This paper discusses people’s dependence on interpretive tools for machine learning. As mentioned in the article, machine learning (ML) models are commonly deployed in various fields from criminal justice to healthcare. Machine learning has gone beyond academia and has developed into an engineering discipline. To this end, interpretability tools have been designed to help data scientists and machine learning practitioners better understand how ML models work. This paper focuses on such software. According to the classification, this software can be divided into two categories, the Interpret ML implementation of GAMs ( glass box models) and the SHAP Python package (a post-hoc explanation technique for blackbox models). The author’s research The results show that users trust machine interpretative results too much and rely too much on the use of machine learning interpretive tools. Few of these users can accurately describe the visualization of the output of these tools. In the end, the authors came to the conclusion that the visualization of the output of the interpretability tool can sometimes help data scientists find problems with data sets or models. For both tools, however, the existence of visualizations and the fact that the tools are publicly available have led to situations of excessive trust and abuse. Therefore, after the final experiments, the authors concluded that experts in two aspects of human-computer interaction and machine learning need to work together. The two interact better together to achieve better results.
First of all, after reading this article, I think that not only the explanatory tools of machine learning will make people over-trusted, including machine learning itself will also make people over-trusted, which may be caused by many aspects such as data sets. This reminds me of the course project I wanted to do this semester. My original intention was because a single, standard data set written by a large number of experts for a long time would cause the trained model to produce too high an accuracy rate, so the data set generated by crowdsourcing was used. Can get better results.
Secondly, for this article, I very much agree with the final solution proposed by the author, which is to better integrate the two aspects of human-machine interaction and machine learning as future research directions. This is because these interpretive tools are a visual display of the results. The better design of human-computer interaction allows users to better extract the results of machine learning, better understand the results, and understand the problems in them. Instead of overly trusting the results of machine learning. The future development direction is definitely that fewer and fewer users understand machine learning, but there will be more people using machine learning, and machine learning will become more and more instrumental, so I think that the interaction aspect will be made more Good for users to understand their results. On the other hand, machine learning should be more diverse and able to adapt to more application scenarios. Only when both aspects are done better can the effects of these tools be achieved.
- Is machine learning more academic or tool-oriented in the future?
- If the user does not know the meaning of the results, how to understand the accuracy of the results more clearly without using interpretive software
- The article mentioned that in the future, the joint efforts of human-computer interaction and machine learning will be required, and what changes should be made in human-computer interaction.
