(You can’t) Have it your way: The Data Double Standard

Tuesday’s class ended with a discussion of involuntary data collection, or the process by which organizations gather information on individuals without expressed permission. Thinking of my upcoming spring break trip, I argued that airlines participate in this form of data collection without explicitly letting customers know. A November 2013 Wall Street Journal article on the subject notes the numerous pieces of data airlines obtain from travelers: American Airlines informs gate agents about past traveling history and frequent flyer status, JetBlue informs crew members about birthdays, and British Airways allows flight attendants to write comments about traveler behavior, including fear of flying. This involuntary data collection shouldn’t be surprising. We often provide our personal information to companies in exchange for some benefit, including airline miles, elite status, and a more personalized experience- as outlined by the WSJ article. Airlines are the big fish in this example; we have minimal power in keeping our data from them.

Growing technology-and easier access to data-has recently created a flip flop in this hierarchy: now, the customer can involuntarily mine data on the airline. Aktarer Zamen, a 22 year-old entrepreneur, faced lawsuits by United Airlines and Orbitz Travel in December after the two large companies outlined objections surrounding his site, Skiplagged.com. Skiplagged plays upon airlines’ use of supply and demand. Flights do not necessarily increase in price as distance increases. For example, a flight from Nashville to Boston with a layover in Atlanta may be cheaper than just taking the first flight from Nashville to Atlanta. Skiplagged would find this discrepancy and tell a customer traveling to Atlanta to book through Boston but not board the second flight. Zamen’s website mines airline data to, like the airlines who work to “personalize the flying experience” (WSJ), “help travelers” receive the best possible product (CNN). Whereas airlines mine the customer’s data, Skiplagged serves as an example of a customer mining the airlines’ data.

Airlines clearly stand aware of how powerful the data they mine from customers must be. If they weren’t, there would be no reason for them to be afraid of Skiplagged and other startups that mine data on them. In the future, airlines and large travel sites may not be the only fish mining data in the travel pond.

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http://www.wsj.com/articles/SB10001424052702304384104579139923818792360

http://money.cnn.com/2014/12/29/news/united-orbitz-sue-skiplagged-22/index.html

Freedom of Choice

In Alice Marwick’s article “How Your Data Are Being Deeply Mined,” she gives an example of a consumer shopping for shampoo. In this scenario the customer is undecided in his/her choice and is picking up several different bottles. In this futuristic store the consumer’s eye movements would be tracked and a coupon for a specific bottle could be printed from the shelf, urging the customer to make a specific decision.

This scenario brings up a theme that is not only related to data collection, but has been in a pressing concern in America since its beginnings. This is the freedom of choice and whether or not anyone but the individual has or should have the power to make decisions for them. People in the United States have always held the power of choice as very important. From resisting taxation without representation before the Revolutionary War to resisting Bloomberg’s decision to ban certain drink sizes in NYC, American people have always valued the ability to make their own decisions. In the shampoo example, a customer’s data in combination with their eye movements results in a third party trying to make their decision for them. It’s as though the advertising company knows better than the consumer. Based on the historical examples in this country I don’t think this will go over well if it ever becomes a reality.

http://archive.onearth.org/blog/does-the-food-lobby-really-care-about-consumer-choice
http://archive.onearth.org/blog/does-the-food-lobby-really-care-about-consumer-choice

Marwick addresses this problem briefly when talking about how Target began sending pregnant women coupons directed towards their specific needs. Instead of responding positively, the women didn’t like that Target knew they were pregnant. Target was forced to mix other advertisements in with those directed towards pregnancies in order to get the women to use the coupons. I feel as though the same end may come to shelf coupons. Instead of pushing one produce heavily I think they will have to give multiple options so consumers still feel as though they have the ultimate power in their decision.

Observations of 2/10 and 2/12

I was not really sure what I was going to observe at first, so I put a list together of some of the observations that I made during class. A lot of it ended up having to do with what the class was wearing on Tuesday and Thursday and whether or not the weather becoming colder than it had been would make any difference in what people were wearing. Overall I noticed that the numbers were pretty close to each other over the two days.

February 10, 2015

  • People in class: 28
  • Drinks: 9
  • Sweat shirts/Jackets on in class: 10
  • Collared shirts: 8
  • Long Sleeves: 27
  • Short Sleeves: 2
  • Glasses: 5
  • Hats: 0
  • Nike shoes: 7
  • Laptops: 14
  • Things Written on board: 1
  • Khakis: 7
  • Sweatpants: 5
  • Shorts: 1
  • Jeans: 12
  • Leggings: 3
  • Davidson Apparel: 5

February 12, 2015

  • People in class: 28
  • Drinks: 11
  • Sweat shirts/Jackets on in class: 13
  • Collared shirts: 8
  • Long Sleeves: 28
  • Short Sleeves: 0
  • Glasses: 6
  • Hats: 4
  • Nike shoes: 6
  • Laptops: 15
  • Things Written on board: 0
  • Khakis: 9
  • Sweatpants: 8
  • Shorts: 0
  • Jeans: 8
  • Leggings: 3
  • Davidson Apparel: 3

 

 

Datacide

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DATACIDE: The Total Annihilation of Life as We Know It

An excellent article I recently read talks about how data, algorithms, and connectedness have changed our world and made us aware of heretofore hidden aspects of our collective selves..

I’ve seen the best minds of my generation sucked dry by the economics of the infinite scroll. Amidst the innovation fatigue inherent to a world with more phones than people, we’ve experienced a spectacular failure of the imagination and turned the internet, likely the only thing between us and a very dark future, into little more than a glorified counting machine.

Am I data, or am I human? The truth is somewhere in between. Next time you click I AGREE on some purposefully confusing terms and conditions form, pause for a moment to interrogate the power that lies behind the code. The dream of the internet may have proven difficult to maintain, but the solution is not to dream less, but to dream harder.

Invisible Observations: Week of February 2nd

Tasked with making observations and gathering data for two DIG 210 classes, February 3rd and 5th, I sought to try something unconventional. Rather than focus on visible characteristic(s), I thought I might gather data about the auditory aspect of our class. Given that we exchange knowledge in our class primary over the medium of sound, through our voices, I thought it might be interesting to transpose the sound waves that reverberated through Studio D of the Davidson library from 1:40pm to 2:55pm on those two days into a static 2D image that we can view and make observations about.

Sound Waves Captured on February 3rd
Sound Waves Captured on February 3rd
Sound Waves Captured on February 5th
Sound Waves Captured on February 5th

There are several limitations to this method of observation. One is the fact that my computer’s microphone was the sole conduit for generating data; being in one place and not designed with high-fidelity audio capture in mind, the data that my computer generates are limited in accuracy. Sounds that I or others at my table made register more prominently than equally loud sounds made by others. These data paint a picture from the vantage point of my computer, which on both occasions I tried to position as close to the center of the room as possible. Finally, my skills in audio analysis are extremely limited; with more expertise and better software, I would be able to provide many more statistics that might allow us to glean more information from these data.

The blue lines vertically indicate amplitude of the sound waves over the horizontal axis of time. We notice by comparing the two that the February 5th class was, on average, louder than the February 3rd class – given that a guest speaker spoke to the class through many speakers on the 5th, whereas Dr. Sample primarily lectured using only his vocal cords on the 3rd, this makes sense. We notice in both graphs more peaks toward the end of the class as opposed to the beginning, which could be the result of many factors – perhaps the conversation becomes more heated and interesting when the class is more involved in the material?

Note: I have chosen to not make the actual sound files available for a variety of reasons.

The Atlantic – ‘The Cloud’ and Other Dangerous Metaphors

Interesting read from The Atlantic:

Underlying the discussion has been a tangle of big, thorny questions: What policies should govern the use of online data collection, use, and manipulation by companies? Do massive online platforms like Google and Facebook, who now hold unprecedented quantities of sensitive behavioral data about people and groups, have the right to research and experiment on their users? And, if so, how and to what extent should they be permitted to do so?

…

Data escapes attempts to fit it neatly into a single conceptual box. Consider three phrases—now so commonplace as to be unremarkable—that we use to talk about data:

  • “Data Stream,” which refers to the delivery of many chunks of data over time;
  • “Data Mining,” which refers to what we do to get insightful information from data; and
  • “The Cloud,” which refers to a place where we store data.

These tropes are notable because they use distinct, physical metaphors to try to make sense of data within a specific context. What’s more, all three impute radically different physical properties to data. Depending on the situation, data is either like a liquid (data streams), a solid (data mining), or a gas (the cloud). Why and how these metaphors get used when they do is not immediately obvious. There are tons of alternatives: Data could be stored in a “data mountain,” or data could be made useful through a process of “data desalination.”

…

And in all our talk about streams and exhaust and mines and clouds, one thing is striking: People are nowhere to be found. These metaphors overwhelmingly draw from the natural world and the processes we use to draw resources from it; because of this, they naturalize and depersonalize data and its collection. Our current data metaphors do us a disservice by masking the human behaviors, relationships, and communications that make up all that data we’re streaming and mining. They make it easy to get lost in the quantity of the data without remembering how personal so much of it is. And if people forget that, it’s easy to understand how large-scale ethical breaches happen; the metaphors help us to lose track of what we’re really talking about.

X-Post: Economic Adaptations of Big Data

Wanted to share a post that I wrote for my Digital Anthropology class at Davidson that discusses how business are attempting to shift to Big Data cultures:

bigdata_large

“Big Data”

epitomizes a buzz word. Tech blogs began following the emerging field of analyzing very, very, very large amounts of data that humanity is generating at an unprecedented pace about 10 years ago. What was once an interesting exercise has become much more as startups have built tools that allow businesses to gather and analyze big data in real time. Tools like Hadoop and MongoDB are few among many of the entrants vying to lead the big data revolution that transcends mainstay relational SQL databases in favor of NoSQL databases that hoard every last datapoint that they can, even if those data provide no perceivable analytical value at present.

While many organizations are waking up to the potential of big data, most are slow to understand it. Companies are quick to advertise that they “leverage big data analytics” to “provide value” and discover “new synergies”, but more often than not they are putting a new dress on the same dog. While tools to harness big data are developing rapidly, people can be the limiting factor in catalyzing a shift to big data culture. This was recently espoused by Mark Gazit in an op-ed on TechCrunch. He cites research that catalogues the talent drought for data scientists, noting that there simply isn’t enough human horsepower available for companies attempting to integrate big data into their core business models. Rather than calling for more training programs, Gazit champions the efficacy of machine learning and artificial intelligence in bringing big data to the masses:

Ultimately, by solving the issues that prevent full optimization of big data analytics — especially the human factor and its disproportionate impact on the current-day process — organizations will be able to detect and address all types of threats and opportunities much more rapidly. This capability is becoming increasingly crucial in an era when data is being generated by both humans and machines, and is sure to become a pivotal way for businesses to create situational awareness, detect issues and optimize operations to achieve their business objectives. – Mark Gazit, CEO of ThetaRay

It remains to be seen how machines and humans will complement and subordinate one another as the data economy continues to evolve. Greater efficiencies, lower costs, and more predictability are inevitable – the question is, who will we have to thank?

Note: I am sharing this post out of interest and relevancy, not for any form of academic credit. This post was created as an assignment for a separate class. 

Are We Trapped?

net-privacyhttp://cdn.ubergizmo.com/photos/2010/12/net-privacy.jpg

During our class discussion and activities, we payed particular attention to a simple phrase from our reading, “From Big Brother to the Electronic Panopticon” by George Lyon. This phrase was “made visibility a trap” (Lyon, 66). As the various groups dissected this phrase to modernize the meaning, many themes came up throughout our various re-wording of this phrase. The general meanings of these re-wordings were along the lines of  “Invisibility is impossible” or ” Digital convenience/efficiency takes away privacy”.

Although these ideas resonated with me, during our discussion the word “traceable” kept popping into my head as an over-arching theme or problem with constant dataveillance. Although this word can be closely tied and related to visibility, I believe that there are differences between the meanings and implications of these words. Although it is true that with the technological age almost everything we do can be visible to a government, website, or company, it is the implications of this visibility that make it a trap. This visibility leads to a vast amount of information about ourselves being able to be traced  by virtually anyone. From every google search, credit card purchase, magazine subscription, or song downloaded; technological advances make these everyday activities traceable to people who wish to seek this information. As someone in class said, this can be seen as a positive to gain information on people to gauge their loyalty or potential, I personally view this as an unnerving reality. People can gain absurd amounts of insight on virtually anyone, and the intentions of this curiosity can be for be both positive or negative reasons. The idea that people can trace such vast amounts of information about a persons life leads me to believe that visibility is a trap because it leads to the traceability of a personal information.