Summary

Bansal et al.’s paper “Updates in Human-AI Teams” explores an interesting problem — the influence of updates to an AI system on the overall team performance. Nowadays, AI systems have been deployed to  support human decision making in high-stakes domains including criminal justice and healthcare. In the working process of a team of humans and AI systems, humans make decisions with a reference to AI’s inferences. A successful partnership requires that the human develops an understanding into the AI system performance, especially its error boundary. Updates with algorithms of higher performance can potentially increase the AI’s predictive accuracy. However, that may require humans to regain interactive experiences and rebuild their confidence in the AI system, the adjusting process of which may actually hurt team performance. The authors introduce the concept of compatibility between an AI update and prior user experience and present methods for studying the role of compatibility in human-AI teams. Extensive experiments on three high-stakes classification tasks (recidivism, credit risk, and mortality) demonstrate that current AI systems are not provided with compatible updates, resulting in decreased performance after updating. To improve the compatibility of an update, the authors propose a re-training objective by penalizing new failures from AI systems. Their proposed compatible updates achieve a good balance of the performance and compatibility trade-off in different tasks.

Reflection

I think making AI and humans as a team to take full advantage of the collaboration is a pretty neat idea. Humans are born with the ability to adapt in the face of an uncertain and adverse world with the capacity of logic reasoning. Machines cannot perform well in those areas but can achieve efficient computation and free people for higher-level tasks. Understanding how machines can efficiently enhance what humans perform best and how humans can augment the work scope of machines is the key to rethink and redesign current decision making system.

What I find interesting about the research problem discussed in this paper is that the authors focus on the idea of unifying a decision made by humans and machines but not merely on the performance in tasks to recommend updates. In machine learning with no human involved, the goal is usually to achieve better and better performance which is evaluated by metrics such as accuracy, precision, recall, etc. The compatible updates can be seen as the machine learning algorithms with similar decision boundaries but better performance, which seems to be an even more difficult task to accomplish. To get there, humans need to perform crucial roles. Firstly, humans must train machines to achieve good performance on certain tasks. Next, humans need to understand and be able to explain the outcomes of those tasks, especially where AI systems fail. That requires an interpretability component in the system. As AI systems are increasingly drawing conclusions through opaque processes (also-called black-box problem), there is a large demand of human experts in the field to explain model behavior to non-expert users. Last but not least, humans need to sustain the responsible use of  AI systems by, for example, updating for better decision making discussed in the paper. That would require a large body of human experts who continually work to ensure that AI systems are functioning properly, safely, and responsibly. 

The above discussion is one side of a coin, focusing on how humans can extend what machines can achieve. The other side is comparatively less discussed in the current literature. Except for extending physical capabilities, how humans can learn from the interaction with AI systems and enhance individual abilities is an interesting question to explore. I would imagine, in an advanced Human-AI team, that humans and AI systems communicate in a more interactive way which allows for collaborative learning from their own mistakes and the rationale of the correct decisions made by each other. That leads to another question, if AI systems can exceed or rival humans in high-stake decision making such as recidivism and underwriting, how risky is it to handle the tasks to machines? How can we decide when to let humans take control? 

Discussion

I think the following questions are worthy of further discussion.

• What can humans do that machines cannot and vice versa?
• What is the goal of decision making and what factors are stopping humans or machines from making good decisions? 
• In the Human-AI teams discussed in the paper, what can humans benefit from the interaction with the AI systems?
• The partnership introduced by the authors is more like a human-assisting-machine approach. Can you provide some examples of machine-assisting-human approaches?

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This paper first proposes the complementarity between humans and artificial intelligence. In many cases, humans and artificial intelligence will form a team. When people make decisions after checking the inferences of AI, this cooperation model has applications in many fields, and achieved significant results. Usually, this kind of achievement requires certain prerequisites. First, people must have their own judgments on the conclusions of artificial intelligence. At the same time, the results of artificial intelligence must be accurate. The tacit cooperation between the two can improve efficiency. However, with the updating of artificial intelligence systems and the expansion of data, this cooperation will be broken. On the one hand, the accuracy of artificial intelligence will decline, and because of the expansion of boundaries, people’s understanding of artificial intelligence will be broken. So after the system update, the efficiency will be reduced instead. This paper mainly studies this situation. The article hopes to be compatible with the previous method after the update, so several methods are proposed to achieve this purpose, so as to achieve more compatible and accurate updates.

It is also suggested that this idea is obtained by analogy. In software engineering, if the updated system can support legacy software, it will be compatible after the update. I agree with this kind of analogy greatly, which is similar to bionics. We can continuously apply new ideas to the computer field through this kind of thought. The method mentioned in this paper is also very necessary. In the ordinary process of artificial intelligence or machine learning, we usually build data sets for each time, and lack the concept of inheritance, which is very inconvenient. After adopting compatible ideas, it will greatly save energy and be able to serve people more smoothly.

This article introduces CAJA, a platform for measuring the impact of AI performance and updates on team performance. At the same time, a practical retraining goal is introduced in the article to improve update compatibility. The main idea is to improve update compatibility by punishing new errors. But it can also be seen from the text that trust is the core of team work. Admittedly, trust is the essence of a team, but only as the basis of work, I think that more simulations and improvements are needed to improve humanity. The combination of problem-solving factors and the key of machine learning, we know that after learning new things, people will not have a negative impact on previous skills, but we will have more perspectives and methods to think about a problem, so I think that humans and machines should be mixed, that is, a team as a whole, so that the results can be more compatible, and the human machine interaction can be more successful.

question：

1. What are the implications of compatible AI updates?
2. How to better treat people and machines as a whole?
3. Whether compatible AI will affect the final training results?

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Summary

            Bansal et. al discuss how human-AI teams work in solving high-stakes issues such as hospital patient discharging scenarios or credit risk assessment. They point out that the humans in these teams often create a mental model of the AI suggestions, where the mental model is an understanding of when the AI is likely wrong about the outcome. The authors then show that updates to the AI can produce worse performance if they are not compatible with the already formed mental model of the human user. They go on to define types of compatibility for AI updates, as well as a few other key terms relating to human/AI teams. They develop a platform to measure how compatibility can affect team performance, and then measure AI update compatibility effectiveness through a user study using 25 mTurk workers. In all, they show that incompatible updates reduce performance as compared to no update at all.

Personal Reflection

            The paper was an interesting study in the effect of pushing updates without considering the user involved in the process. I hadn’t thought of the human as an exactly equal player in the team, where the AI likely has more information and could provide a better suggestion. However, it makes sense that the human leverages other sources of information and forms a better understanding of what choice to ultimately make.

            CAJA, the human/AI simulation platform, seems like a good way to test AI updates, however I struggle to see how it can be used to test other theories as the authors seem to suggest. It is, essentially, a simple user-learning game, where users figure out when to trust the machine and when to deviate. While this isn’t exactly my field of expertise, I only see the chance to change information flows and the underlying AI as ways of learning new things about human/AI collaboration. This would mean terming this as a platform is a little excessive.

Questions

1. The authors mention that, in order to defeat mTurk scammers who click through projects like these quickly, they drop the lowest quartile (in terms of performance) out of their results. Do you think this was an effective countermeasure, or could the authors be cutting good data?
2. From other sources, such as Weapons of Math Destruction, we can read how some AI suggestions are inherently biased (even racist) due to input data. How might this change the authors results? Do you think this is taken into consideration at all?
3. One suggestion near the end of the paper stated that, if pushing an incompatible update, the authors of the AI should make the change explicit so that the user could adjust accordingly. Do you think this is an acceptable tradeoff to not creating a compatible update?  Why or why not?
4. The authors note that, as the complexity of error boundary f became more complex, errors increased, so they kept to relatively simple boundaries. Is this an effective choice for this system, considering real systems are extremely complex? Why or why not?
5. The authors state that they wanted the “compute” cost to be net 0. Does this effectively simulate real-world experiences? Is the opportunity-cost the only net negative here?

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Summary:

In this paper, the author presented that it is prevailing nowadays human and AI form a team to make decisions in a way that AI provides recommendations and humans decide to trust or not. In these cases, successful team collaboration mainly based on human knowledge on previous AI system performance. Therefore, the author proposed that though the update of the AI system would enhance the AI predictive precision, it will hurt the team performance since the updated version of the AI system usually not compatible with the mental model that humans developed with previous AI systems. To solve this problem, the author introduced the concept of compatibility of AI update with prior user experience. To examine the role of this compatibility in Human-AI teams, the author proposed methods and designed a platform called CAJA to measure the impact of updates on team performance. The outcomes show that team performance could be harmed even the updated system’s predictive accuracy improved. Finally, the paper proposed a re-training objective that can promote the compatibility of updates. In conclusion, to avoid diminish team performance, the developer should build a more compatible update without surrendering performance. 

Reflection:

In this paper, the author talked about a specific interaction, which is AI-advised human decision making. As the example presented in the paper–Patient readmission system. In these cases, an incompatible update of the AI system would indeed harm the team performance. However, I think the extent of the impact largely depends on the correlation between the human and AI systems.

If the system and the user have a high grade of interdependence, both are not specialists on a task, the system prediction accuracy and user knowledge have the same impact on the decision result, the incompatible update of the AI system will weaken the team performance. Even though this effect can be eliminated by the running-in of the user and the system later, the cost for the decision in the high-stakes domain will be very large.

However, if the system interacts with users frequently, but the system’s predictions are only one of the concerns for humans to make decisions and cannot directly affect the decision, then the impact of incompatible updates on team performance will be limited.

Besides, if humans are more expertise on the task, and can validate the correctness of the recommendation promptly, then both the improvement of the system performance and the new errors caused by the system update will not have much impact on the results. On the other hand, if the error caused by the update does not affect team performance, then when updating the system, we do not need to consider compatibility but only need to consider the improvement of system performance. As a conclusion, if there is not enough interaction between the user and the system, and the degree of interdependence is not high, or the system only serves as an auxiliary or double-check, then the system update will not have a great impact on team performance.

A compatible update is indeed helpful for users to quickly adapt to the new system, but I think the impact of update largely depends on the correlation between the user and the system, or the proportion of the system’s role in teamwork.

Besides, design a compatible update version also requires extra cost. Therefore, I think we should consider minimizing the impact of system errors on the decision-making process when designing the system and establishing human-AI interaction.

Question:

1. What do you think about the concept of compatibility of AI update?
2. Do you have any human-AI system examples that apply this author theory?
3. Under what circumstances do you think the author’s theory is the most used and when it is not applicable?
4. When we need to update the system frequently, do you think it is better to build a compatible update or to use an alternative method to solve the human adaptation costs?
5. In my opinion, Huaman’s degree of adaptation is very high, and the cost required for humans to adapt is much smaller than the cost of developing a compatible update version. what do you think?

Word Count: 682

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