanations to understand how they impact people’s fairness judgments of ML systems. In the experiment, these four interpretations have different characteristics, and after the experiment, the author has the following findings:

Some interpretations are inherently considered unfair, while others can increase people’s confidence in the fairness of the algorithm;
Different interpretations can more effectively expose different fairness issues, such as the model-wide fairness issue and the fairness difference of specific cases.
There are differences between people, different people have different positions, and the perspective of understanding things will affect people’s response to different interpretation styles.

In the end, the authors obtained that in order to make the results of machine learning generally fair, in different situations, different corrections are needed and differences between people must be taken into account.

Reflection：

In another class this semester, the teacher gave three reading materials on the results of machine learning and increased discrimination. In the discussion of those three articles, I remember that most students thought that the reason for discrimination should not be Is the inaccuracy of the algorithm or model, and I even think that machine learning is to objectively analyze things and display the results, and the main reason that people feel uncomfortable and even feel immoral in the face of the results is that people are not willing to face these results. It is often difficult for people to have a clear understanding of the whole picture of things, and when these unnoticed places are moved to the table, people will be shocked or even condemn others, but it is difficult to really think about the cause of things. But after reading this paper, I think my previous understanding was narrow: First, the results of the algorithm and the interpretation of the results must be wrong and discriminatory in some cases. So only if we resolve this discrimination can the results of machine learning be able to better serve people. At the same time, I also agree with the ideas and conclusions in the article. Different interpretation methods and different emphasis will indeed affect the fairness of interpretation. All the prerequisites to eliminate injustices are to understand the causes of these injustices. At the same time, I think the main solution to eliminate injustice is still on the researcher. Reason why I think computer is fascinating is it can always keep things rational and objective to deal with problems. People’s response to different results and the influence of different people on different model predictions are the key to eliminating this injustice. Of course, I think people will think that part of the cause of injustice is also the injustice of our own society. When people think that the results of machine learning carry discrimination based on race, sex, religion, etc., should we think about this discrimination itself, should we pay more attention to gender equality, ethnic equality and how to make the results look better.

Question:

Do you think that this unfairness is more because the results of machine learning mislead people or it is existed in people’s society for a long time.
The article proposes that in order to get more fair results, more people need to be considered, what changes should users make.
How to combine the points of different machine learning explanations to create a fairer explanation.

2/26/20 – Jooyoung Whang – Will You Accept an Imperfect AI? Exploring Designs for Adjusting End-user Expectations of AI Systems

February 25, 2020 Jooyoung Whang 1 Comment

This paper seeks to study what an AI system could do to get more approved by users even if it is not perfect. The paper focuses on the concept of “Expectation” and the discrepancy between an AI’s ability and a user’s expectation for the system. To explore this problem, the authors implemented an AI-powered scheduling assistant that mimics the look of MS Outlook. The agent detects in an E-mail if there exists an appointment request and asks the user if he or she wants to add a schedule to the calendar. The system was intentionally made to perform worse than the originally trained model to explore mitigation techniques to boost user satisfaction given an imperfect system. After trying out various methods, the authors conclude: Users prefer AI systems focusing on high precision and users like systems that gives direct information about the system, shows explanations, and supports certain measure of control.

This paper was a fresh approach that appropriately addresses the limitations that AI systems would likely have. While many researchers have looked into methods of maximizing the system accuracy, the authors of this paper studied ways to improve user satisfaction even without a high performing AI model.

I did get the feeling that the designs for adjusting end-user expectations were a bit too static. Aside from the controllable slider, the other two designs were basically texts and images with either an indication of the accuracy or a step-by-step guide on how the system works. I wonder if having a more dynamic version where the system reports for a specific instance would be more useful. For example, for every new E-mail, the system could additionally report to the user how confident it is or why it thought that the E-mail included a meeting request.

This research reminded me of one of the UX design techniques: think-aloud testing. In all of their designs, the authors’ common approach was to close the gap between user expectation and system performance. Think-aloud testing is also used to close that gap by analyzing how a user would interact with a system and adjusting from the results. I think this research approached it in the opposite way. Instead of adjusting the system, the authors’ designs try to adjust the user’s mental model.

The followings are the questions that I had while reading the paper:

1. As I’ve written in my reflection portion, do you think the system will be approved more if it reported some information about the system for each instance (E-mail)? Do you think the system may appear to be making excuses for when it is wrong? In what way would this dynamic version be more helpful than the static design from the paper?

2. In the generalizability section, the authors state that they think some parts of their study are scalable to other kinds of AI systems. What other types of AI could benefit from this study? Which one would benefit the most?

3. Many AI applications today are deployed after satisfying a certain accuracy threshold which is pretty high. This can lead to more funds and time needed for development. Do you think this research will allow the stakeholders to lower the threshold? In the end, the stakeholders just want to achieve high user satisfaction.

02/26/2020 – Bipasha Banerjee – Will You Accept an Imperfect AI? Exploring Designs for Adjusting End-user Expectations of AI Systems

February 25, 2020 bipashab 1 Comment

Summary

The paper talks about user-expectation when it comes to end-user applications. It is essential to make sure that the user-expectations are set to an optimal level so that the user does not find the end product underwhelming. Most of the related work done in this area highlights the fact that user disappointment occurs if the initial expectation is set to “too high”. Initial expectations can originate from advertisements, product reviews, brands, word of mouth, etc. They tested their hypothesis on an AI-powered scheduling assistant. They created an interface similar to the Microsoft Outlook email system. The main purpose of the interface was to detect if an email was sent with the intention of scheduling a meeting. If so, the AI would automatically highlight the meeting request sentence and then allow the user to schedule the meeting. The authors designed three techniques, namely, accuracy indicator, example-based explanations, and control slider, to design for adjusting end-user expectations. Most of their hypotheses were proved to be true. Yet, it was found that an AI system based on high recall had better user acceptance than high precision.

Reflection

The paper was an interesting read on adjusting the end-user expectation. The AI scheduling assistant was used as a UI-tool to evaluate the users’ reactions and expectations of the system. The authors conducted various experiments based on three design techniques. I was intrigued to find out that the high precision version did not result in a high perception of accuracy. An ML background practitioner always looks at precision (false positive). From this, we can infer that the task at hand should be the judge of what metric we should focus on. It is certainly true that here, displaying a wrongly highlighted sentence would annoy the user less than completely missing out on the meeting details in an email. Hence, I would say this kind of high recall priority should be kept in mind and adjusted according to the end goal of the system.

It would also be interesting to see how such expectation oriented experiments performed in the case of other complex tasks. This AI scheduling task was straight-forward, where there can be only one correct answer. It is necessary to see how the expectation based approach fairs when the task is subjective. By subjective, I mean, the success of the task would vary from user to user. For example, in the case of text summarization, the judgment of the quality of the end product would be highly dependent on the user reading it.

Another critical thing to note is the expectation can also stem from a user’s personal skill level and subsequent expectation from a system. As a crowd-worker, having a wrongly highlighted line might not affect as much when the number of emails and tasks are less. How likely is this to annoy busy professionals who might have to deal with a lot of emails and messages with meeting requests. Having multiple incorrect highlights a day is undoubtedly bound to disappoint the user.

Questions

How does this extend to other complex user-interactive systems?
Certain tasks are relative, like text summarization. How would the system evaluate success and gauge expectations in such cases where the task at hand is extremely subjective?
How would the expectation vary with the skill level of the user?

02/26/2020 – Sushmethaa Muhundan – Will You Accept an Imperfect AI? Exploring Designs for Adjusting End-user Expectations of AI Systems

February 25, 2020 Sushmethaa Muhundan 1 Comment

The perception and acceptance of AI systems are impacted by the expectations that the users have on that system as well as their prior experiences working with AI systems. Hence, expectation setting before interacting with the system is extremely pivotal to avoid any inflated expectations which in turn could lead to disappointment if they are not met. A Scheduling Assistant system has been used as an example in this paper and expectation adjustment techniques are discussed. The paper focusses on exploring methods to shape the user’s expectation before they use the system and study the impacts on the acceptance of the system by the user. Apart from this, the impact of different AI imperfections is also studied, specifically the cases of false positives vs false negatives. Accuracy indicator, example-based explanation and performance control are the three techniques proposed and evaluated. Via the studies conducted, it is concluded that a better expectation setting done before using a system decreases the chances of disappointment by highlighting the flaws of the system beforehand.

The study conducted assumes that the users are new to the environment and dedicates time explaining the interface at the initial stage of the experiment. I felt that this was helpful since the people involved in the survey can now follow along. I found this missing in some of the earlier papers read where it was assumed that all the readers had sufficient prior knowledge to follow along. Also, despite the fact that the initial performance of the system was ninety-three percent on the test dataset, in order to gauge the sentiments of the users and evaluate their expectation setting hypothesis, the authors decided to set the accuracy to fifty percent. I felt that this greatly improved the scope for disappointment, thereby helping them efficiently validate their expectation setting system and its effects. I felt that the decision to use visualizations as well as a short summary of the intent in their explanation was helpful since this eradicated the need for the users to read lengthy summaries and would offer better support for user decision. It was also good to note the authors take on deception and marketing as a means to set false expectations. This study went beyond such techniques and focused on shaping the expectations of the people via explaining the accuracy of the system. I felt that this perspective was more ethical compared to the other means adopted in this area.

Apart from the expectations that users have, what other factors influence the perception and acceptance of AI systems by the users?
What are some other techniques, visual or otherwise that can be adopted to set expectations of AI systems?
How can the AI system developers tackle trust issues and acceptance issues? Given that perceptions and individual experiences are extremely diverse, is it possible for an AI system to be capable of completely satisfying all its users?

02/26/2020 – Subil Abraham – Will you accept an imperfect AI?

February 25, 2020 Subil Abraham 1 Comment

Reading: Rafal Kocielnik, Saleema Amershi, and Paul N. Bennett. 2019. Will You Accept an Imperfect AI? Exploring Designs for Adjusting End-user Expectations of AI Systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19), 1–14. https://doi.org/10.1145/3290605.3300641

Different parts of our lives are being infused with AI magic. With this infusion, however, comes problems, because the AI systems deployed aren’t always accurate. Users are used to software systems being precise and doing exactly the right thing. But unfortunately they can’t extend that expectation for AI systems because they are often inaccurate and make mistakes. Thus it is necessary for developers to set expectations of the users ahead of time so that the users are not disappointed. This paper proposes three different visual methods of setting the user’s expectations on how well the AI system will work: an indicator depicting accuracy, a set of examples demonstrating how the sytem works, and a slider that controls how aggressively the system should work. The system under evaluation is a detector that will identify and suggest potential meetings based on the language in an email. The goal of the paper isn’t to improve the AI system itself, but rather to evaluate how well the different expectation setting methods work given an imprecise AI system.

I want to note that I really wanted to see an evaluation on the effects of mixed techniques. I hope that it will be covered in possible future work they do but am also afraid that such work might never get published because it would be classified as incremental (unless they come up with more expectation setting methods beyond the three mentioned in this paper, and do a larger evaluation). It is useful to see that we now have numbers to back up that high-recall applications under certain scenarios are perceived as more accurate. It makes intuitive sense that it would be more convenient to deal with false positives (just close the dialog box) than false negatives (having to manually create a calendar event). Also, seeing the control slider brings to mind the trick that some offices play where they have the climate control box within easy reach of the employees but it actually doesn’t do anything. It’s a placebo to make people think it got warmer/colder when nothing has changed. I realize that the slider in the paper is actually supposed to do what it advertised, but it makes me think of other places where a placebo slider can be given to a user to make them think they have control when in fact the AI system remains completely unchanged.

What other kinds of designs can be useful for expectation setting in AI systems?
How would these designs look different for a more active AI system like medical prediction, rather than a passive AI system like the meeting detector?
The paper claims that the results are generalizable for other passive AI systems, but are there examples of such systems where it is not generalizable?