This paper mainly explores the injustice of the results of machine learning. These injustices are usually reflected in gender and race, so in order to make the results of machine learning better serve people, the author of the paper conducted an empirical study with four types of programmatically generated explanations to understand how they impact people’s fairness judgments of ML systems. In the experiment, these four interpretations have different characteristics, and after the experiment, the author has the following findings:

1. Some interpretations are inherently considered unfair, while others can increase people’s confidence in the fairness of the algorithm;
2. Different interpretations can more effectively expose different fairness issues, such as the model-wide fairness issue and the fairness difference of specific cases.
3. There are differences between people, different people have different positions, and the perspective of understanding things will affect people’s response to different interpretation styles.

In the end, the authors obtained that in order to make the results of machine learning generally fair, in different situations, different corrections are needed and differences between people must be taken into account.

Reflection：

In another class this semester, the teacher gave three reading materials on the results of machine learning and increased discrimination. In the discussion of those three articles, I remember that most students thought that the reason for discrimination should not be Is the inaccuracy of the algorithm or model, and I even think that machine learning is to objectively analyze things and display the results, and the main reason that people feel uncomfortable and even feel immoral in the face of the results is that people are not willing to face these results. It is often difficult for people to have a clear understanding of the whole picture of things, and when these unnoticed places are moved to the table, people will be shocked or even condemn others, but it is difficult to really think about the cause of things. But after reading this paper, I think my previous understanding was narrow: First, the results of the algorithm and the interpretation of the results must be wrong and discriminatory in some cases. So only if we resolve this discrimination can the results of machine learning be able to better serve people. At the same time, I also agree with the ideas and conclusions in the article. Different interpretation methods and different emphasis will indeed affect the fairness of interpretation. All the prerequisites to eliminate injustices are to understand the causes of these injustices. At the same time, I think the main solution to eliminate injustice is still on the researcher. Reason why I think computer is fascinating is it can always keep things rational and objective to deal with problems. People’s response to different results and the influence of different people on different model predictions are the key to eliminating this injustice. Of course, I think people will think that part of the cause of injustice is also the injustice of our own society. When people think that the results of machine learning carry discrimination based on race, sex, religion, etc., should we think about this discrimination itself, should we pay more attention to gender equality, ethnic equality and how to make the results look better.

Question:

1. Do you think that this unfairness is more because the results of machine learning mislead people or it is existed in people’s society for a long time.
2. The article proposes that in order to get more fair results, more people need to be considered, what changes should users make.
3. How to combine the points of different machine learning explanations to create a fairer explanation.

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A big concern with the usage of current ML systems is the issue of fairness and bias when making their decisions. Bias can creep into ML decisions through either the design of the algorithm or through training datasets that are labeled in a way to bias against certain kinds of things. The example used in this paper is the bias against African Americans in an ML system used by judges to predict the probability of a person re-offending after committing a crime. Fairness is hard to judge when ML systems are black boxes so this paper proposes that if ML systems expose reasons behind the decisions (i.e. the idea of explainable AI), a better judgement of the fairness of the decision can be made by the user. To this end, this paper examines the effect of four of different kinds of explanations of the ML decisions on people’s judgements of the fairness of that decision.

I believe this is a very timely and necessary paper in these times, with ML systems being used more and more for sensitive and life changing decisions. It is probably impossible to stop people from adopting these systems so the next best thing is making explainability of the ML decisions mandatory, so people can see and judge if there was potentially bias in the ML system’s decisions. It is interesting that people were mostly able to perceive that there were fairness issues in the raw data. You would think that that would be hard but the generated explanations may have worked well enough to help with that (though I do wish they could’ve shown an example comparing a raw data point and a processed data point that showed how their pre-processing cleaned things). I did wonder why they didn’t show confidence levels to the users in the evaluation, but their explanation that it was something they could not control for makes sense. People could have different reactions to confidence levels, some thinking that anything less than a 100% is insufficient, while others thinking that 51% is good enough. So keeping it out is a limiting but is logical.

1. What other kinds of generated explanations could be beneficial, outside of the ones used in the paper?
2. Checking for racial bias is an important case for fair AI. In what other areas is fairness and bias correction in AI critical?
3. What would be ways that you could mitigate any inherent racial bias of the users who are using explainable AI, when they are making their decisions?

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Summary

            Dodge et al. cover a terribly important issue with artificial intelligence programs and biases from historical datasets, and how to mitigate the inherent racism or other biases within. They also work understand how to better communicate why AIs reach the recommendations they do and how. In an experiment, they look at communicating outcomes from a known biased ML model for predicting recidivism amongst released prisoners called COMPAS. They cleaned the ML model to make race less impactful to the final decision, and then produced 4 ways of explaining the result of the model to 160 mTurk workers: Sensitivity, Input-influence, Case, and demographic. “Input” emphasizes how much each input affected the results, “Demographic” describes how each demographic affects the results, “Sensitivity” shows what flipped demographics would have changed the results, and “Case” finds the most similar cases and details those results. They found that local-based explanations (case and sensitivity) had the largest impact on perceived fairness.

Personal Reflection

This study was pretty interesting to me based on it actually trying to adjust for the biases of input data as well as understanding how to better convey insights from less-biases systems. I am still unsure that the authors removed all bias from the COMPAS system but seeing that they did lower the coefficient significantly shows that it was working on it. In this vein, the paper made me want to read the paper they cited as how they could mitigate biases in these algorithms.

I found their various methods on how to communicate how the algorithm came to its recommendation to be rather incisive. I wasn’t surprised that people found that when the sensitivity explanation said that if the individual’s race was flipped the decision would be flipped lead to more perceived issues with the ML decision. That method of communication seems to lead people to see issues with the dataset more easily in general.

The last notable part of the experiment is that they didn’t give a confidence value for each case – they stated that they could not control for it and so did not present it to participants. That seems like an important part of making a decision based on the algorithm. If the algorithm is really on the fence, but has to recommend one way or the other, it might make it easier to state that the algorithm is biased.

Questions

1. Would removing the race (or other non-controllable biases) coefficient altogether affect the results too much? Is there merit in zero-ing out the coefficient of these factors?
2. Having an attention check in the mTurk workflow is, in itself, not surprising. However, the fact that all of the crowdworkers passed the check is surprising. What does this mean for other work that ignores a subset of data assuming the crowdworkers weren’t paying attention? (Like the paper last week that ignored the lowest quartile of results)
3.  What combination of the four different types would be most effective? If you presented more than one type, would it have affected the results?
4. Do you think showing the confidence value for the algorithm would impact the results significantly?  Why or why not?

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Summary:
“Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment” by Dodge et. al. talks about explainable machine learning and how to ensure fairness. They conduct an empirical study involving around 160 Amazon Mechanical Turk workers. They demonstrate that certain explanations are considered “inherently less fair” while others may help in enhancing the people’s confidence in the fairness of algorithms. They also talk about different kinds of model interpretability; (i) model wide fairness and (ii)case-specific fairness discrepancies. They also show that people react differently to different styles of explanations based on individual differences. They conclude with a discussion on how to provide personalized and adaptive explanations. There are 21 different definitions of fairness. In general, fairness can be defined as “….discrimination is considered to be present if for two individuals that have the same characteristic relevant to the decision making and differ only in the sensitive attribute (e.g., gender/race) a model results in different decisions”. Disparate impact is the consequence of deploying unfair models where one protected group is affected negatively compared to the protected group. This paper talks about the explanation given by machine learning models and how such models can be inherently fair or unfair.

Reflections:
The researchers attempt to answer three primary research questions: (i) How do different styles of explanation impact fairness judgment of an ML system? They study in depth if certain ML models are more effective in teasing out the unfairness of the models. They also analyze if some explanations are inherently fairer. The second questions that the researchers tackle are (ii) How do individual factors in cognitive style and prior position on algorithmic fairness impact the fairness judgment with regard to different explanations? Lastly, the researchers question the benefits and the drawbacks of different explanations in supporting fairness judgment of ML systems? The researchers offer various explanations that can be based on input features, demographic features, sensitive features, and case-based explanations. The authors conduct an online survey and ask participant different questions. However, an individual’s background might also influence the answers given by the mechanical turkers. The authors perform a qualitative as well as quantitative analysis. One of the major limitations of this work is that the analysis was performed by crowd workers with limited experience whereas in real life the decision is made by lawyers. Additionally, the authors could have used LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive Values) values for offering post-hoc explanations. The authors have also not studied an important element which is the confidence as they did not control for it.

Questions:
1. Which other model is unfair? Give some examples?
2. Are race and gender the only sensitive attributes? Can models discriminate based on some other attribute? If yes, which ones?
3. Who is responsible for building unfair ML models?
4. Are explanations of unfair models enough? Does that build enough confidence in the model?
5. Can you think of any adverse effects of providing model explanations?

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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?

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