{"id":931,"date":"2019-09-05T14:00:10","date_gmt":"2019-09-05T14:00:10","guid":{"rendered":"https:\/\/ctlj-dev.cu.law\/?p=931"},"modified":"2019-09-05T22:07:25","modified_gmt":"2019-09-05T22:07:25","slug":"a-day-in-your-data","status":"publish","type":"post","link":"https:\/\/ctlj-dev.cu.law\/?p=931","title":{"rendered":"A Day in Your Data"},"content":{"rendered":"\n
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Artwork by Oxyman.<\/a><\/figcaption><\/figure><\/div>\n\n\n\n

Digital privacy rights remain a fleeting uneasy feeling to most people, mildly relevant during Facebook\u2019s Cambridge Analytica Scandal<\/a> or data breaches from companies like Equifax<\/a> and Capital One<\/a>. Yet, Consumer data is continuously harvested and utilized in a never-ending effort to turn ‘cookies into cash’. Your information, or rather information about<\/em> you, is big business, more lucrative and exponentially expanding than oil<\/a>.<\/p>\n\n\n\n

This pervasive, essentially unavoidable<\/a>, collection and commercialization of data creates extensive social costs<\/a> (legalese for knock-on effects in the economy that hurt people<\/a> \u2013 like pollution). While some social costs are moderate, who might see a job notice for example, some social costs are extreme.<\/a> As conversations inappropriately recorded and retained by \u2018smart\u2019 home assistants like Amazon\u2019s Alexa are used in court<\/a>, and automated systems without transparency, or indeed even accuracy or accountability, are used to determine sentences, calculate recidivism risks, or award bail<\/a>, the social costs created from data utilization may eclipse their benefits in certain contexts.<\/p>\n\n\n\n

The data economy is full of risks and<\/em> benefits. In the U.S., consumers are largely responsible for navigating these issues, and are typically bound to whatever decisions they make. Those decisions are of limited value however, as companies routinely disregard their own policies or violate expressed consumer choices; Google retained and utilized the location data<\/a> of consumers despite those consumers turning off location services in their settings, just as Apple violated its own commitments not to share consumer listening data with third parties<\/a>. When evaluating a data decision, consumers must evaluate not only the decision, but also the risk that they are engaging with a bad actor.<\/p>\n\n\n\n

To help illustrate our contemporary data reality, the CTLJ Digital Content Team crafted a brief hypothetical timeline of the average day of a CTLJ member, given the nom de plume \u201cBob\u201d to protect their privacy, that catalogs how data about us is implicated in every facet of daily life.<\/p>\n\n\n\n

5:30AM \u2013 Rise ‘n Shine<\/strong><\/p>\n\n\n\n

On weekdays Bob wakes up at 5:30AM. He uses a Google Assistant \u201croutine\u201d to reads the forecast for the day, based on his current location. It also plays the most recent episode of the WSJ Tech News Briefing.<\/a><\/p>\n\n\n\n

Bob is receiving some real benefits here, and without spending any money. Benefits include the alarm itself, the forecast, and the podcast. These are real benefits, and most would agree that they are a good result for Bob. <\/p>\n\n\n\n

Google also collected useful information about Bob that they can sell, in some form or another, to generate revenue. They track what time he typically gets up in the morning, and potentially how often he snoozes his alarm, which will be integrated into an advertising profile, and possibly a psychographic profile, similar to those created by Cambridge Analytica<\/a>. They also track his current location and interests based on the \u201croutine\u201d, which could also be used to tailor advertisements, alter the offering of services, influence prices for certain goods<\/a> for Bob, and may ultimately impact which job or housing postings he has access to.<\/p>\n\n\n\n

5:45AM \u2013 Struggling to Get Out of Bed<\/strong><\/p>\n\n\n\n

While Bob does occasionally hop straight out of bed in the morning, all too often he spends about 15 minutes lingering under the sheets scrolling through Twitter or Reddit, or watching videos on YouTube. Sometimes Bob stays in bed under the covers all day.<\/p>\n\n\n\n

For every YouTube article or Reddit link Bob clicks, more information is added to his various profiles, updating targeting algorithms to serve him ads that he is more likely to find relevant. Most large data collection firms like Facebook have their proprietary profiles<\/a>, even if you don\u2019t use their service, while data brokers and other ill-defined entities create and trade their own.<\/a><\/p>\n\n\n\n

7:30AM \u2013 Hitting the Gym<\/strong><\/p>\n\n\n\n

If everything is going well for Bob so far this morning, he\u2019s hopefully getting to the gym around 7:30AM. He uses a Garmin Fenix watch which tracks his location and heart rate. Garmin also has an app where Bob can enter details on his height, weight, and age. Their app also contains \u201cbadges\u201d that incentivize Bob to do things like record \u201cactivities\u201d for 7 days in a row, climb a certain number of floors in a day, or run a certain number of miles in a week. These \u201cbadges\u201d show up on a public profile where Bob can compete with locals or people around the globe at a similar level. 
<\/p>\n\n\n\n

While Bob enjoys these training metrics, and derives motivation from the gamification of workouts, Garmin likely collects a swath of information about Bob. This data may be used in ads, it may also be eventually used to determine his health insurance coverage and premiums or even his ability to get or keep a job<\/a>. Health trackers in particular have also been implicated in national security issues, revealing not only the location of security installations but also potentially patrol routes and patterns of activity within these installations<\/a>.<\/p>\n\n\n\n

8:45AM \u2013 Morning Coffee<\/strong><\/p>\n\n\n\n

On his way home from the gym, Bob might stop to grab a cup of coffee from his local coffee shop. This purchase will be tracked in a number of ways. If location services are enabled on Bob\u2019s phone, and likely even if they\u2019re turned off, Google tracks that he visited the Brewing Market on Baseline, and may prompt him to write a review, in addition to storing that information in his profile and likely sharing it with third parties. If Bob uses his credit card, Wells Fargo tracks when, where, and how much money Bob just spent. If Bob forgot his wallet at home and uses Google Pay, his transaction information is also shared with Google. Additionally, the WiFi router in Brewing Market likely logs when Bob enters and leaves the area and shares it with third parties<\/a>, even if Bob\u2019s phone is in \u2018airplane mode\u2019.<\/p>\n\n\n\n

12:00PM \u2013 Lunch<\/strong><\/p>\n\n\n\n

On most days Bob packs a lunch, but occasionally he has a lazy morning and resigns himself to eating out for lunch. Because Bob is perpetually indecisive, he usually spends a fair amount of time searching around for a new place to eat before eventually giving up and heading to Qdoba. As he tools around on Google Maps mulling over the menu choices at the newest food truck or fast casual restaurant, every single click is recorded and analyzed. 
<\/p>\n\n\n\n

Because Bob has location services enabled on his phone, after he leaves wherever he went for lunch that day, he us immediately prompted with a notification asking him how his experience was and prompting him to write a review. Both Bob\u2019s visit itself, as well as any potential review he posts, will be used to improve his search experience next time, as well as to determine if Bob is a good prospect for a job, a service, or advertisements. These profiles can become so accurate at predicting and interpreting human behavior that some people are convinced that their phones are being used to surreptitiously record them. That is possible, but highly unlikely \u2013 several studies have debunked this claim<\/a>. The reality is these algorithms and profiles are finely tuned with access to nearly limitless data about you, so they are simply that good.<\/p>\n\n\n\n

1:30PM \u2013 Return to Work<\/strong><\/p>\n\n\n\n

Whether Bob is trying to catch up on readings or email, the first thing he does after booting up his computer is fire up a music app. Bob\u2019s usual choice when he\u2019s trying to be productive is the YouTube channel \u201clofi hip hop radio<\/a>\u201d (seriously, check it out). As Bob has been going about his day, algorithms have been hard at work updating his profile with all the data they\u2019ve collected. Perhaps he liked a post by one of the outdoor gear companies he follows on Instagram, so he might receive an ad for a new piece of gear, or maybe Google allowed a third party to analyze his emails <\/a>and found that Bob has an ailing relative, and he might mysteriously receive an ad for a new breakthrough drug for the disease they were just diagnosed with. <\/p>\n\n\n\n

5:30PM \u2013 Grocery Shopping<\/strong><\/p>\n\n\n\n

Before Bob heads home he takes care of errands, which usually entails a stop for groceries. Most of the time Bob heads for Whole Foods, tempted by the \u201cPrime Deals\u201d available to Amazon Prime members. Bob is incentivized to use his Prime account every time he shops to receive savings, but at the same time he\u2019s providing Amazon with information including what he likes to eat, how often he buys certain items, and when during the day he likes to shop. All this information can be useful to target advertisements for Bob down the road, as well as evaluate his health, future earning potential, or likelihood for certain personality traits.<\/p>\n\n\n\n

Data utilization is a mixed bag. There are risks and real benefits to utilizing behavioral data. Perhaps Bob found the more tailored advertising content more relevant than the generic ads for pharmaceuticals that plague network television. Bob likely derived real benefits from the recommendation received from Google on a new taco truck that he should try in the area. Platforms can also use this data to better understand consumer preferences to deliver higher quality content. Netflix uses behavioral data to decide which shows to greenlight, make decisions about the creative team and casting for origional content, and better source recommendations<\/a> for existing content to users. According to a recent survey, 39% of consumers thought that Netflix had the best original content <\/a>(vs. 14% for HBO, and 5% for Amazon Prime Video).  These are real, tangible benefits that cannot be discounted when examining data governance<\/p>\n\n\n\n

There are also real costs caused by these data mining operations, that aren\u2019t often visible on the surface. For example, when asked about the profiling tool Cambridge Analytica built to study Facebook users, former employee Brittany Kaiser called it \u201cweapons grade\u201d technology<\/a>. When behavioral data is used to sell us more widgets or promote new content, there is likely a net benefits to society. When data about us is used to influence electoral outcomes, restrict personal and professional opportunities, or expose our deepest secrets, the costs loom.<\/p>\n\n\n\n

Join the CTLJ Content Team at the Silicon Flatiorns Near Future of U.S. Privacy Law Conference<\/a> on September 6, 2019 at CU Law. Luminaries in the field of data governance and privacy will examine these issues, in particular the possibility of a new Federal Law on data privacy rights.<\/p>\n\n\n\n


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Digital privacy rights remain a fleeting uneasy feeling to most people, mildly relevant during Facebook\u2019s Cambridge Analytica Scandal or data breaches from companies like Equifax and Capital One. Yet, Consumer […]<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-931","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=\/wp\/v2\/posts\/931","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=931"}],"version-history":[{"count":7,"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=\/wp\/v2\/posts\/931\/revisions"}],"predecessor-version":[{"id":953,"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=\/wp\/v2\/posts\/931\/revisions\/953"}],"wp:attachment":[{"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=931"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=931"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ctlj-dev.cu.law\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=931"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}