Internet Addiction TEDx Talk

Here is the TEDx talk about internet addiction that we started watching during my presentation. The video is less than 20 minutes long.

https://www.youtube.com/watch?v=vOSYmLER664

Article: Can AI Help Gender Diversity Help AI?

http://motherboard.vice.com/en_uk/read/can-ai-help-gender-diversity-help-ai

Cognitive and Psychological Effects - Data, Numbers, Machines

That Facebook numbers trick is so obvious right after someone tells you about it. I never gave much thought to the reasoning behind that, which in hindsight seems really odd. That whole racking up likes thing does kind of give you a warm fuzzy feeling, but the whole 1,682 friends on Facebook thing is a little nuts. I've never even met that many people, let alone that many people I would call, even in the loosest of definitions, friends.

Of course, one of the results of having taken more than my share of social psychology-related classes is that I'm familiar with the concept of Dunbar's number,  If you aren't familiar, it's a social science concept that suggests we have a cognitive limit on the number of stable relationships we can manage at one time, and that number is 150. So basically, however many friends you have, you have 150 friends.

Anyway, I installed the Facebook Demetricator because I'm curious. Does it actually make Facebook a more pleasant and relaxing experience? We'll see.

I have a pretty fair comprehension of algorithms and was still a little shocked at just how far they go. I mean, seriously. I'm a Mac user and there's no way I'm going to choose anything that can be classified as more expensive because I'm also a broke college student.

The implications of their capabilities, though. I always thought of algorithms as basically helpful if maybe slightly irritating sometimes. Helpful in the sense that they're shortcuts, irritating in that sometimes they don't work exactly right and Netflix not only can't remember what episode of Gilmour Girls you were watching last night but it keeps trying to show you cheese documentaries.

It stands to reason though that if machines can learn, they're going to pick up things they shouldn't or process normal things in ways they shouldn't. Kind of like toddlers. Maybe exactly like toddlers, mimics who don't really comprehend that they're saying something they shouldn't say, they've just picked it up from the world around them. Or been taught to say it by someone who thinks they're funny but is really just a little bit evil.

Teaching them seems like a really large job, but regulating the exploitation of them seems like an even bigger one. I found this whole concept kind of fascinating, in a  horrifying sort of way, and my horror was not dispelled when I ran across an article about the Chicago PD's heat list.

Basically, they got this really big grant ($2 million) to set up and experiment with an incredibly high-tech piece of computer gadgetry that has allowed them use algorithms to profile people based on a set of criteria that included geographic area, volume of emergency calls placed, previous interactions with the PD, and a whole slew of other items that generated them an index they called the heat list.

Not surprisingly, there were people on it who never committed a crime or had any kind of run in with the police whatsoever.

Seems legit, though.

Week 15 Reading Response

NUMBERS ON FACEBOOK

I mostly agreed with this reading but I think the author overestimates in parts the influence of the numbers on certain user behaviors on Facebook. I'd like to think for example that sending and accepting friend requests is still more influenced by human discretion than it is by our obsession with numbers. There's a host of reasons why, to each individual person logging on to the social media platform with histories and experiences and prejudices, etc, a "+1" shouldn't just be a "+1." But what do I know? The author does state that many view their friend counts as status markers and I do have have a friend who once said he had to start an additional Facebook account long ago because apparently they'd reached the limit on the number of friends they were allowed to keep on Facebook.

THE PROMISE OF BIG DATA

On the subject of human discretion (maybe there's a better term for this), I read this article and one of the things that jumped out at me was the part about how the people who volunteered through crowdflower to help analyze Sarah Fortune's "movies" had no scientific expertise. Generally, I tend to come at crowdsourcing from a cynical standpoint because as someone who works in the creative field, I've seen enough companies use crowdsourcing as a way to take advantage of people eager to prove their creative worth. The end results almost always end up sacrificing nuance that comes with applying expertise to the problem in favor of something that is cheap and crowd pleasing. I'm not sure how this translates in the field of scientific research but I suspect having a large group of non-scientists analyze scientific data comes with its own problems.

SOCIAL BIAS

This article had me asking the question: can sensitivity, perspective or awareness be programmed into software? I suspect the answer is yes but I take the many unfortunate examples provided in the article of software displaying bias as just another negative effect of a deeply rooted culture in the tech industry where heterosexual white males see a lot more representation than any other group.

HYPOCRISY

I have little to add after reading this article because I honestly don't fear website trackers like most others do. I'm especially indifferent to fears about google analytics being embedded inside web pages. Maybe it's just my experience with google analytics. It doesn't strike me as some nefarious thing that companies pay big bucks for (it is readily available to anyone with a google account) and I suspect the same for some other trackers. I've used google analytics code on many websites I've designed and I can say that the end goal there for the clients have always been to help make the user experience better by seeing how people were engaging the content. I'm not saying this is the same for all analytics software and all trackers but I just find it mildly amusing when something I use and check often gets lumped in with big, scary tracking softwares.

The Opacity of the Algorithm

Sorry this was late! I ended up having to work late today unexpectedly.

Big data is so hard to conceptualize, it is difficult to have a coherent opinion on it.  I installed Ghostery after our discussion about it in class a few weeks ago, and the sheer amount of hidden third-party activity going on in the background of my day-to-day existence on the internet is astonishing.

Before I started getting automatic reports on the dozens of advertising and analytics companies that involve themselves in my web browsing, I think I would have been a little dismissive of Norton’s article on Medium.  Sure my information is being collected, but it’s not a big deal, right? After all, it doesn’t really influence my life.  Norton describing how he would play with people like toys, shaping their tastes and buying patterns, would seem like an exaggeration. Maybe some people are influenced by internet advertising, but not me.

Of course, this line of thought is the textbook example of third-person bias.  Psychologically, we are predisposed to think that things like advertising don’t really affect us – it only affects other people. But that’s wrong.  And if 30 different companies are monitoring my clicks when I visit The Atlantic, surely my data must be more valuable that I would have thought.

But as our readings make clear, big data is about more than just stalking us as we waste time on the internet.  It can be used for everything from optimizing HIV/AIDS research to misidentifying photos of concentration camps to shoring up the glass ceiling by targeting ads for high-paying jobs at men rather than women.

So many elements of life are becoming data-driven and algorithmic, often without us even realizing it. And in a way, the problem with this is the same as the problem with the algorithms that track my clicks to determine my ad preferences. It is so unclear what is happening, what data goes where and how that data triggers events in our lives, it is impossible to know what needs to be changed.  And as these algorithms get more and more complex, transparency seems less and less likely.

It's the end of the world as we know it - Numbers, big data, and the brave new world

How numbers on Facebook change behavior 

So it seems this article falls into the overall disturbing theme of analytics and data being used to effect, change or even shape people’s lives and even viewpoints. I found Ben Grosser’s ‘Facebook Demetricator’ very interesting, as well as people’s responses to it. The fact that many people expressed that once the numbers were gone, they realized they had been using them to decide what they liked or whether or not the like something was particularly intriguing. It reminded me of mob mentality, but in an extremely prolonged state. My brother used to always say, “A person is smart, but people are stupid,” meaning that while individuals can think through problems and be reasoned with, a mob of people is irrational and cannot easily change its course or its mind. Perhaps this can explain people’s reaction to the numbers on Facebook – they are so used to following the group’s decisions that they have temporarily lost sight of their own preferences.  This is a scary thought when you relate this piece to Quinn Norton’s “The Hypocrisy of the Internet Journalist” and her warnings about opinion shaping and how data analytics potentially allow those collecting the data to remake people’s worldviews.

The promise of big data

I did find the majority of the research and work being done described in this article to be very promising. The work that IBM’s T.J. Watson Research Lab is doing with the Ureport System I think is particularly inspiring, because it simply isn’t possible for a person to comb through that much data and the impact on people’s lives is very real and very important. However, I can’t help but immediately worry about the computer program making mistakes. My first impulse is to want an actual human being reviewing the data or reviewing the computer’s work, even though I know this is improbable. There are countless examples of analytic software making mistakes – several are mentioned in later articles. But what is the solution? Just not using this software is not an option and it’s simply too much data for human beings to handle without computer aid. I think this is a very important question to focus on and research. 

There were two points in this article in particular that stuck out to me as problematic. First, Google research scientist Diane Lambert’s comment that, “If you’ve ever put a query into Google, then you’ve been in an experiment.” I guess consenting to participant in experiments in one of those items in the impossibly long terms of service that no one reads. 

Second was Dean Cherry A. Murray’s comment about the new breed of data scientists, that graduates would “have the opportunity to inform decision-making in science, business, or government settings.” Taking into account later articles’ point about inherent human bias in these analytic software programs, I found it disturbing that she foresees creators of such programs informing government decision-making.

The pitfalls of big data

The most infuriating part of this piece, besides the point of the article itself, is that the Princeton Review does not seem to have any intention of addressing the problem brought to light by the study. I tried to find an update on this story but was unsuccessful. Has the Princeton Review made any effort to change its discriminatory policies? It doesn’t matter if it was unintentional, it should still be fixed. 

Kate Cox, the author of the article, makes the point that the Princeton Review’s algorithms are designed by humans that bring their own assumptions and biases into the code. So perhaps it’s not all that surprising that the organization that told Asian American college applicants to play down their Asianness by not attaching a photograph, not answering the optional question about ethnic background, and advising against application essays that discuss how their culture affects their lives to increase their chances of admission in their 2004 Cracking College Admissions (http://www.bloomberg.com/news/articles/2014-11-21/princeton-review-tells-asians-to-act-less-asian-and-black-students-to-attach-photos) has been found to discriminate against Asian Americans. 

The source for this article, ProPublica, even belies an inherent bias with the title it chooses for its version of this story: “The Tiger Mom Tax: Asians Are Nearly Twice as Likely to Get a Higher Price from Princeton Review.” ‘Tiger Mom’ being a negative term often applied to Asian mothers who, according to stereotypes, push their children too hard and are overbearing and controlling.

https://www.propublica.org/article/asians-nearly-twice-as-likely-to-get-higher-price-from-princeton-review

 

Interestingly, this article lists several other recent instances of discriminatory or suspect behavior by companies’ programmed analytical software:

• In 2012, Staples was varying prices by zip code with the inadvertent effect that people in lower income zip codes had the higher prices
• In 2014, Northeastern University found that top websites like Home Depot, Orbitz, and Travelocity were steering their users toward more expensive products
• In 2015, another study found that users identified by Google as female received fewer ads for a high-paying job (also mentioned in article below)

How social bias creeps into web tech

The biggest thing that stood out to me in this article was the quote from Vivienne Ming: “Computers aren’t magically less biased than people.” The problem of bias and discrimination in data analytic software is not a simple one and not easily solved. But I think a great first step would be to move away from the idea that computers and software are neutral and impartial, therefore unbiased, solely because they are not human beings. They are still created and programmed by human beings who cannot escape bias and program their personal biases into them. The sooner we all realize that, the sooner we can move closer to reducing and potentially one day solving this problem.

Brave New World

The Facebook article reminds me of an incident that happened with my cousin’s daughter. On the first day of school my cousin posted a picture of her daughter, and then that photo got a lot of likes. Apparently she told her daughter who was surprised at how many likes it got, so then her daughter wanted her to post a picture of her going to school the next several days. At the time I knew I was probably not going to like her daughter’s picture everyday, just because I knew I wouldn’t be logging into FB that often, and I felt a tad guilty, like would I be contributing to my cousin’s daughter not feeling as good about herself if I didn’t log on and like her daughter’s picture everyday?

The analytics article suggest that it can start to seem that whatever article gets the most traffic for the longest amount of time is a success. If that ever does become the standard, that means that popularity determines what is published. That has already happened in some ways such as when the Associated Press cut other types of reporters for more entertainment reporters. Or, closer to home, the Dallas Morning News in February put a story about a mother in a car accident who confronted the man who stole her van with her child inside on the front page because it was popular; this was a change in thought and occurred because of new leadership. Is it good or bad? I don’t know. I guess it depends on what other story got bumped, and if readers who were wanting to read about the story would have bought the paper and been disappointed because the story was hard to find.

If what the journalist who used to be a marketer says is true, then two things do suggest a brave new world: that data warehousers and advertisers are the news industry’s only path to survival in the 21^st century; and that surveillance over time CREATES a person’s tastes – wow!

While some would say that digital culture should eliminate social biases, the articles about test preps companies charging more by race and how bias creeps into computer programs such as Flickr suggest otherwise, and is not surprising to me. Humans program these systems so human shortcomings will also be part of digital culture.