Summary:

The main objective of this paper is to investigate how people make fairness judgments of the ML system and how explanations impact their fairness judgments. In particular, they explored the difference between a global explanation, which describes how the model works, and local explanations, which are sensitive and case-based. Besides, the author also demonstrates the impact of individual differences in cognitive style and prior position on algorithmic fairness impact the fairness judgment regarding different explanations. To achieve this goal, the author conducted an online survey-style study with Amazon Mechanical Turk workers with specific criteria. The experiment results indicate that based on different kinds of fairness issues and user profiles, there are varies effective explanation. However, a hybrid explanation that using global explanations to comprehend and evaluate the model and using local explanations to examine individual cases may be essential for accurate fairness judgment. Furthermore, they also demonstrated that individuals’ previous positions on the fairness of algorithms affect their response to different types of interpretations.

Reflection:

First, I think this paper talked about a very critical and imminent topic. Since the exploration and implementation of the machine learning system and AI system, it has been wildly deployed that using ML prediction to make decisions on high-stake fields such as healthcare and criminal predictive. However, societies have great doubts about how the system makes decisions. They cannot accept or even understand why these important decisions should be left to a piece of algorithm. Then, the community’s call for algorithm transparency is getting higher and higher. At this point, an effective, unbiased and user-friendly interpretation of ML system which enables the public to identify fairness problems would not only improve on ensuring the fairness of the ML system, but also increase public trust in ML system output.

However, it is also tricky that there is no one-size-all solution for an effective explanation. I do understand that different people shall have a different reaction to explanations, nevertheless, I was kinda surprised that people have very different opinions on the judgment of fairness. Even though this is understandable considering their prior position on the algorithm, their cognition, and different background, this will make it more complex to ensuring the fairness of the machine learning system. Since the system may need to take into account individual differences in their fairness positions, which may require different corrective or adaptive actions.

Finally, this paper reminds me of another similar topic. When we explain how the model works, how much information should we provide? What kind of information should we preserved so that this information will not be abused? In this paper, the author only mentioned that they would provide two types of explanations, global explanations that describe how the model works, and local explanations that attempt to justify the decision for a specific case. However, they didn’t examine the extent of system model information provided in the explanation. I think this is an interesting topic since we are investigating the impact of explanations on fairness judgment.

Question:

1. Which type of explanations mentioned in this article would you prefer to see when you judge the fairness of the ML system?
2. How did the user perceive machine learning system fairness influence the fairness ensuring process when designing the system?
3. This experiment conducted based on an online survey with crowd workers instead of judges, do you think this would have any influence on experiment results?

Word Count: 564

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Summation

Even though Machine Learning has recently taken the title of “The Best Technology Buzzword” away from cyber security a few years ago, there are two fundamental problem with it; understanding the percentage each feature contributes to an answer and ensuring the model itself is fair. The first problem limits the progress on the second problem and has spawned its own field of research: explainable artificial intelligence. Due to this, it is difficult to ensure models are fair to sensitive data and don’t learn biases. To help ensure models are understood as fair, this team has concentrated on automatically generating four different types of explanations for the rational of the model. These models spawned a multitude of regions of the data, including input influence based, demographic-based, sensitivity-based, and case-based. By showing these heuristics of the same models to a group of crowd-workers, they were able to determine quantitatively determine there is not one perfect explanation method. There must be instead a tailored and customized explanation method.

Response

I agree with the need for explainable machine learning, though I’m unsure about the impact of this team’s work. Using work done previously for the four types and their own preprocessor, they seemingly resolved a question by only continuing it. This may be due to my lack of experience reading psychology papers, though their rationalization for the explanation styles and fairness in judgement seems to be common place. Two of the three conclusions wrapping up the quantitative study seemed appropriate, case-based explanation seemed less fair while local-based explanation was more effective. Though the latter conclusion of people having a previous bias towards machine learning seems to be redundant.

I can appreciate the lengths they went to measure the results against the mechanical turks. Seemingly creating an incremental paper (see the portion about their preprocessor), this may lead to more papers off their gathered heuristics.

Questions

• I wonder if the impact of the survey for the mechanical turks was limited due to only using the four different types of explanations studied. The conclusion of the paper indicated there is no good average and each explanation type was useful in one scenario or another. In this manner would different explanations lead to a good overall explanation?
• A known and understood limitation of this paper was in the use of mechanical turks instead of actual judges. This may be better due to representation of the jury; however, it is hard to measure the full impact without including the judge in this. It would be costly and timely, though it would help to better represent the full scenario.
• Given the only four different types of explanation, would there be room for a combination or collaboration explanation? Though this paper mostly focuses on generating the explanations, there should be room to combine the factors to potentially create the overall good and average explanation, despite the paper limiting itself to the only four explanations early on by fully utilizing the Binns et al survey.

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