03/04/2020 – Vikram Mohanty – Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind

Vikram Mohanty Leave a comment

Authors: Elliot Salisbury, Ece Kamar, and Meredith Ringel Morris

Summary

This paper studies how crowdsourcing can be used to evaluate automated approaches for generating alt-text captions for BVI (Blind or Visually Impaired) users on social media. Further, the paper proposes an effective real-time crowdsourcing workflow to assist BVI users in interpreting captions. The paper shows that the shortcomings of existing AI image captioning systems frequently hinder a user’s understanding of an image they cannot see, much to the extent that clarifying conversations with sighted assistants can’t even correct. The paper finally proposes a detailed set of guidelines for future iterations of AI captioning systems. 

Reflection

This paper is another example of people working with imperfect AI. Here, the imperfect AI is a result of not relying on collecting meaningful datasets, but as a result of building algorithms from constrained datasets without having a foresight of the application i.e. alt-text for BVI users. The paper demonstrates a successful crowdsourcing workflow augmenting the AI’s suggestion, and serves as a motivation for other HCI researchers to think of design workflows that can integrate the strengths of interfaces, crowds and AI together. 

The paper shows an interesting finding where the simulated BVI users found it easier to generate a caption from scratch than from the AI’s suggestion. This shows how the AI’s suggestion can bias a user’s mental model in the wrong direction, from where recovery might be costlier compared to no suggestion in the first place. This once again stresses the need for considering real-world scenarios and users in the evaluation workflow. 

The solution proposed here is bottlenecked by the challenges presented by real-time deployment with crowd workers. Despite that, the paper makes an interesting contribution in the form of guidelines essential for future iteration of AI captioning systems. Involving potential end-users and proposing systematic goals for an AI to achieve is a desirable goal in the long-run.

Questions

  1. Why do you think people preferred to generate the captions from scratch rather than from the AI’s suggestions? 
  2. Do you ever re-initialize a system’s data/suggestions/recommendations to start from blank? Why or why not? 
  3. If you worked with an imperfect AI (which is more than likely), how do you envision mitigating the shortcomings when you are given the task to redesign the client app? 

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03/04/2020 – Sushmethaa Muhundan – Pull the Plug? Predicting If Computers or Humans Should Segment Images

Sushmethaa Muhundan 1 Comment

The paper proposes a resource allocation framework that intelligently distributes work between a human and an AI system in the context of foreground object segmentation. The advantages of using a mix of both humans and AI rather than either of them alone is demonstrated via the study conducted. The goal is to ensure that high-quality object segmentation results are produced while using considerably less human efforts involved. Two systems are implemented as part of this paper that automatically decide when to transfer control from the human to the AI component and vice versa, depending on the quality of segmentation encountered at each phase. The first system eliminates the need for human annotation effort by replacing human efforts with computers to generate coarse object segmentation which is refined by segmentation tools. The second system predicts the quality of the annotations and automatically identifies a subset of them that needs to be re-annotated by humans. Three diverse datasets were used to train and validate the system and these include datasets representing visible, phase contrast microscopy, and fluorescence microscopy images.

The paper explores leveraging the complementary strengths of humans and AI and allocates resources accordingly in order to reduce human involvement. I particularly liked the focus on quality throughout the paper. This particular system that employs a mixed approach mechanism ensures that the quality of the traditional systems which relied heavily on human involvement is met. The resultant system was successfully able to reduce significant hours of human effort and also maintain the quality of the resultant foreground object segmentation of images which is great.

Another aspect of the paper that I found impressive was the conscious effort to develop a single prediction model that is applicable across different domains. Three diverse datasets were employed as part of this initiative. The paper talks about the disadvantages of other systems that do not work well on multiple datasets. In such cases, only a domain expert or computer vision expert would be able to predict when the system would succeed. This paper claims that this is altogether avoided in this system. Also, the decision to intentionally include humans only once per image is good as opposed to the existing system where human effort is required multiple times during the initial segmentation phase of each image.

  1. This paper primarily focuses on reducing human involvement in the context of foreground object segmentation. What other applications can extend the principles of this system to achieve reduced involvement of humans in the loop while ensuring that quality is not affected?
  2. The system deals with predicting the quality of image segmentation outputs and involves the human to re-annotate only the lowest quality ones. What other ideas can be employed to ensure reduced human efforts in such a system?
  3. The paper implies that the system proposed can be applied across images from multiple domains. Were the three datasets described varied enough to ensure that this is a generalized solution?

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03/04/2020 – Nurendra Choudhary – Combining crowdsourcing and google street view to identify street-level accessibility problems

Nurendra Choudhary Leave a comment

Summary

In this paper, the authors discuss a crowd-sourcing method utilizing Amazon MT workers to identify accessibility issues in google street view images. They utilize two levels of street views for annotations: image-level and pixel-level. They evaluate intra and inter-annotator agreement and conclude a feasible level of accuracy of 81% (increased to 93% with minor quality control additions) for real-world scenarios.

The authors initiate the paper with a discussion about the necessity of such approaches. The solution could lead to more accessibility-aware solutions. The paper utilizes precision, recall and f1-score to consolidate and evaluate image-level annotations. For pixel-level annotations, the authors utilize two sets of evaluation metrics: overlap between annotated pixels and precision-recall scores. The experiments depict an inter-annotator agreement that makes the system feasible in real-world scenarios. The authors also utilize majority voting between annotators to improve the accuracy further.  

Reflection

The paper introduces an interesting approach to utilize crowd-sourced annotations for static image databases. This leads me to question other cheaper sources of images that can be utilized for this purpose. For example, google maps provides a more frequently updated set of images. Also, acquiring these images is more cost-effective. I think this will be a better alternative to the street-view images.

Additionally, the paper adopts majority voting to improve its results. Theoretically, this should lead to perfect accuracy. The method gets 93% accuracy after the addition. I would like to see examples where the method fails. This will enable development of better collation strategies in the future. I understand that in some cases, the image might be too unclear. However, examples of such failures would give us more data to improve the strategies.

Also, the images contain much more data than currently being collected. We can build an interpretable representation of such images that collect all world information contained in the images. However, the computational effectiveness and validity is still questionable. But, if we are able to better information systems, such representations might enable a huge leap forward in the AI research (similar to ImageNet). We can also combine this data to build a profile of any place such that it helps any user that wants to access it in the future (e.g.; accessibility of restaurants or schools). Furthermore, given the time-sensitivity of accessibility, I think a dynamic model will be better than the proposed static approach. However, this will require a cheaper method of acquiring street-view data. Hence, we need to look for alternative sources of data that may provide comparable performance while limiting the expenses.

Questions

  1. What is the generalization of this method? Can this be applied to any static image database? The paper focuses on accessibility issues. Can this be extended to other issues such as road repairs and emergency lane systems?
  2. Street view data collection requires significant effort and is also expensive. Could we utilize Google maps to achieve reasonable results? What is a possible limitation to applying the same approach on Google satellite imagery?
  3. What about the time sensitivity of the approach? How will it track real-time changes to the system? Does this approach require constant monitoring?
  4. The images contain much more information. How can we exploit it? Can we use it to detect infrastructural issues with government services such as parks, schools, roads etc.? 

Word Count: 560

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02/26/20 – Fanglan Chen – Will You Accept an Imperfect AI? Exploring Designs For Adjusting End-user Expectations of AI Systems

Fanglan Chen 4 Comments

Summary

Kocielnik et al.’s paper “Will You Accept an Imperfect AI?” explores approaches for shaping expectations of end-users before their initial working with an AI system and studies how appropriate expectations impact users’ acceptance of the system. Prior study has presented that end-user expectations of AI-powered technologies are influenced by various factors, such as external information, knowledge and understanding, and first hand experience. The researchers indicate that expectations vary among users and users perception/acceptance of AI systems may be negatively impacted when their expectations are set too high. To fill in the gap of understanding how end-user expectations can be directly and explicitly impacted, the researchers use a Scheduling Assistant – an AI system for automatic meeting schedule detection in email – to study the impact of several methods of expectation shaping. Specifically, they explore two system versions with the same accuracy level of the classifier but each is intended to focus on mitigating different types of errors(False Positives and False Negatives). Based on their study, error types highly relate to users’ subjective perceptions of accuracy and acceptance. Expectation adjustment techniques are proposed to make users fully aware of AI imperfections and enhance their acceptance of AI systems.

Reflection

We need to be aware that AI-based technologies cannot be perfect, just like nobody is perfect. Hence, there is no point setting a goal that involves AI systems making no mistake. Realistically defining what success and failure look like associated with working with AI-powered technologies is of great importance in adopting AI to improve the imperfection of nowadays solutions. That calls for an accurate positioning of where AI sits in the bigger picture. I feel the paper mainly focuses on how to set appropriate expectations but lacks a discussion on different scenarios associated with the users expectations to AI. For example, users expectation greatly vary to the same AI system in different decision making frameworks: in human-centric decision making process, the expectation of AI component is comparatively low as AI’s role is more like a counselor who is allowed to make some mistakes; in machine-centric system, all the decisions are made by algorithms which render users’ low tolerance of errors, simply put, some AIs will require more attention than others, because the impact of errors or cost of failures will be higher. Expectations of AI systems vary not only among different users but also under various usage scenarios.

To generate positive user experiences, AI needs to exceed expectations. One simple way to achieve this is to not over-promise the performance of AI in the beginning. That relates with the intention of the researchers on designing the Accuracy Indicator component in the Scheduling Assistant. In the study case, they set the accuracy to 50%. This accuracy is actually very low in AI-based applications. I’m interested in whether the evaluation results would change with AI systems of higher performance (e.g. 70% or 90% in accuracy). I think it is worthwhile to conduct a survey about users’ general expectations of AI-based systems. 

Interpretability of AI is another key component that shapes user experiences. If people cannot understand how AI works or how it comes up with its solutions, and in turn do not trust it, they would probably not choose to use it. As people accumulate more positive experiences, they build trust with AI. In this way, easy-to-interpret models seem to be more promising to deliver success compared with complex black-box models. 

To sum up, by being fully aware of AI’s potential but also its limitations, and developing strategies to set appropriate expectations, users can create positive AI experiences and build trust in an algorithmic approach in decision making processes.

Discussion

I think the following questions are worthy of further discussion.

  • What is your expectation of AI systems in general? 
  • How would users expectations of the same AI system vary in different usage scenarios?
  • What are the negative impacts brought by the inflated expectations? Please give some examples. 
  • How can we determine which type of errors is more severe in an AI system?

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02/26/20 – Lulwah AlKulaib- Explaining Models

Lulwah AlKulaib 3 Comments

Summary

The authors believe that in order to ensure fairness in machine learning systems, it is mandatory to have a human in the loop process. In order to identify fairness problems and make improvements, they suppose relying on developers, users, and the general public is an effective way to follow that process. The paper conducts an empirical study with four types of programmatically generated explanations to understand how they impact people’s fairness judgments of ML systems. They try to answer three research questions:

  • RQ1 How do different styles of explanation impact fairness judgment of a ML system?
  • RQ2 How do individual factors in cognitive style and prior position on algorithmic fairness impact the fairness judgment with regard to different explanations?
  • RQ3 What are the benefits and drawbacks of different explanations in supporting fairness judgment of ML systems?

The authors focus on a racial discrimination case study in terms of model unfairness and Case-specific disparate impact. They performed an experiment with 160 Mechanical Turk workers. Their hypothesis proposed that given local explanations focus on justifying a particular case, they should more effectively surface fairness discrepancies between cases. 

 The authors show that: 

  • Certain explanations are considered inherently less fair, while others can enhance people’s confidence in the fairness of the algorithm
  • Different fairness problems-such as model-wide fairness issues versus case-specific fairness discrepancies-may be more effectively exposed through different styles of explanation
  • Individual differences, including prior positions and judgment criteria of algorithmic fairness, impact how people react to different styles of explanation.

Reflection

This is a really informative paper. I like that it had a straightforward hypothesis and chose one existing case study that they evaluated. But I would have loved to see this addressed with judges instead of crowdworkers. They mentioned it in their limitations and I hope that they find enough judges willing to work on a follow-up paper. I believe that they would have insightful knowledge to contribute especially since they practice it. It would give a more meaningful analysis to the case study itself from professionals in the field.

I also wonder how this might scale to different machine learning systems that cover similar racial biases. Having a specific case study makes it harder to generalize even for something in the same domain. But definitely worth investigating since there are so many existing case studies! I also wonder if changing the case study analyzed, we’d notice a difference in the local vs. global explanations patterns in fairness judgement. And how would a mix of both affect the judgement, too. 

Discussion

  • What are other ways you would approach this case study?
  • What are some explanations that weren’t covered in this study?
  • How would you facilitate this study to be performed with judges?
  • What are other case studies that you could generalize this to with small changes to the hypothesis?

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02/26/20 – Sukrit Venkatagiri – Will You Accept an Imperfect AI?

Sukrit Venkatagiri 2 Comments

Paper: Rafal Kocielnik, Saleema Amershi, and Paul N. Bennett. 2019. Will You Accept an Imperfect AI? Exploring Designs for Adjusting End-user Expectations of AI Systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19), 1–14.

Summary: 

This paper explores people’s perceptions and expectations of an intelligent scheduling assistant. The paper specifically considers three broad research questions: the impact of AI’s focus on error avoidance versus user perception, ways to set appropriate expectations, and impact of expectation setting on user satisfaction and acceptance. The paper explores this through an experimental setup, whose design process is explored in detail. 

The authors find that expectation adjustment designs significantly affected the desired aspects of expectations, similar to what was hypothesized. They also find that high recall resulted in significantly higher perceptions of accuracy and acceptance compared to high precision, and that expectation adjustment worked by intelligible explanations and tweaking model evaluation metrics to emphasize one over the other. The paper concludes with a discussion of the findings.

Reflection:

This paper presents some interesting findings using a relatively simple, yet powerful “technology probe.” I appreciate the thorough exploration of the design space, taking into consideration design principles and how they were modified to meet the required goals. I also appreciate the varied and nuanced research questions. However, I feel like the setup may have been too simple to explore in more depth. Certainly, this is valuable as a formative study, but more work needs to be done. 

It was interesting that people valued high recall over high precision. I wonder if the results would differ among people with varied expertise, from different countries, and from different socioeconomic backgrounds. I also wonder how this might differ based on the application scenario, e.g. AI scheduling assistant versus a movie recommendation system. In the latter, a user would not be aware of what movies they were not recommended but that they would actually like, while with an email scheduling assistant, it is easy to see false negatives.

I wonder how these techniques, such as expectation setting, might apply not only to users’ expectations of AI systems, but also to exploring the interpretability or explainability of more complex ML models.

At what point do explanations tend to result in the opposite effect? I.e. reduced user acceptance and preference? It may be interesting to experimentally study how different levels of explanations and expectation settings affect user perceptions versus a binary value. I also wonder how it might change with people of different backgrounds.

In addition, this experiment was relatively short in duration. I wonder how the findings would change over time. Perhaps users would form inaccurate expectations, or their mental models might be better steered through expectation-setting. More work is needed in this regard. 

Questions:

  1. Will you accept an imperfect AI?
  2. How do you determine how much explanation is enough? How would this work for more complex models?
  3. What other evaluation metrics can be used?
  4. When is high precision valued over high recall, and vice versa?

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02/26/2020 – Palakh Mignonne Jude – Interpreting Interpretability: Understanding Data Scientists’ Use Of Interpretability Tools For Machine Learning

Palakh Mignonne Jude 2 Comments

SUMMARY

In this paper, the authors attempt to study two interpretability tools – the InterpretML implementation of GAMs and the SHAP Python package. They conducted a contextual inquiry and survey of data scientists in order to analyze the ability of these tools to aid in uncovering common issues that arise when evaluating ML models. The results obtained during the course of these studies indicate that data scientists tend to over-trust these tools. The authors conducted pilot interviews with 6 participants to identify common issues faced by data scientists. The contextual inquiry performed included 11 participants who were allowed to explore the dataset and an ML model in a hands-on manner via the use of a Jupyter notebook whereas the survey comprised of 197 participants and was conducted through Qualtrics. For the survey, the participants were given access to a description of the dataset and a tutorial on the interpretability tool they were to use. The authors found that the visualizations provided by the interpretability tools considered in the study as well as the fact that these tools were popular and publicly available caused the data scientists to over-trust these tools.

REFLECTION

I think it is good that the authors performed a study to observe the usage of interpretability tools by data scientists. I was surprised to learn that a large number of these data scientists over-trusted the tools and that visualizations impacted their ability to judge the tools as well. However, considering that the authors state ‘participants relied too heavily on the interpretability tools because they has not encountered such visualizations before’ makes me wonder if the authors should have created a separate pool of data scientists who had better experience with such tools and visualizations and then presented a separate set of results for that set of individuals. I also found it interesting to learn that some participants used the tools to rationalize suspicious observations.

As indicated by the limitations section of this paper, I think a follow-up study that includes a richer dataset as well as interpretability techniques for deep learning would be very interesting to learn about and I wonder how data scientists would use such tools versus the ones studied in this paper.

QUESTIONS

  1. Considering that the complexity of ML systems and the time taken for researchers to truly understand how to interpret ML, both the contextual inquiry as well as the survey was conducted with people who had as little as 2 months of experience with ML. Would a study with experts in the field of ML (all with over 4 years of experience) have yielded different results? Perhaps these data scientists would have been able to better identify issues and would not have over-trusted the interpretable tools?
  2. Would a more extensive study comprise of a number of different (commonly used as well as not-so-commonly used) interpretability tools have changed the results? If the tools were not available so easily would it truly impact the amount of trust the users had for the tools?
  3. Does a correlation exist between the amount of experience a data scientist has and the amount of trust for a given interpretability tool? Would the replacement of visualizations with other representations of interpretations of the models impact the amount of trust the human had towards the tool?

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02/26/20 – Lulwah AlKulaib- Interpretability

Lulwah AlKulaib 1 Comment

Summary

Machine learning (ML) models are integrated in many departments nowadays (for example: criminal justice, healthcare, marketing, etc.). The universal presence of ML has moved beyond academic research and grew into an engineering discipline. Because of that, it is important to interpret ML models and understand how they work by developing interpretability tools. Machine Learning engineers, practitioners, and data scientists have been using these tools. However, due to the minimal evaluation of the extent to which these tools achieve interpretability, the authors study the use of two interpretability tools to uncover issues that arise when building and evaluating models. The interpretability tools are: InterpretML implementation of GAMs and the SHAP Python package. They conduct a contextual inquiry and survey197 data scientists to observe how they use interpretability tools to uncover common issues that arise when building and evaluating ML models. Their results show that data scientists did utilize visualizations produced by interpretability tools to uncover issues in datasets and models. Yet, the availability of these tools has led to researchers over-trust and misuse of them.

Reflection

Machine learning is now being used to address important problems like predicting crime rates in cities to help police distribute manpower, identifying cancerous cells, predicting recidivism in the judiciary system, and locating buildings that are subject to catching on fire. Unfortunately, these models have been shown to learn biases. Detecting these biases is subtle, especially to beginners in the field. I agree with the authors that it is troublesome when machine learning is misused, whether intently or due to ignorance, in situations where ethics and fairness are eminent. Lacking models explainability can lead to biased and ill-informed decisions. In our ethics class, we went over case studies where interpretability was lacking and caused representing racial bias in facial analysis systems [1], biasing recidivism predictions [2], and textual gender biases learned from language [3]. Some of these systems were used in real life and have affected people’s lives. I think that using a similar analysis to the one presented in this paper before deploying systems into practice should be mandatory. It would give developers better understanding of their systems and help them avoid making biased decisions that can be corrected before going into public use. Also, informing developers on how dependable are interpretability tools and when to tell that they’re over trusting them, or when are they misusing them is important. Interpretability is a “new” field to machine learning and I’ve been seeing conferences adding sessions about it lately. I’m interested in learning more about interpretability and how we can adapt it in different machine learning modules.

Discussion

  • Have you used any of the mentioned interpretability packages in your research? How did it help in improving your model?
  • What are case studies that you know of where machine learning bias is evident? Were these biases corrected? If so, How?
  • Do you have any interpretability related resources that you can share with the rest of the class?
  • Do you plan to use these packages in your project? 

References

  1. https://splinternews.com/predictive-policing-the-future-of-crime-fighting-or-t-1793855820
  2. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  3. Bolukbasi, T., Chang, K. W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in neural information processing systems (pp. 4349-4357).

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02/26/2020 – Palakh Mignonne Jude – Explaining Models: An Empirical Study Of How Explanations Impact Fairness Judgment

Palakh Mignonne Jude 1 Comment

SUMMARY

The authors of this paper attempt to study the effect explanations of ML systems have in case of fairness judgement. This work attempts to include multiple aspects and heterogeneous standards in making the fairness judgements that go beyond the evaluation of features. In order to perform this task, they utilize four programmatically generated explanations and conduct a study involving over 160 MTurk workers. They consider the impact caused by different explanation styles – global (influence and demographic-based) as well as local (sensitivity and case-based) explanations, fairness issues including model unfairness and case-specific disparate impact, and the impact of individual difference factors such as cognitive style and prior position. They authors utilized the publicly available COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) data set for predicting risk of recidivism which is known to have racial bias. The authors developed a program to generate different explanation versions for a given data point and conducted an online survey style study wherein the participants were made to judge the fairness of a prediction based on a 1 to 7 Likert scale and had to justify the rating given by them.

REFLECTION

I agree that ML systems are often seen as ‘black boxes’ and that this truly does make gauging fairness issues difficult. I believe that this study conducted was indeed very useful in throwing light upon the need for more well-defined fairness judgement methodologies involving humans as well. I feel that the different explanation styles taken into account in this paper – influence, demographic-based, sensitivity, and case-based were good and helped cover various aspects that could contribute in understanding the fairness of the prediction. I found it interesting to learn that the local explanations helped to better understand discrepancies between disparately impacted cases and non-impacted cases whereas the global explanations were more effective in exposing case-specific fairness issues.

I also found interesting to learn that different regions of the feature space may have varied levels of fairness and fairness issues. Having not considered the fairness aspect of my datasets and the impact this would have on the models I build, this made me realize that it would indeed be important to have more fine-grained sampling methods and explanation designs in order to judge the fairness of ML systems.

QUESTIONS

  1. The participants involved in this study comprised of 78.8% self-identified Caucasian MTurk workers. Considering that the COMPAS dataset being considered in this study is known to have racial bias, would changing the percentage of the African American workers involved in these studies have altered the results? The study focused on workers living in the US, perhaps knowing the general judgement of people living across the world from multiple races may have also been interesting to study?
  2. The authors utilize a logistic regression classifier that is known to be relatively more interpretable. How would a study of this kind extend when it comes to other deep learning systems? Could the programs used to generate explanations be used directly? Has any similar study been performed with these kinds of more complex systems?
  3. As part of the limitations of this study, the authors mention that ‘the study was performed with crowd workers, rather than judges who would be the actual users of this type of tool’. How much would the results vary if this study was conducted with judges? Has any follow-up study been conducted?

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02/26/20 – Fanglan Chen – Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment

Fanglan Chen 1 Comment

Summary

Dodge et al.’s paper “Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment” presents an empirical study on how people make fairness judgments of machine learning systems and how different styles of explanation impact their judgments. Fairness issues of ML systems attract research interests during recent years. Mitigating the unfairness in ML systems is challenging, which requires the good cooperation of  developers, users, and the general public. The researchers state that how explanations are constructed have an impact on users’ confidence in the systems. To further examine the potential impacts on people’s fairness judgments of ML systems, they conduct empirical experiments involving crowdsourcing workers on four types of programmatically generated explanations (influence, demographic-based, sensitivity, and case-based). Their key findings include: 1) some explanations are considered more fair, while others have negative impact on users’ trust of the algorithm in regards of fairness; 2) varied fairness issues (model-wide fairness and case-specific fairness) can be detected more effectively through an examination of different explanation styles; 3) individual differences (prior positions and judgment criteria of algorithmic fairness) lead to how users react to different styles of explanation. 

Reflection

This paper shines light on a very important fact that bias in ML systems can be detected and mitigated. There is a growing attention to the fairness issues in AI-powered technologies in the machine learning research community. Since ML algorithms are widely used to speed up the decision making process in a variety of domains, beyond achieving good performance, they are expected to produce neutral results. There is no denying the fact that algorithms rely on data, “garbage in, garbage out.” Hence, it is incumbent to feed the unbiased data to these systems upon developers in the first place. In many real-world cases, race is actually not used as an input, however, it correlates to other factors that make predictions biased. That case is not as easy as the cases presented in the paper to detect but still requires effort to be corrected. A question here would be in order to counteract this implicit bias, should race be considered and used to calibrate the relative importance of other factors? 

Besides the bias introduced by data input, there are other factors that need to be taken into consideration to deal with the fairness issues in ML systems. Firstly, machine bias can never be neglected. The term bias in the context of the high-stakes tasks (e.g. future criminal prediction) is very important because a false positive decision could have a destructive impact on a person’s life. This is why when an AI system deals with the human subject (in this case human life), the system must be highly precise and accurate and ideally provide reasonable explanation. Making a person’s life harder to live in a society or impacting badly a person’s life due to a flawed computer model is never acceptable. Secondly, the proprietary model is another concern. One thing should be kept in mind that many high-stacks tasks such as future criminal prediction is a matter of public matter and should be transparent and fair. That does not mean that the ML systems used for those tasks need to be completely public and open. However, I believe there should be a regulatory board of experts who can verify and validate the ML systems. More specifically, the experts can verify and validate the risk factors used in a system so that the factors could be widely accepted. They can also verify and validate the algorithmic techniques used in a system so that the system incorporates less bias. 

Discussion

I think the following questions are worthy of further discussion.

  • Besides model unfairness and case-specific disparate impact, are there any other fairness issues?
  • What are the benefits and drawbacks of global and local explanations in supporting fairness judgment of AI systems?
  • Are there any other style or element of explanations that may impact fairness judgement you can think about?
  • If an AI system is not any better than untrained users at predicting recidivism in a fair and accurate way, why do we need the system?

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