Summary

Diakopoulos’s paper makes the point that AI has a power over users that is not often clearly expressed to the users, even when those algorithms have massive amounts of power over users’ lives. The author then points out four different ways algorithms have power over users: prioritization, classification, association, and filtering. After a brief description of each, the author then speaks about how transparency is key to the balancing of these powers.

Then a series of AI implementations were discussed and showed how they exerted some amount of power without informing the user. The author used autocompletions on Google & Bing, autocorrections on iPhone, political emails, price discrimination, and stock trading as examples. The author then uses interviews in order to gain insight in how journalists better understand AIs and write stories on them. This is a form of accountability, and journalists use this information to allow users to understand the technology around them.

Personal Reflection

               I thought this paper brought up a good point that was seen in other readings this week: even if the user is given agency over the final decision, the AI biases them towards a particular set of actions. Even if the weaknesses of the AI are understood, like in the Bansal et al. paper on updates, the participant is still biased from the actions and recommendations of the AI. This power, when combined with the effect it can have on peoples’ lives, can greatly change the course of lives.

               The author also makes a point that interviews with designers is a form of reverse-engineering. I had not thought of it in this way before, so it was an interesting insight into journalism. Furthermore, the idea that AIs are black boxes, but their inputs and outputs can be manipulated such that the interior workings can be better understood was another thing I hadn’t thought of.

               I was actually aware of most of the cases the author presented as ways of algorithms exerting power. I have used different computers and safe modes on browsers in the past to ensure I was getting the best deal in travel or hotels before, for example.

               Lastly, I thought the idea of journalists having to uncover these AI (potential) malpractices was an interesting quandary. Once they do this, they must publish a story, but then most people will likely not hear about it. There’s an interesting problem here of how to warn people about potential red flags in algorithms that I felt the paper didn’t discuss well enough.

Questions

1. Are there any specific algorithms that have biased you in the past? How did they? Was it a net positive, or net negative result? What type of algorithmic power did it exert?
2. Which of the four types of algorithmic power is the most serious, in your opinion? Which is the least?
3. Did any of the cases surprise you? Do they change how you may use technology in the future?
4. What ways can the users abuse the AI systems?

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Summary

Diakopoulos’s paper “Algorithmic Accountability” explores the broad question of how algorithms exert their potential power and are worthy of scrutiny by journalists and studies the role of computational approaches like reverse engineering in articulating algorithmic transparency. Nowadays, Automated decision-making algorithms are now used throughout businesses and governments. Given that such algorithmically informed decisions have the potential for significant societal impact, the goal of this paper is to address algorithmic accountability reported as a mechanism for articulating and elucidating the power structures, biases, and impacts that automated algorithms exercise in our society. Through the use of reverse engineering methods, the researcher conducted five cases of algorithmic accountability reporting, including autocompletion, autocorrection, political email targeting, price discrimination, and executive stock trading plans. Also, the applicability of transparency policies for algorithms is discussed along with the challenges of conducting algorithmic accountability as a broadly viable investigative method.

Reflection

I think this paper touches upon the important research question on the accountability of computational artifacts. Our society currently relies on automated decision-making algorithms on many different aspects, ranging from dynamic pricing to employment practices to criminal sentencing. It is important that developers, product managers, and company/government decision-makers are aware of the possible negative social impacts and necessity for public accountability when they design or implement algorithmic systems.

This research also makes me think about whether we need to be that strict with every algorithmic system. I think to answer the question we need to consider different application scenarios, which are not fully discussed in the paper. Take the object detection problem in the computer vision research area, for example, we have two application scenarios: one is to detect if there is a car in the image for automatic labeling, the other is to check if there is any tumor in the computed tomography for disease diagnosis. Apparently, the level of algorithm accountability is required to be much higher in the second scenario. Hence, in my opinion, the accountability of algorithms needs to be discussed under the application scenarios associated with the user’s expectations and potential consequences when the algorithms go wrong. 

The topic of this research is algorithmic accountability. As far as I am concerned, accountability is a wide scope of concept, including but not limited to an obligation to report, explain, and justify algorithmic decision-making as well as mitigate any potential harms. However, I feel this paper mainly focuses on the accountability aspect of the problem with little discussion on other aspects. There is no denying the fact that transparency is one-way algorithms can be made accountable, but just as the paper puts it, “[t]ransparency is far from a complete solution to balancing algorithmic power.” I think other aspects such as responsibility, fairness, and accuracy are worthy of further exploration as well. Considering these aspects throughout the design, implementation, and release cycles of algorithmic system development would lead to a more socially responsible deployment of algorithms.

Discussion

I think the following questions are worthy of further discussion.

• What aspects other than transparency you think would be important in the big picture of algorithmic accountability?
• Can you think about some domain applications that would hardly let automated algorithms make decisions for humans?
• Do you think transparency potentially leaves the algorithm open to manipulation and vulnerable to adversarial attacks? Why or why not?
• Who should be responsible if algorithmic systems make mistakes or have undesired consequences?

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Authors: Nicholas Diakopoulos

Summary

This paper discusses the challenges involved in algorithmic accountability reporting and the reverse engineering approaches used to frame a story. The author interviewed four journalists who have reported on algorithms, and discusses five different case studies to present the methods and challenges involved. Finally, the paper outlines the need for transparency and potential ethical issues.

Reflection

This paper offers great insights into the decision-making process behind the reporting of different algorithms and applications. It is particularly interesting to see the lengths journalists go to figure out the story and the value in reporting. The paper is a great read even for non-technical folks as it introduces the concepts of association, filtering, classification and prioritization with examples that can be understood universally. While discussing the different case studies, the paper manages to paint a picture of the challenges the journalists encountered in a very easy-to-understand manner (e.g. incorrectly determining that Obama’s campaign targeted by age) and therefore, succeeds in showing why reporting on algorithmic accountability is hard!

In most cases, the space for potential input(s) is large enough not to be figured out easily, making the field more challenging. This somehow necessitates using the skills of computational social scientists to conduct additional studies, collect additional data and come up with inferences. The paper makes a great point about reverse engineering offering more insights than directly asking the algorithm developers, as the unintended consequences would never surface without investigating the algorithms in operation. Another case of “we need more longitudinal studies with ecological validity”!

It was very interesting to see the discussion around last-mile interventions at the user interface stages (in case of the autocomplete case). It shows the fact that (some of the) developers are self-aware and therefore, ensure that the user experience is an ethical experience. Even though they may fall short, it’s a good starting point. This also demonstrates why augmenting an existing pipeline (be it data/AI APIs or models) to make it work for the end-user is desirable (something that some of the papers discussed in the class have shown).

The questions around the ethics, as usual, do not have an easy answer — whether the reporting can enable developers to make it difficult to investigate in the future. However, regulations around transparency can go a long way in holding algorithms accountable. The paper does a great job synthesizing the challenges in all the case studies and outlines four high-level points for how algorithms can become transparent.

Questions

1. Would you add anything more to the reverse engineering approaches discussed for the different case studies in the paper? Would you have done anything differently?
2. If you were to investigate into the power structures of an algorithm, which application/algorithm would you chose? What methods would you follow?
3. Any interesting case studies that this paper misses out on?

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Summary: in this paper, the author has mentioned that, in modern society, automated algorithms have become more and more important, and algorithms gradually regulate all aspects of our lives, but the outline of their functions may still be difficult to grasp. So, it is necessary to elucidating and articulating the algorithms’ power. The author proposes a new notion “algorithmic accountability reporting”. This concept can reveal how algorithms work, and it is well worth reviewing by computing journalists. The author explores methods such as transparency and reverse engineering, and how they can be useful in elucidating algorithmic capabilities. And the author analyzes the case studies of five journalists on algorithm research, and describes the challenges and opportunities they face when working on algorithm accountability. The final concept proposed by the author has highlights and main contributions: (1) It proposed the theoretical lens of various atomic algorithm decisions. These decisions raised some major issues that can guide algorithm research and algorithm transparency policy development. (2) It can conduct preliminary evaluation and analysis of the algorithm through algorithmic accountability, including various restrictions. and author discuss the challenges faced in adopting this reporting method, including human resources, legitimacy and ethics, and look ahead to how journalists themselves use transparency when using algorithms

Reflection: I think the author has put forward a very innovative idea. This is also the first point that comes to my mind when I meet or use some new algorithms: what is the boundary of this algorithm, and what scope can it be applied to. For example, the insurance algorithm of an insurance company, we all know that the insurance cost is generated based on a series of attributes, but people are often uncertain about the proportion of each attribute in the insurance algorithm, then there will be some doubts about the results, and even think some results are immoral. Therefore, it is very important to study the capabilities and boundaries of an algorithm.

At the same time, the concept of reverse engineering is mentioned in the article, that is, the ability to study algorithms by studying input and output, but there are often such mechanisms in some websites. It makes the algorithm dynamic, so we need other methods to solve this kind of problem. However, once the input-output relationship of the black box is determined, the challenge becomes a data-driven search for news stories. Therefore, I think the algorithm is more inclined to understand whether there is an unreasonable situation in an algorithm, and the root cause of this unreasonable situation is whether it is caused by man or negligence, or it is people’s deep-rooted ideas . So, in some aspects, I think exploring the borders of algorithms is exploring the morality of algorithms. Therefore, I think this article provides a framework for reviewing the morality of the algorithm. This method can effectively explore a place where the algorithm is unreasonable, and for news reporters, it can be used to discover meaningful news.

In addition, I think the framework described in this article is a special way of human-computer interaction, that is, people study the machine itself, and understand the process of algorithm operation through the feedback of the machine. This also broadened my understanding of human-computer interaction.

Problem:

1. Do you think the framework mentioned in the paper can be used in detecting the ethic issues of an algorithm?
2. Can this system be used in a automatic system to elucidating and articulating the algorithms’ power?
3. Is there any other value of detecting algorithms’ power except news value?

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Summary 

The paper provides a perspective on algorithmic accountability from the journalists’ eyes. The motivation of the paper is to detect how algorithms influence various decisions in different cases. The author investigates explicitly the area of computational journalism and how such journalists could use their power to “scrutinize” to uncover bias and other issues current algorithms pose. He lists out a few of the decisions that algorithms make and which has the potential to affect the algorithms capability to be unbiased. Some of the decisions are classification, prioritization, association, filtering, and algorithmic accountability. It is also mentioned that transparency is a key factor in building trust in an algorithm. The author then proceeds to discuss reverse engineering by providing examples of a few case studies. Reverse engineering is described in the paper as a way by which the computational journalists have reverse engineered to the algorithm. Finally, he points out all the challenges the method poses in the present scenario.

Reflection

The paper gives a unique perspective on the algorithmic bias from a computational journalists’ perspective. Most of the papers we read come from either completely the computational domain or the human-in-the-loop perspective. Having journalists who are not directly involved in the matter is, in my opinion, brilliant. This is because journalists are trained to be unbiased. From the CS perspective, we tend to be “AI” lovers and want to defend the machine’s decision and consider it as true. The humans using the system wither blindly trust them or completely doubt them. Journalists, on the other hand, are always motivated to seek the truth, however unpleasant it might be. Having said that, I am intrigued to know the computational expertise level of the journalists. Although having an-in-depth knowledge about AI systems might introduce a separate kind of bias. Nonetheless, this would be a valid experiment to conduct. 

The challenges that the author mentioned include ethics, legality, among others. These are some of the challenges that are not normally discussed. We, from the computational side, need to be aware of these challenges. The “legal ramification” could be enormous if we do not end up using authorized data to train the model and then publish the results. 

I agree with the author that transparency indeed helps bolster confidence in an algorithm. However, I also agree that it is difficult for companies to be transparent in the modern digital competitive era. It would be difficult for companies to take the risk and make all the decisions public. I believe there might be a middle ground for companies; they could publish part of the algorithmic decisions like the features they use and let the users know what data is being used. This might help improve trust. For example, Facebook could publish the reasons why they recommend a particular post, etc.

Questions

1. Although the paper talks about using computational journalism, how in-depth is the computational knowledge of such people? 
2. Is there a way for an algorithm to be transparent, yet the company not lose its competitive edge?
3. Have you considered the “legal and ethical” aspect of your course project? I am curious to know about the data that is being used and other models etc.?

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Summary

With the prevalence of technology, the mainstream programs that help the rise of it not only dictate the technological impact but also the direction of news media and people’s opinions. With journalists turning to various outlets and adapting to the efficiency created by technology, the technology used may introduce bias based on their internal sources or efficiencies and therefor introduce bias into their story. This team measured multiple algorithms against four different categories: prioritization, classification, association, and filtering. Using a combination of these different categories, these are then measured within a user survey to measure how different auto complete features bias their opinions. Using these measurements, it has also been determined by the team that popular search engines like Google specifically tailor results based on other information the user has previously searched. For a normal user this makes sense however for some investigative journalist these results may not accurately represent a source of truth. 

Reflection

Noted by the team, there is a strong conflict in the transparency used within an algorithm. These transparency discrepancies may be due to certain government concerns dependent on certain secrets. These creates a strong sense of resiliency and distrust against the use of certain algorithms based. Though these secrets are claimed for national security, there may be misuse of power or overstepping of definition that overuses the term for personal or political gain and are not correctly appropriated. These kinds of acts may be located at any level of government, from the lowest of actors to the highest of rankings.  

One of the key discussion points raised by the team to fix this potential bias in independent research is to teach journalists how better to use computer systems. This may only seem to bridge the journalist’s new medium they are not familiar with. This could also be seen as an attempt to create a handicap for the journalists to better understand a truly fragmented news system. 

Questions

• Do you think introducing journalists into a computer science program would extend their capabilities or it would only further direct their ideas while potentially removing certain creativity? 
• Since there is a type of monopolization throughout the software ecosystem, do you believe people are “forced” to use such technologies that tailor the results? 

• Given how a lot of technology uses user information for potential misuse, do you agree with this information being introduced with a small disclaimer acknowledging the potential preference? 
• There are a lot of services that offer you better insights to clean your internet trail and clear any biases internet services cache to ensure a faster and more tailored search results. Have you personally used any of these programs or step by step guides to clean your internet footprint? 
• Many programs capture and record user usage with a small disclaimer at the end detailing their usage on data. It is likely many users do not read these for various reasons. Do you think if normal consumers of technology were to see how corrective and auto biasing the results could be that they would continue using the services? 

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Algorithms have pervaded our every day lives, because computers have become essential in our every day lives. Their pervasion also means that they need to be closely scrutinized to ensure that they are functioning like they should, without bias, obeying the guarantees the creators have promised. Algorithmic Accountability is a category of journalism where the journalists investigate these algorithms to validate their claims and find if there are any violations. The goal is to find mistakes and omissions or bias creeping into the algorithms because though computers do exactly what they’re told, they are still created by humans with blinspots. They classify the four kinds of decisions that algorithm decision making falls under. They claim that transparency alone is not enough because full transparency can often be prevented by trade secret excuses. They utilize the idea of reverse engineering where they put in inputs and observe the outputs, without looking at the inner workings because journalists are often dealing with black box algorithms. They look at five case studies of journalists who’ve done such investigations with reverse engineering, as well as putting a theory and a methodology on how to find news-worthy stories in this space.

This paper is a very interesting look from the non CS/HCI perspective of studying how algorithms function in our lives. This paper, coming from the perspective of journalism and looking at the painstaking way journalists investigate these algorithms. Though not the focus, this work also brings to light the incredible roadblocks that come with investigating proprietary software, especially those from large secretive companies who would leverage laws and expensive lawyers to fight such investigations if it is not in their favor. In an ideal world, everyone would have integrity and would disclose all the flaws in their algorithms but that’s unfortunately not the case which is why the work these journalists are doing is important, especially when they don’t have easy access to the algorithms they’re investigating, and sometimes don’t have access to the right inputs. There is a danger here that a journalist could end up being discredited because they did the best investigation they could with the limited resources they have but the PR team of the company they’re investigating latches on to a poor assumption or two to discredit the otherwise good work. The difficulty in performing these investigations, especially for journalists who may not have prior training or experience in dealing with computers, exemplifies the need for at least some computer science education for everyone so that they can better understand the systems they’re dealing with and have a better handle on running investigations as algorithms pervade even in our lives.

1. Do you think some of the laws in place that allow companies to obfuscate their algorithms should be relaxed to allow easier investigation?
2. Do you think current journalistic protections are enough for journalists investigating these algorithms?
3. What kind of tools or training can be given to journalists to make it easier for them to navigate this world of investigating algorithms?

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In this report, the author studied about how algorithms execute and are worthy of scrutiny by computational journalists. He used methods such as transparency and reverse engineering to analyze the algorithms. Also, he analyzed five kinds of atomic decisions, including prioritization, classification, association, filtering, and algorithmic accountability, to assess algorithmic power. For the reverse engineering part, he analyzed numerous daily cases and presented a new scenario of reverse engineering which considers both inputs and outputs. He considered the variable observability of I/O relationships and identifying, sampling, and finding newsworthy stories about algorithms. Finally, the author discussed challenges that may be faced by the application of algorithmic accountability reporting in the future. Also, he proposed that transparency can be used to effectively force applications to take journalistic norms when newsroom algorithms are applied.

Reflections:

I am really interested in the reverse engineering part of this report. The author concluded different cases of researchers doing reverse engineering towards algorithms. It is quite exciting to understand the opportunities and limitations of the reverse engineering approach to investigating algorithms. And, reverse engineering is valuable in explaining how algorithms work and finding limitations of the algorithms. As many current applied algorithms or models are trained using unsupervised learning or deep learning, it is hard for us to understand and explain them. We can only use metrics like recall or precision to evaluate them. But with reverse engineering, we can know about how the algorithms work and modify them to avoid limitations and potential discriminations. However, I think there may be some ethical issues in reverse engineering. When some bad guys did reverse engineering to some applications, they can steal the ideas in the developed applications or algorithms. Or, they may bypass the security system of the application making use of the drawbacks they found using reverse engineering.

For the algorithmic transparency, I felt that I paid little attention to this principle before. I used to only consider whether the algorithm works or not. However, after reading this report, I felt that algorithmic transparency is an important aspect of system building and maintenance. Instead of letting researchers employing reverse engineering to find the limitations of systems, it is better to make some part of the algorithms, the use of the algorithms and some other data to the public. On one hand, this will raise the public trust of the system due to its transparency. On the other hand, experts from outside the company or the organization can make a contribution to the improvement and secure the system. However, currently, transparency is far from a complete solution to balancing algorithmic power. Apart from the author’s idea that researchers can apply reverse engineering to analyze the systems, I think both corporations and governments can pay more attention to the transparency of the algorithms.

Questions:

I am still confused about how to find the story behind the input-output relationship after reading the report. How can we find out how the algorithm operates with an input-output map?

How can we avoid crackers making use of reverse engineering to do attacks?

Apart from journalists, which groups of people should also employ reverse engineering to analyze systems?

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Summary:

In this paper, the author first presents the algorithmic power, prioritization, classification, association, as well as filtering. Then the author concludes based on the description of algorithmic power that a significant number of humans would be influenced by algorithms outcome. Thus, the author made the point that it is significant to interpreting the output of algorithms in the course of making higher-level decisions. Next, the author examined the possibility and weaknesses of requiring algorithm transparency. Therefore, the paper introduces a replaced method called reverse engineering. In this work, journalistics combined the interviews, document reviews as well as reverse engineering analysis to shed light on the algorithms’ functioning. They introduced five cases of studies of journalistic investigations and also presented the challenges and opportunities for doing algorithmic accountability work. The primary process of the inquiry includes identifying newsworthy algorithm, sampling the input-output relationships to study the correlations, and finally seeking a story. Finally, the author provides a series of suggestions regarding the transparency policy for algorithms.

Reflection:

First, algorithm accountability is not a new topic nowadays. With the penetration of algorithms into our lives, the application of algorithms to all walks of life, not only for entertainment, learning, daily tools, but even for the significant impact on our experiences of security issues, privacy issues, and even the distribution of social resources. People are starting to ask the question, can algorithms be trusted? To what extent are they trustworthy? I have also seen many examples of guessing and analyzing the internal structure of such a black box. I want to share one of them.

The approach of reversing engineering, especially the process that sampling the input-output relationships of the algorithms to study the correlations, remind me of a news report which identifies the bias from the algorithm. That algorithm was designed for individual risk assessment, which is predicting the likelihood of each committing a future crime. It has been increasingly common in courtrooms across the nation, but in 2014, it was accused that the risk scores might be injecting bias into the courts. The way people found the bias in the algorithm is the same as reverse engineering. Here’s there finding in that paper ( I also put the link below):

• The formula was particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants.
• White defendants were mislabeled as low risk more often than black defendants.

Based on this outcome, it seems that reverse engineering is essential and efficient. I think this is a better way to examine the algorithm accountability than transparency. As mentioned in this article, leaving aside the trade secrets problem, disclose the source code of algorithms might helpful for specialists but does not able to improve user experience since they may not make meaningful choices considering their lack of expertise. Thus, identify the issue of algorithms instead of focus on the implementation process is more efficient in encouraging the designer to perfect the algorithm.

Questions:

1. Do you think the algorithm is trustworthy? How much confidence do you have in an algorithm?
2. What do you think about transparency? How transparent do you think the algorithm should be?
3. What do you think of reverse engineering? Does it work? Do you have any other examples regarding this approach?

Link:https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

Word Count: 544

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