Summary

The paper discusses the fact that computer-aided products should be considered to be an enhancement of human work rather than it being a replacement. The paper emphasizes that technology, on its own, is not always full proof and that humans, at times, tend to rely completely on technology. In fact, AI in itself can yield faulty results due to biases in the training data, lack of enough data, among other factors. The authors point out how the coupling of human and machine efforts can be done successfully through some examples of autocompleting of google search and grammar/spelling correction. The paper aimed to use AI techniques but in a manner that makes sure that humans remain the primary controller. The authors considered 3 case studies, namely data wrangling, data visualization for exploratory analysis, and natural language translation, to demonstrate how shared representations perform. In each case, the models were designed to be human-centric and to have automated reasoning enabled. 

Reflection

I agree with the authors’ statement about data wrangling that most of the time is spent in cleaning and preparing the data than actually interpreting or applying the task one specializes in. I was amused by the idea that users’ work of transforming the data is cut short and aided by the system that suggests users the proper action to take. I believe this would indeed help the users of the system if they get the desired options directly recommended to them. If not, it will help improve the machine further. I particularly found it interesting to see that users preferred to maintain control. This makes sense because, as humans, we have an intense desire to control.

The paper never explains clearly who the participants of the system are. This would be essential to know who the users were exactly and how specialized they are in the field they are working on. It would also give an in-depth idea about the experience they had interacting with the system, and thus I feel the evaluation would be complete.  

The paper’s overall concept is sound. It is indeed necessary to have a seamless interaction between man and the machine. They have mentioned three case studies. However, all of them are data-oriented. It would be interesting to see how the work can be extended to other forms – videos, images. Facebook picture tagging, for example, does this task to some extent. It suggests users with the “probable” name(s) of the person in the picture. This work can also be used to help detect fake vs. real images or if the video has been tampered.

Questions

1. How are you incorporating the notion of intelligent augmentation in your class project?
2. Case studies are varied but mainly data-oriented. How would this work differ if it was to imply images? 
3. The paper mentions “participants” and how they provided feedback etc. However, I am curious to know how they were selected? Particularly, the criteria that were used to select users to test the system.

Read More

SUMMARY

The authors of this paper aim to demonstrate the capabilities of various interactive systems that build on the complementary strengths of humans and AI systems. These systems aim to promote human control and skillful action. The interactive systems that the authors have developed span three areas – data wrangling, exploratory analysis, and natural language translation. In the Data Wrangling project, the authors demonstrate a means that enabled users to create data-transformation scripts within a direct manipulation interface that was augmented by the use of predictive models. While covering the area of exploratory analysis, the authors developed an interactive system ‘Voyager’ that helps analysts engage in open-ended exploration as well as targeted question answering by blending manual and automated chart specification. As part of the predictive translation memory (PTM) project, that aimed to blend the automation capabilities of machines with rote tasks and the nuanced translation guidance that can be provided by humans. Through these projects, the authors found that there exist various trade-offs in the design of such systems.

REFLECTION

The authors mention that users ‘may come to overly rely on computational suggestions’ and this statement reminded me of the paper on ‘Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning’ wherein the authors discovered that the data scientists used as part of the study over-trusted the interpretability tools.

I thought that the use of visualizations as part of the Data Wrangling project was a good idea since humans often work well with visualizations and that this can speed up the task at hand. As part of previous coursework, my professor had conducted a small experiment in class wherein he made us identify a red dot among multiple blue dots and then identify a piece of text in a table. As expected, we were able to identify the red dot much quicker – attesting to the fact that visual aids often help humans to work faster. The interface of the ‘Voyager’ system reminded me of the interface of the ‘Tableau’ data visualization software. I found that, in the case of the predictive translation memory (PTM) project, it was interesting that the authors mention the trade-off between customers wanting translators that have more consistent results versus human translators that experienced a ‘short-circuiting’ of thought with the use of the PTM tool.

QUESTIONS

1. Given that there are multiple trade-offs that need to be considered while formulating the design of such systems, what is the best way to reduce this design space? What simple tests can be performed to evaluate the feasibility of each of the systems designed?
2. As mentioned in the case of the PTM project, customers hiring a team of translators prefer more consistent results which can be aided by MT-powered systems. However, one worker found that the MT ‘distracts from my own original translation’. Specifically in the case of natural language translation, which of the two do you find to be more important, the creativity/original translation of the worker or consistent outputs?
3. In each of the three systems discussed, the automated methods suggest actions, while the human user is the ultimate decision maker. Are there any biases that the humans might project while making these decisions? How much would these biases affect the overall performance of the system?

Read More

Summary

Heer’s focus in this paper is on refocusing AI and machine learning into everyday interactions that assist users in their work rather than trying to replace users. He reiterates many times in the introduction that humans should remain in control while the AI assists them in completing the task, and even brings up the recent Boeing automation mishaps as an example of why human-in-the-loop is so essential to future developments. The author then describes several tools in data formatting, data visualization, and natural language translation that use AI to assist the user by suggesting actions based on their interactions with data, as well as domain-specific languages (DSLs) that can quickly perform actions through code. The review of his work shows that users want more control, not less, and that these tools increase productivity while allowing the user to ultimately make all of the decisions.

Personal Reflection

               I enjoyed this paper as an exploration of various ways people can employ semantic interaction in interfaces to boost productivity. Furthermore, the explorations in how users can do this without giving up control was remarkable. I hadn’t realized that the basic idea behind autocorrect/autocomplete could apply in so many different ways in these domains. However, I did notice that the author mentioned that in certain cases there were too many options for what to do next. I wonder how much ethnographic research needs to go into determining each action that’s likely (or even possible) in each case and what overhead the AI puts on the system.

               I also wonder how these interfaces will shape work in the future. Will humans adapt to these interfaces and essentially create new routines and processes in their work? As autocomplete/correct often creates errors, will we have to adapt to new kinds of errors in these interfaces? At what point does this kind of interaction become a hindrance? I know that, despite the number of times I have to correct it, I wouldn’t give up autocomplete in today’s world.

Questions

1. What are some everyday interactions that you interact with in specialized programs and applications? I.E., beyond just autocorrect. Do you always utilize these features?
2. The author took three fairly everyday activities and created new user interfaces with accompanying AI with which to create better tools for human-AI collaboration. What other everyday activities can you think of that you could create a similar tool for?
3. How would you gather data to create these programs with AI suggestions? What would you do to infer possible routes?
4. The author mentions expanding these interfaces to (human/human) collaboration. What would have to change in order to support this? Would anything?
5. DSLs seem to be a somewhat complicated addition to these tools. Why would you want to use these and is it worth learning about the DSL?
6. Is ceding control to AI always a bad idea? What areas do you think users should cede more control or should gain back more control?

Read More

Summary

The paper considers the design of systems that enable rich and adaptive interaction between people and algorithms. The authors attempt to balance the complementary strengths and weaknesses of humans and algorithms while promoting human control and skillful action.They aim to employ AI methods while ensuring that people remain in control. Supporting that people should be unconstrained in pursuing complex goals and exercising domain expertise.They share case studies of interactive systems that they developed in three fields: data wrangling, exploratory analysis, and natural language translation that integrates proactive computational support into interactive systems. They examine the strategy of designing shared representations that augment interactive systems with predictive models of users’ capabilities and potential actions, surfaced via interaction mechanisms that enable user review and revision for each case study. These models enable automated reasoning about tasks in a human centered fashion and can adapt over time by observing and learning from user behavior. To improve outcomes and support learning by both people and machines, they describe the use of shared representations of tasks augmented with predictive models of human capabilities and actions. They conclude with how they could better construct and deploy systems that integrate agency and automation via shared representations. They also mention that they found that neither automated suggestions nor direct manipulation play a strictly dominant role.But that a fluent interleaving of both modalities can enable more productive, yet flexible, work.

Reflection

The paper was very interesting to read. The case studies presented were thought provoking. They’re all papers based on research that I have read and gone through while learning about natural language processing and the thought of them being suggestive makes me wonder about such work. How user-interface toolkits might affect design and development of models.

I also wonder as presented in the future work, how to evaluate systems across varied levels of agency and automation. What would the goal be in that evaluation process? Would it differ across machine learning disciplines?  The case studies presented in the paper had specific evaluation metrics used and I wonder how that generalizes to other models. What other methods could be used for evaluation in the future and how does one compare two systems  when comparing their results is no longer enough?

I believe that this paper sheds some light to how evaluation criteria can be topic specific, and those will be shared across applications that are relevant to human experience in learning. It is important to pay attention to how they promote interpretability, learning, and skill acquisition instead of deskilling workers. Also, it’s essential that we think of appropriate designs that would optimize trade offs between automated support and human engagement.  

Discussion

• What is your takeaway from this paper?
• Do you agree that we need better design tools that aid the creation of effective AI-infused interactive systems? Why? Or Why not?
• What determines a balanced AI – Human interaction?
• When is AI agency/control harmful? When is it useful?
• Is insuring humans being in control of AI models important? If models were trained by domain experts and domain expertise, then why do we mistrust them?

Read More

Authors: Jeffrey Heer

Summary

The paper discusses interactive systems in three different areas — data wrangling, exploratory analysis, and natural language translation — to showcase the use of “shared representation” of tasks, where machines can augment human capabilities instead of replacing them. All the systems highlight balancing of the complementary strengths and weaknesses of each, while promoting human control.

Reflection

This paper makes the case for intelligence augmentation i.e. augmenting human capabilities with the strengths of AI rather than striving to replace them. Developers of intelligent user interfaces can come up with effective collaborative systems by carefully designing the interface for ensuring that that AI component “reshapes” the shared representations that users can contribute to, and not “replace” them. This is always a complex task, and therefore, requires scoping down from the notion that AI can be used to automate everything by focusing on these editable shared representations. This has other benefits i.e. helps exploit the benefits of AI in a sum-of-parts manner rather than an end-to-end mechanism where an AI is more likely to be erroneous. The paper discusses three different case studies where a mixed-initiative deployment was successful in catering to user expectations in terms of experience and output. 

It was particularly interesting to see the participants complaining that the Voyager system, despite being good, spoilt them as it made them think less. This can hamper adoption of such systems. A reasonable design implication here should be allowing users to choose the features they want or giving them the agency to adjust the degree of automation/suggestions. This also suggests the importance of conducting longitudinal studies to understand how users use the different features of an interface i.e. whether they use one but not the other. 

According to some prior work, machine-suggested recommendations have been known to perpetrate filter bubbles. In other words, users are exposed to a similar set of items and miss out on other stuff. Here, the Voyager recommendations work in contrast to prior work by allowing users to explore the space, analyze different charts and data points they wouldn’t otherwise notice and combat confirmation bias. In other words, the system does what it claims to do i.e. augment the capabilities of humans in a positive sense using the strengths of the machine. 

Questions

1. In the projects you are proposing for the class, does the AI component augment the human capabilities or strive to replace it (eventually)? If so, how?
2. How do you think developers should cater to cases where users are less likely to adopt a system because it impedes their creativity?
3. Do you think AI components (should) allow users to explore the space more than they would normally? Any possible pitfalls (information overdose, unnatural tasks/interactions, etc.)

Read More

Summary:
“Agency plus automation: Designing artificial intelligence into interactive systems” by Heer talks about the drawback of using artificial intelligence techniques for automating tasks, especially the ones that are considered repetitive and monotonous. However, this presents a monumentally optimistic point of view by completely ignoring the ghost work or the invisible labor that goes into making ‘automating’ these tasks. This gap between crowd work and machine automation highlights the need for design and engineering interventions. The authors of this paper try to make use of the complementary nature strengths and weaknesses of the two – creativity, intelligence, world-knowledge of the crowd workers and the cheap and no cognitive overload provided by automated systems. The authors describe in detail the case studies of interactive systems in three different areas – data wrangling, exploratory analysis, and natural language translation. These systems combine computational support with interactive systems. The authors also talk about sharing representations of tasks to include both human intelligence and automated support in the design itself. The authors conclude that “neither automated suggestions nor direct manipulation plays a strictly dominant role” and ” a fluent interleaving of both modalities can enable more productive, yet flexible, work.”

Reflections:
There is a lot of invisible work that goes into automating a task. Most automated tasks require hundreds, if not thousands, of annotations. Machine learning researchers turn a blind eye to all the effort that goes into annotations by calling their systems ‘fully automated’. This view is exclusionary and does not do justice to the vital but seemingly trivial work done by the crowd workers. One of the areas that one can focus on is the open question of shared representation – Is it possible to integrate data representation with human intelligence? If yes, is that useful? Data representation often involves the construction of latent space to reduce the dimensionality of input data and get concise and meaningful information. There may or may not be such representations exist for human intelligence. Maybe borrowing from social psychology might help in such a scenario. There can be other ways to go around this. For example, the authors focus on building interactive systems with ‘collaborative’ interfaces. The three interaction models: Wrangler, Voyager, and PTM do not distribute the tasks equally between humans and automated systems. The automated methods prompt the users with different suggestions which the end user reviews. The final decision making power lies with the end user. It would be interesting to see what would the results looks like if the roles were reversed and the system was turned on its head. An interesting case study could be if the suggestion was given by the end user and the ultimate decision making capability rested with the system. Would the system still be as collaborative? What would the drawbacks of such systems be?

Questions:

1. What are your general thoughts on the paper?
2. What did you think about the case studies? Which other case studies would you include?
3. What are your thoughts on evaluating systems with shared representations? Which evaluation criteria can we use?

Read More

Summary

In this paper, the author proposes multiple systems that can combine the power of both artificial inttelgence and human computation and overcome each one weakness. The author thinks that automating all tasks can lead to a poor results as human component is needed to review and revise results get the best results. The author the autocomplete and spell checkers examples to show that artificial intelligence can offer suggestion and then human can review or revise these suggestions or dismiss the suggestions. The author propose different systems that uses predictive interaction that help users on their tasks that can be partially automated to help the users to focus more on the things that they care more about. One of these systems called Data Wrangling that can used by data analyst on the data preprocessing to help them with cleaning up the data to save more than %80 of their work. The users will need to setup some data mapping and can accept or reject the suggestions. The author proposed project called Voyager that can help with data visualization for exploratory analysis which can be used to help with suggesting visualization elements. The author suggests using AI to automate repeated task and offer the best suggestions and recommendations and let the human decide whether to accept or reject the recommendations. This kind of interaction can improve both machine learning results and human interaction.

Reflection

I found the material presented in the paper to be very interesting. Many discussions were about whether machine can replace human or not was addressed in this paper. The author mentioned that machine can do well with the help of human and the human in the loop will always be necessary.

I also like the idea of the Data Wrangling system as many data analysts and developer spend considerable time on cleaning up the data and most of the steps are repeated regardless of the type of data, and automating these steps will help a lot of people to do more effective work and to focus more on the problem that they are trying to solve rather than spending time on things that are not directly related to the problem.

I agree with author that human will always be in the loop especially on systems that will be used by humans. Advances in AI need human on annotating or labeling the data to work effectively and also to measure and evaluate the results.

Questions

• The author mentioned that the Data Wrangler system can be used by data analysts to help with data preprocessing, do you think that this system can also be used by data scientist since most machine learning and deep learning projects require data cleanup ?

• Can you give other examples of AI-Infused interactive systems that can help different domains and can be deployed into production environment to be used by large number of users and can scale well with increased load and demands?

Read More

This work explores strategies to balance the role of agency and automation by designing user interfaces that enable the shared representations of AI and humans. The goal is to productively employ AI methods while also ensuring that humans remain in control. Three case studies are discussed and these are data wrangling, data visualization for exploratory analysis, and natural language translation. Across each, strategies for integrating agency and automation by incorporating predictive models and feedback into interactive applications are explored. In the first case study, an interactive system is proposed that aims at reducing human efforts by recommending potential transformation, gaining feedback from the user, and performing the transformations as necessary. This would enable the user to focus on tasks that would require the application of their domain knowledge and expertise rather than spending time and effort manually performing transformations. A similar interactive system was developed to aid visualization efforts. The aim was to encourage more systematic considerations of the data and also reveal potential quality issues. In the case of natural language translation, a mixed-initiative translation approach was explored.

The paper has a pragmatic view of the current AI systems and makes a realistic observation that the current AI systems are not capable of completely replacing humans. There is an emphasis on leveraging the complementary strengths of both the human and the AI throughout the paper which is practical. 

Interesting observations were made in the Data Wrangler project with respect to proactive suggestions. If these were presented initially, before the user has had a chance to interact with the system, this feature received negative feedback and was ignored. But, if the same suggestions were presented to users whilst the user was engaging with the system, although the suggestions were not related to the user’s current task, it was received positively. Users viewed themselves as initiators in the latter scenario and hence felt that they were controlling the system. This observation was fascinating since it shows that while designing such user interfaces, the designers should ensure that their users feel in control and are not feeling insecure while using AI systems.

With respect to the second case study, it was reassuring to learn that the inclusion of automated support from the interactive system was able to shift user behavior for the better and helped broaden their understanding of the data. Another positive effect was that the system helped humans combat confirmation bias. This shows that if the interface is designed well, the benefits of AI amplifies the results gained when humans apply their domain expertise.

• The paper deals with designing interactive systems where the complementary strengths of agents and automation systems are leveraged. What could be the potential drawbacks of such systems, if any?
• How would the findings of this paper be translated in the context of your class project? Is there potential to develop similar interactive systems to improve the user experience of the end-users?
• Apart from the three case studies presented, what are some other domains where such systems can be developed and deployed?

Read More

This paper seeks to investigate a method to achieve AI + IA. That is, enhancing human performance using automated methods but not completely replacing it. The author takes into notice that effective automation should first off bring significant value, second be unobtrusive, third do not require precise user input, and finally, adapt. The author takes these points to account and introduces three interactive systems that he built. All these systems utilize machine computing to handle the initial or small repetitive tasks and rely on human computing to make corrections and improve quality. They are all collaborative systems where AI and humans work together to boost each other’s performance. The AI part of the system tries to predict user intentions while the human part of the system drives the work.

This paper reminded me of Smart-Built Environments (SBE), a term I learned in a Virtual Environments class. SBE is an environment where computing is seamlessly integrated into the environment and interaction with it is very natural. It is capable of “smartly” providing appropriate services to humans in a non-intrusive way. For example, a system where the light automatically lights up upon a person entering a room is a smart feature. I felt that this paper was trying to build something similar in a desktop environment. One core difference with SBEs is that SBE also tries to tackle immersion and presence (which are terms frequently used for evaluating virtual environments). I wonder if the author knows about SBEs or got his project ideas from SBEs.

While reading the paper, I wasn’t sure if the author handled the “unobtrusive” part effectively. In one of the introduced systems, Wrangler was an assist tool for preprocessing data. It tries to predict user intention upon observing certain user behavior and recommends available data transformations on a side panel. I believe this was a similar approach to mimic the Google query auto-completion feature. However, I don’t think it’ll work as well as Google’s auto-completion. Google’s auto-complete suggestions appear right below where the user is typing whereas Wrangler suggests it in the side corner. This requires the user to avert his or her eye from where the point of the previous interaction was, and this is obtrusive.

These are the questions that I had while reading the paper:

1. Do you know any other systems that try to seamlessly integrate AI and human tasks? Is that system effective? How so?

2. The author of this paper mostly uses AI to predict user intentions and process repetitive tasks. What other capabilities of AI would be available for naturally integrating with human tasks? What other tasks are hard to do by humans that machines accel at that could be integrated?

3. Do you agree that “the best kind of systems is one where the user does not even know he or she is using it?” Would there ever be a case where it is crucial that the user feels the presence of the system as a separate entity? This thought came to me because systems could (and ultimately does) fail at some point. If none of the users understand how the system works, wouldn’t that be a problem?

Read More

A lot of work has been independently done along the tangents of improving computers to allow humans to use them better, and separately in helping machines do work by themselves. The paper makes the case that in the quest for automation, research in augmenting humans to do work by improving the intelligence of tools has fallen to the wayside. This provides a rich area of exploration. The paper explores three tools in this space that work with the users in a specific domain and predict what they might need or want next, based on a combination of context clues from the user. Two of the three tools, Data Wrangler and Voyager use domain specific languages to represent to the user the operations that are possible, thus providing a shared representation of data transformations for the user and the machine. The last tool, for language translation, does not provide a shared representation but presents the suggestions directly because there is no real way of using a DSL here outside of exposing the parse tree which doesn’t really make sense for an ordinary end user. The paper also makes several suggestions of future work. This includes methods for better monitoring and introspection tools in these human AI systems, allowing shared representations to be designed by AI based on the domain instead of being pre-designed by a human, and finding techniques that would help to identify the right balance between human control and automation for a given domain.

The paper uses these three projects as a framing device to discuss the idea of developing better shared representations and their importance in human AI collaboration. I think its an interesting take, especially the idea of using DSLs as a means of communicating ideas between the human user and the AI underneath. They backed away from discussing what a DSL would look like for the translation software since anything outside of autocomplete suggestions don’t really make sense in that domain, but I would be interested in further exploration in that field. I also find it interesting and it makes sense that people might not like the machine predictions being thrust upon them, either because it influences the thinking or it is just annoying. I think the tools discussed manage to make a good balance in staying out of the users way. Yes, the user will be influenced but that is inevitable because the other option is to not give the predictions at all and now you get no benefit.

Although I see the point that the article is trying to make about shared representations (at least, I think I do), I really don’t see the reason for the article existing besides just the author saying “Hey look at my research, this research is very important and I’ve done things with it including making a startup”. The article doesn’t contribute any new knowledge. I don’t mean for that to sound harsh, and I can understand how reading this article is useful from a meta perspective (saves us the trouble of reading the individual pieces of research that are summarized in this article and trying to connect the dots between them).

1. In the translation task, Why wouldn’t a parse tree work? Are there other kinds of structured representations that would aid a user in the translation task?
2. Kind of a meta question, but do you think this paper was useful on its own? Did it provide anything outside of summarizing the three pieces of research the author was involved in?
3. Is there any way for the kind of software discussed here, where it makes suggestions to the user, to avoid influencing the user and interfering with their thought process?

Read More