Data Culture – Page 5 – DIG 210 (Spring 2015)

Disney: Behind Closed Doors

The Disney MagicBand is an interesting appliance. Rakim’s blog post explains greater research in the band and comes to the conclusion that the “more customized experience” might be worth the personal data breach.

I find it very interesting that on the DisneyWorld site (link below), there is plenty of talk about the MagicBand’s perks, including ease of room entrance, food purchases, and FastPass+ access to a multitude of experiences. But there is absolutely no notice of any of the “behind the scenes” features of the bands, especially Disney’s ability to track your every waking move. With this data, Disney can increase their efficiency, making more profits, while making more people “happy.”

I personally think it is an ingenious strategy and will ultimately do wonders for the amusement park. But I do believe that this trickery is not natural. Personally, the best part about Disney World for me was that everything was unexpected. I never knew what ride, structure, or cartoon characters I was going to see at every turn. It was exciting and incredibly appealing to me in my adolescence. I feel like the band takes away from this experience. It makes actions predictable and ultimately less fun for a couple extra bucks. The data will definitely make the park more efficient, but playing on people’s affinity towards “immediate payoffs,” (Dr. Sample’s Comment) could have consequences that affect the unpredictability of the park and human nature as a whole in the future.

https://disneyworld.disney.go.com/faq/bands-cards/understanding-magic-band/

Data about Ourselves

John Foreman’s article about data surveillance and machine learning as a means of gaining information about consumers took a unique approach to a topic in which we have previously discussed in depth. I personally really liked the article and the approach hat Foreman took, and his perspective on the issue.

When he started out with the example of the Disney World tracking device, it seemed as if he was trying to ease in to his argument with a somewhat funny and more lighthearted example. However, it divulges into one of his main points of corporations using data for profit. When Disney can track the different time and places that people do activities, it allows them to use this data to best attempt to maximize their profit and work to the needs of the consumers.

I also really liked how he differentiated between businesses using and NSA using personal data, and how he argued that we should prefer the NSA’s use, as he views the NSA of giving citizens more credit than the businesses. This was an interesting perspective, as the NSA spying my seem less obtrusive than simply tracking shopping habits at first glance.

Overall, I believe that extreme use of machine learning will begin to threaten humanities creativity. Speaking for myself, I am a creature of habit. I tend to frequent the same restaurants, vacation spots, and am a very routine oriented person. Will machine learning tracking everyone habits to the T, it will even lower the probability that people will branch out and push their creative boundaries. Although this can be very useful for company market campaigns, I believe that humanity must continue to evolve in a natural way.

http://www.corporate-eye.com/main/wp-content/uploads/2010/07/target.jpg

The Emotional Argument

John Foreman’s use of the doom-and-gloom tactic is interesting. On one hand it is a useful narrative that showcases his opinion; suppressed enthusiasm by the consequences of the technology. But is his negative attitude towards where the technology is pushing society any different than many headlines make? Should I stop drinking milk because it increases my risk of getting cancer? Should you stop reading this blog post on the computer that sits in your lap because it kills sperm count? Should you stop reading my nonsensical words on your phone that is sitting on your chest because it might cause breast cancer? Some of these cases are using worst case scenarios to make you believe whatever fact or opinion they have.

Bringing me to my second point, Foreman is using similar methods that an advertisement will use to push their product/opinion. He is attempting to illicit an emotional response so that his argument becomes more valid in the reader’s mind (the better his argument gets, the better his name is known, the more books he sells). Objectively, are his points valid? Certainly. Does his use of the doom-and-gloom tactic weaken his argument? Definitely not. This is how you win arguments, by presenting facts in a way that illicit an emotional response. Would you be persuaded by a presidential candidate if they just spit out facts, or would it be more effective to give you a sob story to showcase the facts? The answer is pretty clear (for the majority of people). So does using emotion fueled targeted ads destroy humanity? No because at the end of the day, you are making the decision to buy the good or service, and (depending on who you talk to) this ability to freely choose is what makes us human.

Old Information Systems and New

saint chapelle gold

Built in the mid 13th century, Saint-Chapelle, the windows of the of the church were probably the first “clock” that the Parisians had ever seen. Though lacking in precise accuracy, the displays were able to change the color of the chapelbetween night and day, and the shifting seasons. This is a great example to see just how far our information systems have gone. As we saw them boast in class, Apples’ new Apple Watch claims to be accurate to within 50 milliseconds of the actual time. Not only is it accurate, but it can sync with your phone to receive texts, calls, app notifications and much more. The advancement from old information systems too new is highlighted by speed, precision, and size. The old information systems were public, accessible to anyone anytime. As information systems have grown more complex, they have held more personal data. Data people may not be comfortable being public. It is in this struggle that people debate the privacy rights of information system users. What rights does Apple have to the information that is loaded up on their devices? Are they allowed to store that information? Can they sell that information?
eniac

ENIAC, completed in the early 1940’s by the U.S. army is credited for being the first computer in the world. This was just a general-purpose computational machine. It wasn’t until 1969 that information could actually be sent and received by different computers. The first nodes were UCLA and Stanford, only a few blocks apart from each other at the time. Now today, we have the ability to instantaneously send more information than ENIAC could process in a day any where across the globe. Health monitors implanted in peoples chests, camera’s that monitor eye movement for advertisers, and cookies downloaded onto my computer by websites track every moment of my life and are analyzed almost immediately. It is for these reasons that I agree with John Foreman that the mysterious humanity is destroyed. With enough many spent anybody can learn more about a person than the person probably knows about themselves.

Background of ARPANET: http://www.webopedia.com/TERM/A/ARPANET.html

Manufactured Happiness

Amidst all of the doom and gloom that Foreman generates in his discussion of data modeling, collection, and privacy, he repeatedly falls back on ‘happiness’ as the only benign product of invasive technologies and marketing strategies. He claims that at the same time that companies like Google and Facebook “increase our happiness”, they are also “destroying humanity as we know it.”

At the risk of making Foreman’s piece read even more negatively than it already does, I would argue that even the so-called “happiness” that we feel from walking about with Macbooks in our messenger bags or Disney MagicBands on our wrists is just as manufactured as the machines themselves.

Foreman talks quite a bit about how modern advertisements capitalize on our emotional vulnerabilities to make us associate, say, a cheap hamburger with sex. But he then seems to take at face value the ability of products to genuinely heighten the quality of our lives. Would he and his kids really have enjoyed his trip to Disneyland any less if Mickey hadn’t known which rides they’d been on? Alternately phrased, are the MagicBands actually a useful device, or are we just led to believe this because of Disney’s use of buzzwords like “effortless”, “magic”, and “enjoying the fun”? (These were all taken from the MagicBand FAQ page: https://disneyworld.disney.go.com/faq/bands-cards/understanding-magic-band/)

Coca Cola may be the most egregious offender here. In two words, they manage to create a causal relationship between opening a bottle and feeling better. If what they’re saying is true, I don’t know why more doctors don’t just prescribe Coke to treat depression.

If a company can convince us that their product will make us a happier human being, then the battle is already over. Trading personal data for happiness is suddenly an appealing enough swap – assuming we give thought to the transaction at all.

It’s certainly not an easy trade to conceptualize – “happiness” and “data” are two of the most nebulous and ambiguous commodities out there. I remember hearing in the news a few years ago when Google launched a service called Screenwise a few years ago that literally pays users to forfeit all of their Chrome browsing data. It got some buzz when it was first announced, but then promptly fell off the radar. Screenwise is still around, though it certainly isn’t one of Google’s top products or priorities.

I have a feeling that this is because Google recognizes that consumers are more willing to make sacrifices when the compensation is of a similar nature. In other words, people are OK with trading some tangible thing for another tangible thing (e.g. selling an old Nexus device for credit toward a new one) or something intangible for something else intangible (e.g., data for happiness). But people are less interested in swapping something vague (browsing history) for something concrete (Amazon gift cards). As soon as consumers are told that their data has real, monetary value, they become less willing to give it up.

Disney MagicBand

Searching through Disney’s site, I found more details about the MagicBand that John Foreman talks about in his article “Data Privacy, Machine Learning and the Destruction of Mysterious Humanity.” Although in the article Foreman talks about the technology as if Disney is going to hand these out to all those who visit, this is certainly not the case. Only those who who stay at a Disney Resort Hotel (or if you’re a Walt Disney World Passholder) will receive this MagicBand; if you are neither, you will simply receive a card when you purchase park admission. Yet, these MagicBand can be purchased online or at select retail locations at Walt Disney World Resort and beyond for a ‘low’ price of $12.95.

Even though, with the MagicBand, Disney is able to track their customers’ actions inside their parks, where you walk, what you eat, when you “stop to borderline-abusively yell at your kids,” Foreman does support this technology by stating that “even a little tracking of their children is a fine-trade off for a more enjoyable and convenient experience.” Yet, he fails to mention the other subsidiary benefits of the MagicBand. It can be used to unlock the door of your Disney Resort Hotel room, enter theme and water parks (with valid admission), check in at FastPass+ (ability to reserve attractions in advance) entrances, connect Disney PhotoPass images to your account, and charge food and merchandise purchases to your Disney Resort Hotel room. So my question is whether if you guys think this is worth an investment ($12.95 and privacy during your trip at Disney) for a ‘more customized’ experience.

I believe so.

Studio D’s Screens: Where We Look

DIG 210 is my third class in Studio D. As we all know, the room functions pretty differently from other classrooms on campus: moveable desks, a laptop cart, whiteboards around the perimeter, and four monitors make the space pretty flexible. Whereas most rooms contain rows of chairs that face in one direction, Studio D’s design makes students constantly change who (and what) they are looking at. For my observations this week, I focused on the room’s monitors: I know that I don’t have my “go-to” screen in the room, and was curious if other students switched the screens they looked at as well.

The diagram below shows the four monitors in the classroom. On Tuesday, there were three moments in which students were asked to look at a screen: the first and second related to blog posts, and the third related to our discussion on annotating the Apple Watch website. On Thursday, Moment 1 represents a review of the course syllabus, Moment 2 represents pre-Gephi discussion, and Moment 3 occurred after the Gephi demonstration concluded. The following results describe the screens students focused on for each of these moments:

Moment 1: Screen 1: 7, Screen 2: 4, Screen 3: 3, Screen 4: 3, Personal Laptops: 10

Moment 2: Screen 1: 6, Screen 2: 6, Screen 3: 3, Screen 4: 3, Personal Laptops: 9

Moment 3: Screen 1: 5, Screen 2: 2, Screen 3: 2, Screen 4: 0, Personal Laptops: 18

On Thursday, The following results describe the screens students focused on for each of these moments:

Moment 1: Screen 1: 1, Screen 2: 1, Screen 3: 1, Screen 4: 3, Personal Laptops: 20

Moment 2: Screen 1: 2, Screen 2: 0, Screen 3: 1, Screen 4: 10, Personal Laptops: 13

Moment 3: Screen 1: 2, Screen 2: 2, Screen 3: 2, Screen 4: 7, Personal Laptops: 2

Thus, it becomes clear that students in Studio D do not focus on a single screen; instead, they alternate the direction toward which they pay attention. I’m not sure whether this is a good or bad thing- or neither. It certainly does contrast sharply with typical classroom settings, at least. Tuesday saw more students looking at the screens across the classroom’s walls than on their own devices.

Incomplete data of students speaking up in class

Date	No. of times that students spoke up	No. of students that spoke up	The most times a student spoke up
3-Feb	26	15	4
5-Feb	10	8	3
10-Feb	17	12	4
12-Feb	18	11	3
17-Feb	23	13	5
19-Feb	15	9	3
17-Mar	40	15	7

Since last time being an observer, I decided to continue with a set of data in the following classes. The set includes the number of times that students spoke up in total, the number of students who spoke up and the most times that a student spoke up in each class. I tried to separate asking questions, or very brief conversational responses from a legit speak up. Thus, in my definition of speaking up, it should be a student that explains concepts, demonstrates thinking or answers questions. Any conversation that involves very short sentences or clarifications of certain words is not counted.

However, the data is heavily influenced by many possible factors. For instance, the attendance of each class could affect all three types of data. Also, the content of the class could affect the data, such as the “fish bowl” on Feb. 10th. Since “fish bowl” is not normal speaking up in class, the discussions are not counted into the data. Also, on Mar. 17th, the topic involves Apple Watch in which many people have strong interest. The total times of students spoke up is considerably high.

Nevertheless, the most times of a student spoke up in class doesn’t vary much. An assumption is that people tend to not to speak when they think they have already spoken up enough many times. From the data, the limit seems to be around 4 or 5 for people that usually speak up.

The data is incomplete since the collecting process was not consistent. Also, the counting process is performed by only me. I could possibly miss one or two times while taking notes or listening to anything that had my attention.

The Time Spent Talking Among the Class and Clothes for the Week

For Data Collection and Observations this week, I tracked and totaled the amount of time spent talking within the class. I tracked the amount of time spent talking by groups and professor Sample along with the amount of collaboration among the group.

Tuesday

29 total in class= 11 long sleeve shirts and 3 pants were worn.

Talking Among the Groups

Professor Sample- 23 mins, 25:58 seconds

Group 1- 1 min, 39:16 seconds

Group 2- 4 mins, 39:30 seconds

Group 3- 1 min, 27:20 seconds

Group 4- 2 mins, 14 seconds

Group 5- 2 mins, 56:13 seconds

Group 6- 1 min, 44:46 seconds

Group 7- 1 min, 35:23 seconds

Group 8- 1 min, 23:42 seconds

Collaboration Among the Groups- 33 mins, 54:52 seconds

Longest streak of groups responding to each other- 9 (when talking about Genius.com)

Professor Sample spoke the most whenever introducing the next part of the lesson for the day. Within the different sections of the lesson, student groups controlled the flow of conversation.

Thursday

29 total in class= 19 long sleeve shirts and 25 pants were worn.

Talking Among the Groups

Professor Sample- 16 mins, 52:04 seconds

Group 1- 22:01 seconds

Group 5- 8:21 seconds

Group 6- 5:12 seconds

Collaboration among the groups- 57 mins, 32:22 seconds

Longest streak of talking among groups= 2

Lots of Collaboration due to the workshop.

Class Participation, Bracket Style

Since we’re in the middle of March Madness, I decided to track class participation in a bracket-style tournament format. A student advanced each time he or she directly addressed Dr. Sample in class discussion, or if Dr. Sample called on them to answer a question. One could only advance if there was an opponent available to defeat (therefore, someone who answered five straight questions would only win one matchup, not five). Although I could have played the role of the selection committee and “seeded” the field, I chose to use the list of names from the blogging groups page in the order they are listed.

The results from Tuesday’s class are below:

observations 3.20

Unfortunately, we ran out of time on Tuesday to determine a single winner. Also, the Gephi workshop on Thursday prevented the bracket format for serving as a good measure of participation (since we spent more time discussing in tables rather than as a whole class).

Although the bracket exercise was certainly amusing, it doesn’t appear to be the best way to determine the “best” participants; a lot of people who were bounced in the first round ended up speaking a lot later in the class, often more than the people who originally eliminated them. Perhaps the same critique could be applied to the NCAA basketball tournament as well.