Want to Play?

The article we read by Jennifer Whitson focused on how gamification of the quantified self can constantly adjust one’s daily lifestyle to adapt to a “better” life as seen by the designer. Whitson references Nike+ as one of the quantified self apps that allows the user to track their workout routine and observe the quality of their health throughout their day. By accessing this information and creating visualizations of this data, the user can then interpret this to find ways to get closer to the goals that the app has created for them. This use of the person’s data is not seen as surveillance in a negative way because people view this as an attempt to help them become healthier. However, there is no serious delineation between the surveillance of Nike tracking your phone for “workouts” and the government tracking things such as your phone to know where the phone and user are. This poses a serious question to viewers that if we allow apps like these? She does not delve into this divide because she feels it is a slippery slope, but is very important for viewers to consider these similarities.

Whitson also addresses how gamification is only applicable if the person is actively interested in the game. She says, “for it to be experienced as play, everyone needs to be a willing participant”. This thought as well as her belief that “rules are locally situated and constructed by the participants” provides the possibility of reverse analyzing this, to find out what games we are playing subconsciously, in our everyday lives? After reading this we realized that things such as weighing in after a meal or such, was still some form of a game that we would have never considered.

Where do we draw the line on how much we want to be tracked versus what that tracking can give us access to? Also, so much of our life is about a quantified-self game, when do we get to stop playing, or do we even really have control over the game anymore?


Week 9 Response

Group 3’s post and this week’s readings/discussion have been focused on how, or even if, digital society has effected the person-to-person society or the “real” world. Boesel delves into the idea of the “quantified self” and how its popularity has exploded over the past decade whether it be for the health-minded academics of the future or the “fiendish, delusional narcissists,” while Mejias takes the structure of a modern network and applies it to a society integrated by social media. While both seem to believe that the digital society has taken root in the real world and that this is a positive thing, these are still up for debate. Most importantly, it is neither good nor bad. Our digital society definitely affects the ability and kind of networking, development of ideas, and day to day communication between physical bodies but can never replace the connection of 1-on-1 interaction.

One of the most interesting aspects of social media platforms like Facebook is the creation of not just a supplementary connection for those people we see from day to day, but an alternate connection for those who we never plan to meet. We find ourselves creating a digital connection that is either redundant in that it is trumped by constant human interaction or potential in that the satisfaction of being validated by the creation of a new connection comes before the necessary work.

But nonetheless, digital society allows like-minded people to gather and trade ideas on the free market from the comfort on their own screens. While this may sound wonderful, and in practice it allows for the abundance of support and advice where it is needed and unfortunately unavailable, it leads to the false pretense that all ideas can be expressed without ramification in day to day life from person to person.

Finally, due to this newfound ability to express without ramification, many attempt to bring the impulsive, reactionary tendencies of the digital society and are met with much resistance due to the ever-present fears of judgement, criticism, and embarrassment of the person-to-person interaction.

With all mind, it’s easy to see that physical interaction will always be the mainstream of interpersonal communication whether it be out of desire, efficiency, or simple necessity. In the same way the computer was to eliminate the printed page or the escalator to the simple staircase (look how far we’ve come), the digital world will never be able to surpass the the physical though we have tried in emojis, expressive acronyms, and gifs. Even the “friend,” the “follow,” and the “like” fail to match the real-life counterparts as we continue to specify the site that they derive from.


Observer’s Data Week 8

For our second round of observations, we decided to collect data on classmates’ travel over Fall Break by sending out a survey asking about whether or not they travelled, where, by what means, and why.


This is the complete travel plan for the class over Fall Break using Google’s travel plan app with 5 people driving and 1 person flying.




We also took into account the different distances of each trip. Although the flight to New York was the longest, many were not phased by the notion of driving almost the same distance. The total distance travelled by the class over Fall Break was 2105 miles. If driving a Hummer, that would cost you upwards of $500. If riding a Moped, it might cost you $10.

Data Ethics 2: Electric Boogaloo

The advent of social media has brought with it a new kind of data, one that Lev Manovich thinks could bridge the gap between surface and depth that has historically limited scientists and anthropologists in the study of the human condition. Due to the sheer size of what we can now collect and analyze, now on such a scale that normal computers can not handle the load, a scientific study in today’s world could incorporate hundreds of millions of people, each with their own experiences, preferences, and desires.

But what is the “authenticity” of these extensive sets of data? Manovich poses the argument that our data is not a transparent window into our lives as some would think. His most extreme example is that of someone living in an oppressive state where thoughts and actions must be tailored to governmental preferences. Yet while many do not find themselves in this position, the constant burden of social anxiety and the desire to conform may act in place of Big Brother.

On the other hand, Alice Marwick moves from the benefits and potential detriments of this type of data in the scientific world to the darker, more deceptive world of grand-scale data collection and it’s ethical shortcomings. At the article’s time of publishing, The Guardian had very recently published a massive leak of NSA data and protocols creating a rabbit hole that is still getting deeper and deeper today. But, in the same light, Marwick writes about the many instances in which companies, primarily Target, have begun to use this type of data collecting to tailor advertisements towards their customer’s preferences; many times succeeding in being more creepy than helpful. So where do we draw the line between what is ethically correct and not when companies undergo many of the same types of collection and prediction without any of the responsibility of a government agency?

From an even more frightening standpoint, Marwick moves onto the topic of data brokers, companies that not only collect data from hundreds of millions of people each year but put it on the market, in many cases nonchalantly. Acxiom, her main example and the largest data broker in the business, has an untarnished record. Their competitor Experion has not had such good luck. On one occasion they sold personal data to an identity theft ring while on another they sold to a Vietnamese hacker. What is the ethicality, if any, in these forms of business when it comes to personal information?


By Judson, Griffen

Observer’s Data (Week 2)

As Observer’s this week,  we attempted to “guess” the class’s favorite color by taking note of their clothing and accessory color choice. We took into account clothing color including shoes as well as things like backpack, water bottle, and phone case color. We then found a rough percentage of likelihood based on the frequency of that color in each person’s outward appearance.


Display 1: A complete showing of our data.


Display 2: A modified display with the neutral colors removed.


Display 3: A modified display with neutral colors and least frequent secondary colors (purple, orange) removed. Instead of removed them entirely, the secondary colors were accounted for by additions to their primary components.

How close were we?