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.
