In this paper, the authors focused on fairness of machine learning system. As machine learning has been widely applied, it is important to let the models judge fairly, which needs the evaluation from developers, users and general public. With this aim, the authors conduct an empirical study with different generated explanations to understand the meaning of them towards people’s fairness judgement of ML systems. With an experiment involving 160 MTurk workers, they found that the fairness judgement of models is a complicated problem. They found that certain explanations are considered inherently less fair and others will enhance people’s trust of the algorithm, different fairness problems may be more effectively exposed through different styles of explanation and there are individual differences because each person’s unique background and judgment criteria. Finally, they suggested that the evaluation should support different needs of fairness judgment and consider individual differences.
Reflections:
This paper provides me three main thoughts. Firstly, we should pay attention to explain the machine learning models. Secondly, we should consider different needs when evaluating the fairness of the models. Finally, when train models or design human-AI interactions, we should consider the users’ individual differences.
For the first point, the models are well trained and can perform well. However, the public may not trust them as they know little about the algorithms. If we can provide them fair and friendly explanations, the public trust in the output of machine learning systems may be increased. Also, if they are provided explanations, they may propose suggestions related to practical situations, which will improve the accuracy and fairness of the systems reversely. Due to this, all the machine learning system developers should pay more attention to write appropriate explanations.
Secondly, the explanation and the models should consider different needs. For the experienced data scientists, we could leave comprehensive explanations to let them able to dig deeper. For the people who are experiencing machine learning system for the first time, we should leave user-friendly and easy to understand explanations. For the systems which will be used by users with different backgrounds, we may need to write different versions of explanations, for example, one user-instruction and one developer instruction.
For the third point, which is the most complicated one, it is hard to implement a system which will satisfy people with different judgments. Actually, I am thinking if it is possible to develop systems with interface for users to input their bias, which may solve this problem a little bit. It is not possible to train a model which will satisfy everyone’s preference. As a result, we could only train a model which will make most of the users or majority of the public satisfied or just leave a place to let our users to select their preferences.
Questions:
What kinds of information should we provide to the users in the explanations?
Apart from the crowd-sourcing workers, which groups of people should also be involved in the survey?
What kinds of information you would like to have if you need to judge a system?
For question 2, I think those experts in some regions should also be included, because they must know the situation in some regions, they may have more professional sight for some regions.
Yes, I agree with your ideas. Can you specific which areas should we include experts from? Also, for the experts from these areas, we should provide what kinds of information which may help them with their work?