Month: October 2020

A Short Case for a Data Marketplace
By Linda Dong, October 23, 2020

In today’s digital, internet age, data is power. Using data, Netflix can generate recommendations, Facebook can tailor advertisements, and Visa can detect fraud. Google can predict your search phrase, Alexa can prompt you to restock household products, and Wealthfront can create your personalized retirement path, taking into account individual savings, spending, and investment goals.

Not only are data products powerful, but they also tend to be lucrative. Data products tend to be high-margin because the cost of goods sold is so low: companies generally do not pay users to collect their data. Whether companies are channeling these lucrative products into customer savings (by making other services free) or purely amassing these gains as company profits, the central question remains: should data collection be free?

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Image Source: Robinhood

Just like oil, labor, and water, data is a commodity. True – it happens to be a non-finite commodity that humans can create; however, it is also a raw material used to create sold products. Just as a bar of chocolate is made from many cacao beans, so is a web marketing analytics insight crafted from many individual browser interactions.

If you’re a chocolate maker, you’ll likely have a handful of cocoa suppliers. If you’re a web analytics company, you’ll likely have millions of users providing a little data each. However, the simple facts that your suppliers are: (i) distributed, and (ii) orders-of-magnitude more numerous do not constitute adequate justification for not compensating them.

The logistics might be simpler than you think. The idea of web-based microtransactions is not new; little known to most people, the HTTP status code of 402 [2] has been reserved for “Payment Required” use-cases for a while. While this was meant to power the opposite flow (for a requestor to present payment to access content, rather than a content provider to pay a visitor for data gathered during an interaction), this nevertheless brings us one step closer to a future where browsers might contain native wallets that can enable hundreds of microtransactions per hour.

Image Source: Mozilla Foundation

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Regulation lags behind innovation. While privacy concerns have culminated in new statutes regulating how entities should collect and use data, most protections today concern only data subjects’ rights and obligations. They have not yet evolved to address questions of compensation and profit-sharing.

Some of this is due to a lack of pressure from the general public, which, in turn, results from a lack of awareness regarding the value of data, as well as opacity regarding how companies collect and use data. Some of this is due to coercive user policies that foist consent of data collection. And some of it is due to the lack of a clear solution and path forward.

What if we reimagined the concept of privacy in an economic, rather than rights-based, context? Could browsers compete for users by providing more sophisticated privacy customizations? Could they better enable user control to select and disclose limited and specific data in exchange for monetary earnings? Could they auto-respond to pesky cookie preference pop-ups? Could they broker a new type of data marketplace between companies who want to buy data and users who want to sell data? Are these features valuable enough for them to charge users a fee, and would the public pay?

I, for one, would.

 

[1] https://learn.robinhood.com/articles/626haurrOd1BFJ3CkfH7xq/what-is-a-commodity/
[2] https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/402

All about Grandma
By Anonymous, October 23, 2020

My grandma Diane lives in Tulsa, OK on a small farm with one of my aunts, Heather, my uncle Carl, my two cousins Carl III and Toby, and my uncle Carl’s mom Bethanne. They raise goats and fowl, have a couple house dogs and some cats that come and go as they are wont to. The farm has a pond that the dogs swim in sometimes. These are things that I know because they’re my family. I’ve spent countless Thanksgivings and Christmases and been to several weddings with them.

What I didn’t know until today was that grandma is a registered Republican and Heather and Carl are registered Democrats. I didn’t intend to find this information. Rather with the 2020 election on the mind and news media covering early voting, I decided to do a cursory search about what voting information exists in the public domain. It took less than a minute to stumble onto grandma’s voter registration on the data aggregator: voterrecords.com, where voter registration records are available in searchable form for 16 states, Oklahoma included.

 

Of course, voter registration records have been public for a long time, but before sites like voterrecords.com it took real effort to go peruse voter rolls. While the process differed from state to state, you typically had to go to the local county office or the secretary of state’s office to formally request access. These barriers meant only the most interested of actors, like political parties or investigative journalists, took the time to do it. Now, this information is available almost accidentally to anyone with an internet connection anywhere in the world.

While presence of the internet makes access to voter records fundamentally different than in the past, what makes it concerning now is the degree to which political affiliation has become enmeshed with personal identity, particularly for more extreme actors on both ends of the political spectrum, some of which threaten violence.

To make matters much worse, voterrecords.com connects voter registration information to sites that conduct extensive background searches – truthfinder.com and beenverified.com – all without transparent labeling that prominently displayed buttons will trigger a background search.

Truthfinder conducts a search of property records, criminal records, bankruptcy records, social media accounts, etc. While truthfinder exploits public records databases for much of this information, its site is set up to make use of users’ interactions to reinforce algorithmic conclusions about which records are related to the actual person in question. Presenting follow-on questions in a way that most users are likely to think that the site is trying to isolate a particular individuals’ records, the questions ask users to confirm or deny algorithmically generated relationships with other records it has come across, thereby strengthening the person-matching algorithms that form the core of those sites.

After asking several such questions the site prompts users to search for more people – including people with which the person likely has no personal connection such as ‘celebrities’. Truthfinder’s charges for its services, and its model invites people to conduct ‘unlimited’ searches over a month, rather than purchase individual reports. Furthermore, the generated report contains information not just about the person you’ve gone down a rabbit hole searching for but also about several people that truthfinder has determined are related to the person you’ve searched for.

It is through this that I learned, despite having known grandma all my life, that a lien was put on the farm last year, that she received her social security number and card around the time she turned 18 rather than at birth, and the VIN number on her Toyota Sequoia. While she doesn’t have a criminal record, several people in neighboring states with similar names do. While I know those people aren’t her, someone who doesn’t know her as well may not and might mistakenly come to the conclusion that my grandma has a problem with shoplifting. Truthfinder’s presentation of this information makes this outcome more likely by exaggerating and not disclaiming that the information may not be linked to the right person, as happened in this case. This is all in addition to a litany of phone numbers, email addresses, social media accounts, amazon wish lists, and the addresses she has lived at or co-signed for going back decades. A couple more clicks yields similar information about all of my Oklahoma relatives over the age of 18.

While voter registration records and for that matter each of the other sets of public records used by these sites historically may have had valid reasons for being in the public domain, the internet has enabled aggregation across these datasets in a way that it literally takes less than 10 minutes to stumble unintentionally from a person’s voter record to knowing some of the most personal aspects of their lives like bankruptcy and criminal records, and not much longer to unearth similar information about nearly everyone they are related to.

This is made all the more troubling by the devolution in public discourse and increase in othering as personal identities of all sorts and stripes are increasingly coalescing into constellations around bipolar political affiliations. This is all paired with increasing rhetoric of political violence. Americans should consider carefully what information is put into the public domain, and should advocate to their state legislatures to curtail the publication and aggregation of such data sources.

Clearview AI: The startup that is threatening privacy
By Stefania Halac, October 16, 2020

Imagine walking down the street, a stranger points their camera at you and can immediately pull up all your pictures from across the internet; they may see your instagram posts, your friends’ posts, any picture that you appear in, some which you may have never seen before. This stranger could now ascertain where you live, where you work, where you went to school, whether you’re married, who your children are… This is one of many compromising scenarios that may become part of our normal life if facial recognition software is widely available.

Clearview AI, a private technology company, offers facial recognition software that can effectively identify any individual. Facial recognition technology is intrinsically controversial, so much so that certain companies like Google don’t offer facial recognition APIs due to ethical concerns. And while some large tech companies like Amazon and Microsoft do sell facial recognition APIs, there is an important distinction between Clearview’s offering and that of the other tech giants. Amazon and Microsoft only allow you to search for faces from a private database of pictures supplied by the customer. Clearview instead allows for recognition of individuals in the public domain — practically anyone can be recognized. What sets Clearview apart is not its technology, but rather the database it assembled of over three billion pictures scraped from the public internet and social media. Clearview AI did not obtain consent from individuals to scrape these pictures, and has been sent cease and desist orders from major tech companies like Twitter, Facebook and Youtube over its practices due to policy violations.

In the wake of the Black Lives Matter protests earlier this year, IBM, Microsoft and Amazon updated their policies to restrict the sale of their facial recognition software to law enforcement agencies. On the other hand, Clearview AI not only sells to law enforcement and government agencies, but until May of this year was also selling to private companies, and has even been reported to have granted access to high net-worth individuals.

So what are the risks? One on hand, the algorithms that feed these technologies are known to be heavily biased and perform more poorly on certain minority populations such as women and African Americans. In a recent study, Amazon’s Rekognition was found to misclassify women as men 19% of times, and darker-skinned women for men 31% of time. If this technology were to be used in the criminal justice system, one implication here is that dark-skinned people would be more likely to be wrongfully identified and convicted.

Another major harm is that this technology essentially provides its users the ability to find anyone. Clearview’s technology would enable surveillance at protests, AA meetings and religious gatherings. Attending any one of these events or locations would become a matter of public record. In the wrong hands, such as those of a former abusive partner or a white supremacist organization, this surveillance technology could even be life-threatening for vulnerable populations.

In response, the ACLU filed a lawsuit against Clearview AI in May for violation of the Illinois Biometric Information Privacy Act (BIPA), alleging the company illegally collected and stored data on Illinois citizens without their knowledge or consent and then sold access to its technology to law enforcement and private companies. While some cities like San Francisco and Portland have enacted facial recognition bans, there is no overarching national law protecting civilian privacy from these blatant privacy violations. With no such law in sight, this may be the end of privacy as we know it.

References:

We’re Taking Clearview AI to Court to End its Privacy-Destroying Face Surveillance Activities

The Gender Square: A Different Way to Encode Gender
By Emma Tebbe, October 16, 2020

Image: square with two axes, the horizontal reading Masculine and Feminine and the vertical reading Low Gender Association / Agender and Strong Gender Association

As non-gender-conforming and transgender folks become more visible and normalized, the standard male / female / other gender selections we all encounter in forms and surveys become more tired and outdated. First of all, the terms “male” and “female” generally refer to sex, or someones biological configuration, “including chromosomes, gene expression, hormone levels and function, and reproductive/sexual anatomy.” Male and female are not considered the correct terms for gender orientation, which “refers to socially constructed roles, behaviours, expressions and identities of girls, women, boys, men, and gender diverse people.” Although sex exists on a spectrum which includes intersex people, gender has a wide range of identities, including agender, bigender, and genderqueer. This gender square method of encoding gender aims to encompass more of the gender spectrum than a simple male / female / other selection.

Image: triangle defining sex, gender expression, gender attribution, and gender identity

Upon encountering this square in a form or survey, the user would drag the marker to the spot on the square that most accurately represents their gender identity. This location would then be recorded as a coordinate pair, where (0, 0) is the center of the square. The entity gathering the data would then likely use those coordinates to categorize respondents. However, using continuous variables to represent gender identity allows for many methods of categorization. The square could be divided into quadrants, as pictured above, vertical halves (or thirds, or quarters), or horizontal sections. This simultaneously allows for flexibility in how to categorize gender and reproducibility of results by other entities. Other analysts would be able to reproduce results if they are given respondents’ coordinates and the categorization methodology used. Coordinate data could even be used as it was recorded, turning gender from a categorical variable into a continuous one.

Although this encoding of gender encompasses more dimensions, namely representing gender as a spectrum which includes agender identities, it still comes with its own problems. First of all, the gender square still does not leave room for flexible gender identities including those whose gender is in flux or those who identify as genderfluid or bigender. There are a few potential solutions for this misrepresentation on the UI side, but these create new problems with data encoding. Genderfluid folks could perhaps draw an enclosed area in which their gender generally exists, but recording this data is much more complex than a simple coordinate pair, and would become an array of values rather than a coordinate pair. People who identify as bigender could potentially place two markers, one for each of the genders they experience. Both this approach and an area selection approach make the process of categorization more complex – if an individual’s gender identity spans two categories, would they be labeled twice? Or would there be another category for people who fall into multiple categories?

Image: a gender spectrum defining maximum femininity as “Barbie” and maximum masculinity as “G.I. Joe”

Another issue might arise with users who haven’t questioned their gender identity along either of these axes, and may not understand the axes (particularly the Highly Gendered / Agender axis) enough to accurately use the gender square. When implemented, the gender square would likely need an explanation, definitions, and potentially suggestions. Suggestions could include examples such as “If you identify as a man and were assigned that gender at birth, you may belong in the upper left quadrant.” Another option may be to include examples such as in the somewhat problematic illustration above.

This encoding of gender would likely first be adopted by groups occupying primarily queer spaces, where concepts of masculinity, femininity, and agender identities are more prominent and considered. If used in places where data on sex and transgender status is vital information, such as at a doctor’s office, then the gender square would need to be supplemented by questions obtaining that necessary information. Otherwise, it is intended for use in spaces where a person’s sex is irrelevant information (which is most situations where gender information is requested).

Although still imperfect, representation and identification of gender along two axes represents more of the gender spectrum than a simple binary, and still allows for categorization, which is necessary for data processing and analytics. With potential weaknesses in misunderstanding and inflexibility, it finds its strength in allowing individuals to more accurately and easily represent their own identities.

References:
https://cihr-irsc.gc.ca/e/48642.html
https://www.glsen.org/activity/gender-terminology
https://journals.sagepub.com/doi/full/10.1177/2053951720933286
Valentine, David. The Categories Themselves. GLQ: A Journal of Lesbian and Gay Studies, Volume 10, Number 2, 2004, pp. 215-220
https://www.spectator.co.uk/article/don-t-tell-the-parents for image only

 

When Algorithms Are Too Accurate
By Jill Cheney, October 16, 2020

An annual rite of passage every Spring for innumerable students is college entrance exams. Regardless of their name, the end result is the same: to influence admission applications. When the Covid-19 pandemic swept the globe in 2020, this milestone changed overnight. Examinations were cancelled, leaving students and universities with no traditional way to evaluate admission. Alternative solutions emerged with varying degrees of veracity.

In England, the solution used to replace their A-level exams involved developing a computer algorithm to predict student performance. In the spirit of a parsimonious model, two parameters were used: the student’s current grades and the historical test record of the attending school. The outcome elicited nationwide ire by highlighting inherent testing realities.

Overall, the predicted exam scores were higher – more students did better than on any previous resident exam with 28% getting top scores in England, Wales and Northern Ireland. However, incorporating the school’s previous test performance into the algorithm created a self-fulfilling reality. Students at historically high performing schools had inflated scores; conversely, students from less performing schools had deflated ones. Immediate cries of AI bias erupted. However, the data wasn’t wrong – the algorithm simply highlighted the inherent biases and disparity in the actual data modeled.

Reference points did exist for the predicted exam scores. One was from teachers since they provide a prediction on student performance. The other was from student scores on previous ‘mock’ exams. Around 40 percent of students received a predicted score that was one step lower than their teachers’ predictions. Not surprisingly, the largest downturn in predictions occurred amongst poorer students. Many others had predicted scores below their ‘mock’ exam scores. Mock exam results support initial university acceptance; however, they must be followed-up with commensurate official exam scores. For many
students, the disparity between their predicted and ‘mock’ exam scores jeopardized their university admission.

Attempting to rectify the disparities came with its own challenges. Opting to use teacher predicted scores required accepting that not all teachers provided meticulous student predictions. Based on teacher predictions alone, 38% of predicted scores would have been at the highest levels: A*s and As. Other alternatives included permitting students to retake the exam in the Fall or allowing the ‘mock’ exam scores to stand-in should they be higher than the predicted ones. No easy answers existed when attempting to navigate an equitable national response.

As designed, the computer model assessed the past performance of a school over student performance. Individual grades could not offset the influence of a school’s testing record. It also clearly discounted more qualitative variables, such as test performance skills. In the face of a computer-generated scoring model, a feeling of powerlessness emerged. No longer did students feel they possessed control over their future and schooling opportunities.

Ultimately, the predictive model simply exposed the underlying societal realities and quantified how wide the gap actually is. In the absence of the pandemic, testing would have continued on the status quo. Affluent schools would have received higher scores on average than fiscally limited schools. Many students from disadvantaged schools would have individually succeeded and gained university admission. The public outcry this predictive algorithm generated underscores how the guise of traditional test conditions assuages our concerns about the realities of standardized testing.

Sources:
https://www.theverge.com/2020/8/17/21372045/uk-a-level-results-algorithm-biased-coronavirus-covid-19-pandemic-university-applications

https://www.bbc.com/news/education-53764313

Data as Taxation
By Anonymous, October 16, 2020

Data is often analogized with transaction. We formulate our interactions with tech companies as an exchange of our data as payment for services, which in turn allow for the continued provision of those services.

Metaphors like these can be useful in that they allow us to port developed intuitions from a well-trodded domain (transactions) to help us navigate more less familiar waters (data). In this spirit, I wanted to further develop this “data collection = economic transaction” metaphor, and explore how our perceptions of data collection change with a slight tweak: “data collection = taxation”

In the context of data collection, the following quote from Supreme Court Justice Oliver Wendall Holmes might give one pause. Is this applicable, or entirely irrelevant?

Here’s what I mean: with taxation, government bodies mandate that citizens contribute a certain amount of resources to fund public services. The same goes for data – while Google, Facebook, and Amazon are not governments, they also create and maintain enormous ecosystems that facilitate otherwise impossible interactions. Governments allow for a coordination around national security, education, and supply chains, and Big Tech provides the digital analogues. Taxation and ad revenue allow for the perpetual creation of this value. Both can embody some (deeply imperfect) notion of “consent of the governed” through voter and consumer choice, although neither provides an easy way to “opt out.”

Is this metaphor perfect? Not at all, but there is still value in making the comparison. We can recycle centuries of bickering over fairness in taxation.

For instance, one might ask “when is taxation / data collection exploitative?” On one end, some maintain that “all taxation is theft,” a process by which private property is coercively stripped. Some may feel a similar sense of violation as their personal information is harvested – for them, perhaps the amorphous concept of “data” latches onto the familiar notion of “private property,” which might in turn suggest the need for some kind of remuneration.

At the other extreme, some argue that taxation cannot be the theft of private property, because the property was never private to begin with. Governments create the institutions and infrastructure that allows the concept of “ownership” to even exist, and thus all property is on loan. One privacy analogue could be that the generation of data is impossible and worthless without the scaffolding of Big Tech, and thus users have a similarly tenuous claim on their digital trails.

The philosophy of just taxation has provided me an off-the-shelf frame by which to parse a less familiar space. Had I stayed with the “data collection = economic transaction” metaphor, I would have never thought about data from this angle. As is often the case, a different metaphor illuminates different dimensions of the issue.

Insights can flow the other way as well. For example, in data circles there is a developing sophistication around what it means to be an “informed consumer.” It is recognized by many that merely checking the “I agree” box does not constitute a philosophically meaningful notion of consent, as the quantity and complexity of relevant information is too much to expect from any one consumer. Policies and discussions around the “right to be forgotten”, user control of data, or the right to certain types of transparency acknowledge the moral tensions inherent in the space.

These discussions are directly relevant to justifications often given for a government’s right to tax, like the “social contract” or the “consent of the governed.” Both often have some notion of informed consent, but this sits on similarly shaky ground. How many voters know how their tax dollars are being spent? While government budgets are publicly available, how many are willing to sift through reams of legalese? How many voters can tell you what military spending is within an even order of magnitude? Probably as many as who know exactly how their data is packaged and sold. The data world and its critics have much to contribute to the question of how to promote informed decision-making in a world of increasing complexity.

Linguists George Lakoff and Mark Johnson suggest that metaphors are central to our cognitive processes.

Of course, all of these comparisons are deeply imperfect, and require much more space to elaborate. My main interest in writing this was exploring how this analogical shift led to different questions and frames. The metaphors we use have a deep impact on our ability to think through novel concepts, particularly when navigating the abstract. They shape the questions we ask, the connections we make, and even the conversations we can have. To the extent that that’s true, metaphors can profoundly reroute society’s direction on issues of privacy, consent, autonomy, and property, and are thus well-worth exploring.

When an Algorithm Replaces Cash Bail
Allison Godfrey
October 9th, 2020

In an effort to make the criminal justice system more equitable, California Senate Bill 10 replaced cash bail with a predictive algorithm producing a risk assessment score to determine if the accused needs to remain in jail before their trial. The risk assessment places suspects into low, medium, or high risk categories. Low risk individuals are generally released before trial, while high risk individuals remain in jail. In cases with medium risk individuals, the judge has much more discretion in determining their placement before trial and conditions of release. This bill also releases all suspects charged with a misdemeanor without needing a risk assessment. This bill was signed into law in 2018 and effective in October 2019. California Proposition 25 seeks to repeal this bill and return to cash bail on the basis that this algorithm biases the system even more than cash bail. People often see data and algorithms as purely objective, since they are based on numbers and formulas. However, they are often “black box” models where we have no way of knowing exactly how the algorithm arrived at the output. If we cannot follow the model’s logic, we have no way of identifying and modifying its bias.

Image from this article

By the nature of predictive algorithms, they learn from the data in much of the same way as humans learn from their life’s inputs (experiences, conversations, schooling, family, etc). Our life experiences make us inherently biased since we hold a unique perspective purely shaped by this set of experiences. Similarly, algorithms learn from the data we feed into them and spit out the perspective that the data creates: an inherently biased perspective. Say, for example, we feed a predictive model some data about 1,000 people with pending trials. While the Senate Bill is not clear on the exact inputs to the model, say we feed the model the following attributes of each person: age, gender, charge, past record, income, zip code, and education level. We exclude the person’s race from the model in an effort to eliminate racial bias. But, have we really eliminated racial bias?

Image from this article

Let’s compare two people: Fred and Marc. Fred and Marc have the exact same charge, identify as the same gender, have similar incomes, both have bachelor’s degrees, but live in different towns. The model learns from past data that people from Fred’s zip code are generally more likely to commit another crime than people from Marc’s zip code. Thus, Fred receives a higher risk score than does Marc and he awaits his trial in jail while Marc is allowed to go home. Due to the history and continuation of systemic racism in the country, neighborhoods are often racially and economically segregated, so people from one zip code may be much more likely to be people of color and lower income than those from their neighboring town. Thus, by including an attribute like zipcode, we are introducing economic and racial bias into the model even if these additional attributes are not explicitly stated. While the original goal of Senate Bill 10 was to eliminate the ability for wealth to be a determining factor in bail decisions, it inadvertently reintroduces wealth as a predictor in the algorithm through the economic bias that is woven into it. Instead of equalizing the scale in the criminal justice system, the algorithm tips the scale even further.

Image from this article

Additionally, the purpose of cash bail is to ensure the accused will show up to their trial. While it is true that the system of cash bail can be economically inequitable, the algorithm does not seem to be addressing the primary purpose of bail. There is no part of Senate Bill 10 that helps ensure that the accused will be present at their trial.

Lastly, Senate Bill 10 allows judge discretion for any case, particularly medium risk cases. Human bias in the courtroom has historically played a big role in the inequality of our justice system today. The level of discretion the judge has to overrule the risk assessment score could re-introduce the human bias the model partly seeks to avoid. It has been shown that judges exercise this power more often to place someone in jail than they do to release them. In the time of Covid-19, going to jail has an increased risk of infection. With this heightened risk of jail, our decision system, whether that be algorithmic, monetary, and/or human centered, should err more on the side of release, not detainment.

The fundamental question is one that neither cash bail nor algorithms can answer:
How do we eliminate wealth as a determining factor in the justice system while also not introducing other biases and thus perpetuating systemic racism in the courtroom?

To Broadcast, Promote, and Prepare: Facebook’s Alleged Culpability in the Kenosha Shootings
By Matt Kawa | October 9, 2020

The night of August 25, 2020 saw Kenosha, WI engrossed with peaceful protests, riots, arson, looting, and killing in the wake of the shooting of Jacob Blake. In many ways Kenosha was not unlike cities all around the country facing protests both peaceful and violent sparked by the killing of George Floyd and others by police forces. However, Kenosha manages to distinguish itself by the fact that in the midst of the responses to the untimely death of these individuals, more individuals were killed. Namely, two protestors were shot and killed, and another injured, by seventeen-year-old Antioch, IL resident, Kyle Rittenhouse.

Rittenhouse was compelled and mobilized to cross state lines, illegally (as a minor) in possession of a firearm, to “take up arms and defend out City [sic] from the evil thugs” who would be protesting, as posted by a local vigilante militia that calls themselves the Kenosha Guard. The Kenosha Guard set up a Facebook event (pictured below) entitled “Armed Citizens to Protect our Lives and Property” in which the administrators posted the aforementioned quote (also pictured).

In addition to egregious proliferation of racist and antisemitic rhetoric, the administrators of these Facebook groups blatantly promote commission of acts of violence against protestors and rioters, not only via the groups per se, but on their personal accounts as well.

On September 22, a complaint and demand for jury trial was filed by the life partner of one of Rittenhouse’s victims and three other Kenosha residents with the United States District Court for the Eastern District of Wisconsin against shooter Kyle Rittenhouse, Kyle Matheson, “commander” of the Kenosha Guard, co-conspirator Ryan Balch a member of a similar violent organization called the “Boogaloo Bois,” both organizations per se, and most surprisingly, Facebook, Inc.

The complaint effectively alleges intentional negligence on behalf of Facebook for allowing the vigilantes to coordinate their violent presence unchecked. The claim states that Facebook “provides the platform and tools for the Kenosha Guard, Boogaloo Bois, and other right-wing militias to recruit members and plan events.” In anticipation of the defense of ignorance, the complaint then cites that over four hundred reports were filed by users regarding the Kenosha Guard group and event page expressing concern that members would be seeking to cause violence, intimidation, and injury. Reports containing speculation which, as the complaint summarizes, ultimately did transpire.

While Facebook CEO Mark Zuckerberg did eventually apologize for his platforms role in the incident, calling it an “operational mistake” and removing the Kenosha Guard page, the complaint claims that as part of an observable pattern of similar behavior, Facebook “failed to act to prevent harm to Plaintiffs and other protestors” by ignoring material numbers of reports attempting to warn them.

Ultimately, the Plaintiffs’ case rests on the Wisconsin legal principle that, “A duty consists of the obligation of due care to refrain from any act which will cause foreseeable harm to others . . . . A defendant’s duty is established when it can be said that it was foreseeable that [the] act or omission to act may cause harm to someone.” Or, simply put, Facebook had a duty to “stop the violent and terroristic threats that were made using its tools and platform,” including through inaction.

Inevitably, defenses will be made on First Amendment grounds, claiming that the Kenosha Guard and Boogaloo Bois, and their leaders and members, were simply exercising their right to freedom of speech, a right Facebook ought to afford its users. However, the Supreme Court has interpreted numerous exceptions into the First Amendment including quite prominently forbidding of incitement to violence. Whether Facebook has a moral obligation to adjudicate First Amendment claims is less clear cut. But the decision must be made in the modern, rapidly evolving world of social media as to what the role of the platform is in society and what ought or ought no be permissible enforcement of standards across the board.

The full text of the complaint can be found here.

Facing Security and Privacy Risks in the Age of Telehealth
By Anonymous | October 9, 2020

As the world grapples with the coronavirus pandemic, more healthcare providers and patients are turning to telehealth visits–visits where the patient is remote and communicates with her provider through a phone call or video conference. While telehealth visits will continue to facilitate great strides forward in terms of patient access, there are privacy risks that need to be mitigated to secure the success of remote visits.

Image: National Science Foundation

Participating in a remote visit opens up a patient to many potential touchpoints of security risk. For example, ordinary data transmissions from a mobile application or medical device, such as an insulin pump, may be inadvertently shared with a third party based on the permissions granted to applications on a patient’s mobile device. Additionally, devices that stream recordings of customer statements, such as Amazon’s Alexa may record sensitive information that’s communicated over the course of a remote patient visit. In some cases, a patient may have trouble using a HIPAA (Health Insurance Portability and Accountability Act) compliant telemedicine service such as Updox, and the patient and provider might alternatively look to a non-compliant ordinary Zoom call to complete their visit. How does one make the tradeoff between patient privacy and patient access?

There are steps that both patients and providers can take in mitigating the security risks that surround telehealth visits. Patients can limit the permissions of mobile applications they use to reduce the risk of sharing sensitive information with third parties. Patients may also look to briefly turn off any devices that may record activity in their homes. Medical professionals can ensure that only current patient lab results and records are open on their laptops to avoid inadvertently screen sharing inappropriate patient data. Additionally, medical professionals and patients can work to become familiar with HIPAA-compliant telemedicine services, ensuring improved security and seamless telehealth visits.

Image: Forbes

Beyond the actions of patients and providers, patient privacy is often addressed through regulatory institutions such as the U.S. Department of Health and Human Services (HHS) with acts such as HIPAA. The HHS has recognized the need for telehealth visits during the coronavirus pandemic, and has stated that its Office for Civil Rights (OCR) “will not impose penalties for noncompliance with the regulatory requirements under the HIPAA Rules against covered health care providers in connection with the good faith provision of telehealth during the COVID-19 nationwide public health emergency”. As a supplement to the previous statement, the HHS has stated only non-public telecommunication products should be used in telemedicine visits. While the point at which the world will start to recover from the COVID-19 pandemic remains to be seen, protecting patient privacy through improved regulatory guidelines around telehealth should become a higher priority.

Further regulatory control around patient privacy with respect to telehealth will help to ensure its success. The potential benefits of remote visits are great and are quickly becoming realized. Patients with autoimmune diseases can speak to their providers from home, alleviating their higher-than-average risk of COVID-19 complications. Rural patients who once had to travel hours to see the right provider can participate in lab work and testing closer to home and discuss results and steps forward with talented healthcare providers across the country. Providers may be able to see more patients than before. Patients and providers alike can look forward to a world where telemedicine is more easily integrated into daily life, but steps should be taken to ensure patient privacy.

References

• Germain, T. (2020, April 14). Medical Privacy Gets Complicated as Doctors Turn to Videochats. Retrieved October 05, 2020, from https://www.consumerreports.org/health-privacy/medical-privacy-gets-complicated-video-chats-with-doctors-coronavirus/
• Hall, J. L., & McGraw, D. (2014, February 01). For Telehealth To Succeed, Privacy And Security Risks Must Be Identified And Addressed. Retrieved October 05, 2020, from https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2013.0997
• McDougall, J., Ferucci, E., Glover, J., & Fraenkel, L. (2017, October). Telerheumatology: A Systematic Review. Retrieved October 06, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436947/
• Notification of Enforcement Discretion for Telehealth. (2020, March 30). Retrieved October 07, 2020, from https://www.hhs.gov/hipaa/for-professionals/special-topics/emergency-preparedness/notification-enforcement-discretion-telehealth/index.html
• Schwab, K. (2020, August 21). Telehealth has a hidden downside. Retrieved October 07, 2020, from https://www.fastcompany.com/90542626/telehealth-has-a-hidden-downside