Slaves of the Machines

In his book “Slaves of the Machines”, first published in 1997, Gregory J.E. Rowlins take lay readers on a tour of the sometimes scary world to which computers are leading us. Today, 20 years later, and in a world where Artificial Intelligence (AI) has become a household name, his predictions are more relevant than ever.

Before we dive into the risks we are now facing, let us first start off with defining what Artificial Intelligence is. Stated simply, AI is machines doing things that are considered to require intelligence when humans do them, e.g. understanding natural language, recognizing faces in photos or driving a car. It’s the difference between a mechanical arm on a factory production line programmed to repeat the same basic task over and over again, and an arm that learns through trial and error how to handle different tasks by itself.

There are two risks that are most often brought up in relation to the introduction of Artificial Intelligence into our society and workplace:

  • Robots and further automation risk to displace a large set of existing jobs; and
  • Super-intelligent AI agents risk running amok, creating a so-called AI-mageddon.

In relation to the first risk, a recent research report by McKinsey Global Institute called “Harnessing Automation for a Future that Works” makes this threat quite clear by predicting that 49 percent of time spent on work activities today could be automated with “currently demonstrated technology” either already in the marketplace or being developed in labs. Luckily for us, McKinsey do think it will take a few decades to come to fruition due to other ingredients such as economics, labor markets, regulations and social attitudes.

As for the second risk, the dooms-day thesis has perhaps most famously been described by the Swedish philosopher and Oxford University Professor Nick Bostrom in his book “Superintelligence: Paths, Dangers, Strategies”. The risk Bostrom describe is not that an extremely intelligent agent would misunderstand what humans want it to do and do something else. Instead, the risk is that intensely pursuing the precise (but flawed) goal that the agent is programmed to pursue could pose large risks. An open letter on the website of the Future of Life Institute shows the seriousness of this risk. The letter is signed not just by famous AI outsiders such as Steve Hawking, Elon Musk, and Nick Bostrom but also by prominent computer scientists (including Demis Hassabis, a top AI researcher at Google).

Compared to the above two risks, less has been written about a potential third one, namely the threat of lost autonomy/fairness for and potential deceit of workers when controlled by AI. This arrangement, where machines are the brains and humans are the robots (or slaves), is not only in existence in manufacturing and logistics today. It also occurs frequently in new sectors ranging from medical sales to transportation services where human intervention is still required while AI is desired for productivity and profitability.

Ryan Calo and Alex Rosenblat touch on this dilemma in their paper “The Taking Economy: Uber, Information, And Power“. The paper gives a good picture of the limited autonomy Uber drivers have vis-à-vis the automated Uber AI control system. In order to maximize productivity, the system imposes severe restrictions on the information and choices available to drivers. Drivers are not allowed to know the destination of the next ride before pick-up; heat maps are shown without precise pricing or explanations how they were created; and no chances are given to drivers to opt-out from default settings. The AI platform is in control and the information process is concealed to the degree that we cannot review or judge its fairness.

Thankfully, there are increasing efforts in academia (e.g. UC Berkeley – Algorithmic Fairness and Opacity Working Group) and legislators (see Big Data – Federal Trade Commission) to help demystify AI and the underlying Machine Learning procedures on which it is built. These efforts look to implement:

  • Increased verification and audit requirements to prevent discrimination from creeping into algorithm designs;
  • Traceability and validation of models through defined test setups where both input and output data are well-known;
  • The possibility to override default settings to ensure fairness and control;
  • The introduction of security legislation to prevent unintentional manipulation by unauthorized parties.

In a world of AI, it is the “free will” that separates humans from machines. It is high time that we exercise this will and define how we want a world with AI to be.

Data Privacy just officially became a massive financial liability for Social Media!

How many of us woke up to the news of a 1.5% dip in the stock market today? This is primarily due to the outfall of Cambridge Analytica’s illicit use of profile data from Facebook. Of course, the illegality, as far as Facebook is concerned is for holding data that Cambridge Analytica said they had voluntarily removed from their servers years before. The current fallout to Facebook (down 7% today) is not for the potentially catastrophic end use of that data if proven to have been used in electioneering, which Cambridge Alaytica is under investigation in the UK for swinging the Brexit vote as well as in the US for helping elect Trump, who paid handsomely ($6M) to get access to their user-profile centered analyses.

Admittedly, with #deletefacebook trending up a storm on Twitter (of all places), there is a little bit of schadenfreude aimed at greedy Facebook ad executives baked into that 400 point drop in the Dow, but at its heart is an international call for better regulation of the deeply personal data that is housed and sold by Facebook and other tech giants. In this instance, the data policies that are in the limelight are two of the most problematic for Facebook: third party sharing/housing of data, and using research as a means for data acquisition. The research use of Facebook data is definitely tarnished

The market volatility and the fact that Facebook actually lost daily users last quarter in the US, some of which was attributable to data privacy concerns from their user base, highlights the need for more secure third party data use policies. These are exactly the reason why, even if you delete your profile, the data can live on (indefinitely) on the servers of third party vendors without known/feasible recourse by the Facebook users to demand the deletion of this data. And their privacy policy makes this clear, though it is a difficult read to figure that out.

Facebook’s outsized market value is based in a great part on their ability to aggregate their users’ personal data and freely sell it as desired. The European Union’s upcoming May 25th deadline to implement the General Dat Protection Regulations is likely to help push the needle towards more control of data deletion and usage by third parties in Europe, and it is exactly the specter of potentially farther reaching regulation about data usage that dragged down the market today and will ultimately lower Facebook’s value if more regulation comes about. The big question is whether Facebook and other large data acquiring companies will be able to balance their voracious profit motive and inherent need to sell our data with the ability to help protect our privacy, and/or whether heavy handed government tactics can achieve that second goal for them?

Learning From Uber: Questions On How Airbnb Suggests Prices To Their Hosts

2017 was a bad year for Uber. If you’re reading this, you probably don’t need me to tell you why. What you might not have seen though, is how Uber used data science experiments to manipulate drivers.  In this New York Times article, Noam Sheiber discusses how Uber uses the results of data-driven experiments to influence drivers, including ways to get drivers to stay on the app and work longer, as well as getting drivers to exhibit certain behaviors (e.g. drive in certain neighborhoods at certain times).

In light of Uber’s widespread bad behavior, it’s been brought up several times that maybe we should have seen this coming.  After all, this is a company that has flown in the face of laws and regulations with premeditation and exuberance, operating successfully in cities where by rule their model isn’t allowed.  Given this, the question I’ll pursue here is what should we make of Airbnb, a company whose growth to unicorn status has been fueled by similarly brazen ignorance of local laws, pushing into cities where hosts often break the law (or at least the tax code) by listing their homes?

In particular, I’d like to take a look at how Airbnb affects how their hosts price their listings. Why? Well, this is where Airbnb has invested a lot of their data science resources (from what’s known publicly) and it’s one of the key levers where they can influence hosts.  The genesis of  their pricing model came in 2012, when Airbnb realized they had a problem. In a study, they found that many potential new hosts were going through the entire signup process, just to leave when prompted to price their listing. People didn’t know, or didn’t want to put in the work, to find out how much their listing was worth.  So, Airbnb built  hosts a tool that would offer pricing “tips”. The inference from Airbnb’s blog posts covering their pricing model is that this addressed the problem, as users happily rely on their tips – though they are careful to point out, repeatedly, that users are free to price at whatever they want.

As someone who is looking at this with the agenda of flagging any potential areas of concerns, this caught my attention.  The inference I took from reading several accounts of their pricing model, is that Airbnb believe users lean heavily (or blindly) on their pricing suggestions. I’d buy that. And why that’s concerning is we don’t really know how their model works.  Yes, we know that it’s a machine learning classifier model, that extracts features out of a listing, as well as incorporating dynamic market features (season, events, etc) to predict the value of the listing.  In their postings about their model, they list features it uses, and many make sense.  Wifi, private bathrooms, neighborhood, music festivals, all of these are things we’d expect. And others like “stage of growth for Airbnb” and “factors in demand” seem innocuous at first pass. But wait, what do those really mean?

One of the underlying problems present in Sheiber’s Uber article was that fundamentally, Uber’s and their Driver’s agendas were at odds. And while I wouldn’t say the relationship between Airbnb and their hosts is nearly as fraught as Uber and its drivers, it might not be 100% aligned. For host’s, the agenda is pretty simple: on any given listing, they’re trying to make as much money as possible. But for Airbnb, there’s way more at play. They’re trying to grow, and establish themselves as a reliable, go-to source for short-term housing rentals. They’re competing with the hotel industry as a whole, trying to establish themselves in new markets, and trying to change legislature the world over.  Any of these could be a reason why they might include features in their pricing tips model that do not lead it price listings at the maximum potential value.

The potential problem here is that while Airbnb likes to share their data science accomplishments, and even open source tools, they aren’t fully transparent with users and hosts about what factors go into some of the algorithms that effect user decisions. While it would be impossible to share every feature and it’s associated weights, it is entirely possible for them to inform users if their model takes into account factors whose intent is not to maximize user revenue. 

Clearly, this is all speculative, as I can’t with any certainty say what is behind the curtain of Airbnb’s pricing model. In writing this, I’m mearly hoping to bring attention to an interaction that is vulnerable to manipulation.

Filter Bubbles

During our last live session, we discussed in detail the concept of filter bubbles. The condition in which we isolate ourselves inside an environment where everyone around us agrees with our points of view. It is being said a lot lately, not just during our live session, that these filter bubbles are exacerbated by business models and algorithms that power most of the internet. For example, Facebook runs on algorithms that aim to show the users the information that Facebook thinks they will be most interested in based on what the platform knows about them. So if you are on Facebook and like an article from a given source, chances are you will continue to see more articles from that and other similar sources constantly showing up on your feed and you will probably not see articles from other publications that are far away in the ideological spectrum. The same thing happens with Google News and Search, Instagram feeds, Twitter feeds, etc. The information that you see flowing through is based on the profile that these platforms have built around you and they present the information that they think best fits that profile.

Filter bubbles are highlighted as big contributors to the unexpected outcomes of some major political events around the world during 2016 such as the UK vote to exit the European Union as well as the result of the US presidential election in favor of Donald Trump. The idea is that in a politically divided society, filter bubbles make it even harder for groups to find common ground, compromise, and work towards a common goal. Another reason filter bubbles are seen as highly influential in collective decision making is that people tend to trust other individuals in their own circles much more than “impartial” third parties. For example, a person would much rather believe what his or her neighbor is posting on the Facebook wall over what the article in a major national newspaper is reporting on, if the two ideas are opposed to each other, even if the newspaper is a longstanding and reputable news outlet.

This last effect is to me, the most detrimental aspect of internet-based filter bubbles. Because it lends itself for easy exploitation and abuse. With out-of-the-box functionality, these platforms allow trolls and malicious agents to easily identify and join like-minded cohorts and present misleading and false information pretending to be just another member of the trusted group. This type of exploitation is currently being exposed and documented, for example, as part of the ongoing investigation on Russian meddling in the 2016 US Presidential election. But I believe that the most unsettling aspect of this is not the false information itself, it is the fact that that the tools being used to disseminate it are not backdoor hacking or sophisticated algorithms. It is being done using the very core and key functionality of the platforms, which is the ability of third party advertisers to identify specific groups in order to influence them with targeted messages. That is the core business model and selling point of all of these large internet companies and I believe it is fundamentally flawed.

So can we fix it? Do we need to pop out the filter bubbles and reach across the aisle? That would be certainly helpful. But very difficult to implement. Filter bubbles have always been around. I remember in my early childhood, in the small town where I grew up, pretty much everyone around me believe somewhat similar things. We all shared relatively similar ideas, values, and world views. That is natural human behavior. We thrive in tribes. But because we all knew each other, it was also very difficult for external agents to use that close knit community to disguise false information and propaganda. So my recommendation to these big internet companies, would not necessarily be to show views and articles across a wider range of ideas. That’d be nice. But most importantly, I would ask for them to ensure that the information shared by their advertisers and the profiles they surface on users’ feeds are properly vetted out. Put truth before bottom lines.

We need to protect our information on Social Media

Currently, for most tech companies, their major revenue comes from online advertisement. In order to how to deliver the advertisement more efficient to the right target, companies like Linkedin and Facebook start to collect a large variety of data from the society. They start to analysis people’s geographic location, friends, education and the place they usually go etc. For marketing A/B testing purpose, the more information you collected, the more accurate you will predict.
Recently, Facebook started to launch their new product called Facebook Local App. Based on its descriptions – Keep up with what’s happening locally—wherever you are—whether you’re looking for something to do with friends this weekend or want to explore a new neighborhood. The key idea will try to help people know what’s happening in their neighborhood. Everything looks good and interesting. However, this app’s average score on Apple Store is only 3.3 out of 5.
Here are the reasons: first is people started to care about their privacy. And there are lots of similar apps on Apple Store. Why they need to choose this one. Facebook already has most of their information, they do not want to let Facebook know everything about them. Second and the most important, once you start to use this App, Facebook will start to get your information more accurate. For example, on your Facebook account, you only need to put which city you live in, for me, my Facebook account only shows I live in San Francisco. However, once I start to use this app, Facebook will know which area in the city I live in and what is my life patterns. For me I am a Pokemon Go Fan, they will know where I am going every weekend, how long I will be in these locations. I may feel I was watched by someone every day.
Based on what we learn so far, Facebook gets huge benefits, because they can directly charge their advertisement fees to their clients. Because they will deliver the content to people based on area. They may keep sending the good restaurants around the areas where I usually go. For me, I will increase the times to go these places because I have no choice which saves me time as well. But, if they use this information for another purpose such as using the information to develop another Apps or research? We do not know how they store and use our data. We all have the similar experience that when we start to put our contact information to apply for credit card in credit companies like Visa or American Express, we will get a lot of calls from many banks as well. Why, because credit companies share our information with the banks. Banks use the information to find us and ask us to open an account with them.
There will be the similar situation on Linkedin as well. Once we change our title to currently looking full-time position, we will get a lot of email or request from the staffing companies for job hiring.
Above all, the technology does change our lives, but we need more rules to protect us and avoiding being bothering from them as well.

Racial Bias in the National Instant Criminal Background Check System

Modern gun control began with the Gun Control Act of 1968, passed after the assassinations of John F. Kennedy, Martin Luther King Jr., and Bobby Kennedy. It prohibits mail-order gun purchases, requires all new guns be marked with a serial number, and created the Federal Firearms License (FFL) system, which manages licenses required for businesses to sell guns. The law was further strengthened in 1993 by the Brady Handgun Violence Prevention Act. This new addition established a set of criteria that disqualify a person from legally purchasing a gun. It also created the National Instant Criminal Background Check System (NICS), which is maintained by the FBI and used by FFL licensed businesses to quickly check if a person matches any of the disqualifying criteria.

Although NICS was created with good intent, and without any explicit racist assumptions, the NICS database and algorithms likely inflict greater burden on the African American community than its white counterpart. This unintended consequence is based on a perfect storm of seemingly unrelated policies and history.

To see this bias we must first understand how a background check is performed. When you purchase a gun through an FFL licensed business you submit identifying information, such as your name, age, address, and physical descriptions. Then the NICS system looks for an exact match on your personal data in three databases that track criminal records. If no exact match is found a close name match can still halt your purchase.

The data and matching algorithms used by NICS are not publically available so we can only guess at what exists in the databases and how it is utilized, but based on public record and one particular criteria established by the Brady Act, conviction of a crime punishable by imprisonment for a term exceeding one year, we can make educated assumptions about the data. First, drug possession and distribution can result in multi-year imprisonment. Second, the largest proportion of inmates are there because of drug related offenses. These imply a large–maybe the largest–population in NICS is there due to drug related crimes. Lastly, African Americans are imprisoned at a rate six times greater than whites for drug related crimes even though white and African Americans use and possess drugs at essentially the same rate. This final statistics indicates the NICS databases must include a disproportionate number of African Americans due to biases in law enforcement and the criminal justice system. These upstream biases not only affect the inmates at the time of conviction but follows them throughout life, limiting their ability to exercise rights protected by the 2nd Amendment.   

Unfortunately this is not where the bias ends. There is evidence that shows using loose name-based matching algorithms against felony records in Florida disproportionately identified black voters incorrectly as felons and stripped them of their right to vote in the 2000 elections because African Americans are over-represented in common names due to losing their family names during the slavery era. It’s worth wondering if the FBI’s name-matching algorithm suffers from the same bias and results in denying or delaying a disproportionate number of law-abiding African Americans from buying guns. In addition, this bias would result in law-abiding African Americans having their gun purchases tracked in NICS. By law, NICS deletes all traces of successful gun purchases. However, if you are incorrectly denied purchase, you can appeal and add content to the databases that proves you are allowed to purchase guns. This is done to prevent the need to appeal every time you purchase a gun. The existence of this content is the only record of gun purchases in NICS, information the government is generally forbidden to retain. If this bias does exist, there is sad irony in laws passed on the backs of infamous violence perpetrated by non-African Americans now most negatively affecting African Americans.

This evidence should be weighed carefully, especially by those who advocate for both gun control and social justice. The solutions settled upon for gun control must pass intense scrutiny to insure social justice is not damaged. In the case of NICS, the algorithms should be transparent, and simple probabilistic methods employed to lessen the chance of burdening  African Americans who have common names.

Enrollment Management from a Student Perspective

If you received an undergraduate degree in the United States, you are likely familiar with the U.S. financial aid system from a student perspective – you submit your essays, your academic records and test scores, you file the FAFSA, and you expect some amount of financial aid from the colleges you applied to in return. Your institution may or may not have provided an estimated cost calculator on its website, and you may or may not have received as much financial aid as you hoped for from your institution. Given that approximately 71% of each undergraduate class takes on student loan debt (TICAS, 2014), institutional aid typically does not cover the gap between what the student can pay and what the institution offers (also known as unmet need). What is clear, however, is that despite a consistent sticker price, the actual cost of college differs from student to student.

Colleges and consultants refer to the practice as “enrollment management” or “financial aid leveraging”, but the pricing strategy itself is known as price discrimination (How Colleges Know What You Can Afford, NY Times, 2017). As with any business where functionality is constrained by net revenue, in some ways there is a fundamental opposition between the best interests of the consumer (student) and the seller (college), since consumers ideally want the product that the cheapest rate and sellers want to earn as much revenue as possible (though many factors other than revenue also drive colleges’ decision making). However, this idea becomes more problematic as we consider that education is not an inessential service, but a key component in personal development and economic opportunity.

The looming ethical discussion, at least in the U.S., is whether higher education should be free for anyone who wants it, perhaps eliminating the need for universities to engage in price discrimination. A parallel discussion is whether price discrimination that leaves unmet need for students is what needs more immediate resolution.

Rather than taking a stance on U.S. college pricing, however, I am interested in the enrollment management paradigm from a student privacy perspective. If Nissenbaum et al. posit that “informational norms, appropriateness, roles, and principles of transmission” govern a framework of contextual integrity (Nissenbaum et al., 2006), how might the use of student-provided data by enrollment consultants violate contextual integrity from the perspective of a student?

I cannot find any existing studies on students’ expectations of how colleges handle their data. As a U.S. student myself, I expect that many students’ expectations are driven by the norms laid out by U.S. policy (particularly FERPA), which treats educational and financial data as private and protected.

I believe, therefore, that certain expectations about the flow of data from student to institution may be violated when universities don’t explicitly divulge their partnerships. If the flow is expected to be a straight line from the student to the college, the continuation of that information from college to consultancy and back to the college may seem aberrant. Equally important, I think, is the expectation of the extent of the information. Students likely expect, and cost calculators imply, that certain static pieces of information will be used to make an admit decision, offer merit aid, and determine financial need. In that case, the passing of that information to an outside consultancy who can use that information (and third-party data) in a predictive model to an extent that surpasses any individual piece of data, both to recommend aid and to predict behavior, and then return that information to the college, may also violate students’ expectations.

It seems to me that whether financial aid leveraging is beneficial to the student or not, a lapse in privacy occurs to the benefit of institutions when they fail to disclose the extent to which student data will be used, and by whom exactly.

Privacy and Security of On-Board Diagnostics (OBD)

Privacy issues arising from technology often share more or less a similar story. A technology is usually developed with simple intentions to enhance a feature or perform a modest task. The fittest of those technologies survive to serve a wide set of users. However, as more information is logged and transmitted, a growing concern over privacy surfaces until that privacy issue devours the once simple technology. We have observed too many of these stories. Notably, each of the social networking sites that took turns in popularity were developed as a means to host personal opinions and connections. That never changed, except the discussion around privacy infringements exploded and profoundly affected the direction of the sites. The baton for the next debate seems to be handed over to On-Board Diagnostics (OBD). OBD is a device that is placed behind driver dashboards for the sake of collecting data on the car, such as whether or not tire pressure is low. But more features have been added with more to come. Addition of entertainment systems, cameras, and navigation devices contribute richer layers of data onto the OBD.

Originally developed to track maintenance information and later gas emissions, OBD is attracting mounting concern in its expanding capability to inflict some serious privacy violations. Much like the social network sites, OBD is becoming a lucrative source of rich data. In the case with cars, insurance agencies, advertisers, manufacturers, governments, and hackers all have an interest in the data contained in the OBD. For example, some insurance companies have used information from OBD to measure driving distance to determine discounts to drivers with low mileage. And other insurance companies are issuing monetary incentives for customers to submit information from their OBD. Manufactures can use the information to improve their cars and services. And governments can monitor and regulate traffic and gas emissions with the information. Advertisers can be guided with the information as well. Of course, the distribution of information to insurers and marketers seem trivial when you weigh the harm in a possible hacking incident.

As more OBDs are being loaded with internet connectivity functions, the vulnerability may be worsening. The types of information are no longer limited to whether or not your tires are low in pressure. More personal information such as your preference of music, number of passengers, and real time location. Location data can be used to infer your home address, school or office, choice of supermarkets, and maybe even your religious views or night life habits. Cameras in and around the vehicles can supply streaming videos as well. While each of these devices are useful in enhancing driver and passenger experiences, the privacy and security concerns are indeed alarming. Moreover, OBD loaded on a “smart” car can collect more information more accurately, and share the information faster with a wider audience. Unlike those of smartphones, however, developers of smart cars face bigger challenges in keeping up with the rapid technological evolution. Also, even if choices were offered to turn off features of the OBD, many of them are still likely to remain on as safety concerns may override privacy concerns. The question of ownership of the information is also debated in the absence of clear rules and regulations.

A collaborative effort involving governments, manufacturers, and cybersecurity professionals is needed to address the privacy and security concerns arising from OBD. In the United States, senators introduced a bill “Security and Privacy in Your Car Act of 2015” that reads cars to be “equipped with reasonable measures to protect against hacking attacks.” However, the bill is too ambiguous and will be difficult to enforce in a standardized way. Manufacturers, while acknowledging the possible risks associated with OBD, are not fully up to speed on the matter. Federal and state governments need to take leadership, with the cooperation of manufacturers and security professionals, to make sure safe and reliable automobiles are delivered to customers. How we collectively approach the issue will certainly affect what cost we pay.

Strava and Behavioral Economics

I am a self-described health and fitness nut, and in the years since smartphones have become an essential device in our day-to-day lives, technology has also slowly infiltrated my daily fitness regime.  With such pervasive use of apps to track one’s own health and lifestyle choices, is it any wonder that companies are also collecting the data that we freely give them, with the potential to monetize that information in unexpected ways?  Ten years ago, when I went outside for a run, I would try to keep to daylight hours and busy streets because of the worry that something could happen to me and no one would know.  Now, the worry is completely different – now I am worried that if I use my GPS-enabled running app, my location (along with my heart rate and running speed) is saved and stored in some unknown database, to be used in some unknown manner.

 

Recently, a fitness app called Strava made headlines after it published a heat map showing the locations and workouts of users who made the data public (which is the default setting) and inadvertently revealed the location of secret military bases and the daily habits of personnel.  It was a harsh reminder of how the seemingly innocuous use of an everyday tool can have serious consequences – not just personally, but also professionally, and even for one’s own safety (the Strava heatmap showed certain jogging routes of military personnel in the Middle East).  Strava’s response to the debacle was to release a statement that said they were reviewing their features, but also directed their users to review their own privacy settings – thus the burden remains on the user to opt out, for now.

 

Fitness apps don’t just have the problem of oversharing their users’ locations.  Apps and devices like Strava, or Fitbit, are in the business of collecting a myriad of health and wellness data, from sleep patterns, and heart rates, to what the user eats in a day.  Such data is especially sensitive, because it relates to a user’s health – however, because the user is not sharing it with their doctor or hospital, they may not even realize the extent to which others’ may be able to infer their private sensitive information.

 

One of the biggest issues here is the default setting.  Behavioral economics studies show that the status quo bias is a powerful indicator of how us humans make (or fail to make) decisions.  Additionally, most users simply fail to read and understand privacy statements when they sign up to use an app.  Why do some companies still choose to make the default setting “public” for users of their app – especially in cases where it is not necessary? For Strava, if the default had been to “opt in” to share your location and fitness tracking data with the public, their heatmaps would have looked very different.

 

It is not in the interest of companies to allow the default settings to be anything other than public.  The fewer people who share data, the less the company has about you, and the less likely they are able to use the data to their benefit – such as targeted marketing techniques, or using the data to develop additional features for the individual user.  Thus, they could argue that collecting their users’ data on a more widespread basis also benefits their users in the long run (as well as their own revenues).  However, headlines like this one erode public trust in technology companies – and companies such as Strava would do well to remember that their revenues also depend on the trust of their users.  In the absence of allowing “private” or “friends only” default settings, these companies would do well to analyze the potential consequences before releasing the public data that they collect about their users.

 

Candy Cigarettes- now available in “blue speech bubble” flavor

Less than two months after the launch of MessengerKids, Facebook’s new child-focused correspondence app has received backlash from child-health advocates, including a plea directly to Mark Zuckerberg to pull the plug. On January 30th, the Campaign for Commercial-Free Childhood published an open letter compiled and signed by over 110 medical professionals, educators, and child development experts, which accuses the tech giant of forsaking its promise to “do better” for society and targeting children under 13 to enter the world of social media.  

At its introduction in early December 2017, MessengerKids was branded as another tool for parents struggling to raise children in the digital age. After installing the app on their child’s device(s), parents can control their child’s contact list from their own Facebook account. The app has kid-friendly gifs, frames, and stickers, built in screening for age-inappropriate content in conversations, and a reporting feature for both parents and children to hopefully combat cyberbullying. It contains no advertisements, and the child’s personal information isn’t collected, in accordance with US federal law. Creating an account does not create a Facebook profile, but nonetheless, the service introduces children to social media and their own online presence.

Contrary to the image MessengerKids hoped to present, child-health advocates have interpreted the application less as a gatekeeper for online safety and more as a gateway for unhealthy online habits. In its letter to Mark Zuckerberg, the CCFC cites multiple studies linking screen time and social media presence to depression and negative mental health effects. In addition, the app will interfere with the development of social skills, like the “ability to read human emotion, delay gratification, and engage in the physical world.” The letter argues that the connectivity MessengerKids promises is not an innovation, as these communication methods already exist with parent’s approval or supervision (e.g. Skype or parents’ Facebook accounts); nor does the app provide the solution for underage Facebook accounts, as there’s little incentive for those users to migrate to a service with fewer features designed for younger kids. Instead, it reads as a play to bring users onboard even earlier but marketing specifically to the untapped, under 13 audience.

In addition to the psychological development concerns, a user’s early-instilled brand trust may surpass the perceived importance of privacy later on. Data spread and usage is already a foggy concept to adults, and young children certainly won’t understand the consequences of sharing personal information. This is what the US federal law (“COPPA”) hopes to mitigate by protecting underage users from targeted data collection. MessengerKids normalizes an online identity early on, so young users may not consider the risks of sharing their data with Facebook or other online services once they age out of COPPA protection. The prioritization of online identity that MessengerKids may propagate presents a developmental concern which may affect how those after generation Z  value online privacy and personal data collection.

While Facebook seems to have done its homework by engaging a panel of child-development and family advocates, this could be another high-risk situation for user trust, especially in the midst of the fake-news controversy. Facebook’s discussions with its team of advisors are neither publicly available nor subject to the review process of academic or medical research. With the CCFC’s public backlash, parents who wouldn’t have questioned the feature otherwise may now perceive the impact of the app and its introduction as a medical decision for their child’s health. A curated panel of experts may not be enough to assure parents that Facebook does, in fact, care about kids as more than potential users. The app has no built-in capability to report or prevent cyberbullying, so if Facebook is concerned about unmitigated online activity why not just enforce the existing policy of age restrictions?

Comparing the “benefits” of this service to the developmental risks, the private business interests have clearly outweighed Facebook’s concerns for users’ well-being. While changing social interactions has long been Facebook’s trademark, MessengerKids threatens to alter interpersonal relationships by molding the children who form them and could additionally undermine data responsibility by normalizing online presence at an early age. It appears that Facebook is willing to risk the current generation’s trust to gain the next generation’s- a profitable, but not necessarily ethical decision.