# Reading Responses Checklist
- Checklist for a [good reading response](https://reagle.org/joseph/zwiki/Teaching/Best_Practices/Learning/Writing_Responses.html) of 250-350 words
- [ ] Begin with a punchy start.
- [ ] Mention specific ideas, details, and examples from the text and earlier classes.
- [ ] Offer something novel that you can offer towards class participation.
- [ ] Check for writing for clarity, concision, cohesion, and coherence.
- [ ] Send to professor with “hackmd” in the subject, with URL of this page and markdown of today’s response.

## Reading responses 5 out of 5

### Mar 19 Tue - Ads & Social Graph Background

“The trackability of online advertising is what makes it so superior to conventional advertising” (Stokes, 2013, p.311). Throughout the chapter, Stokes delves into the methods and goals of online advertising, and credits its effectiveness to advanced tracking capabilities. The world of online advertising is dictated by cookies that provide companies with crucial user data. This data enables advertisers to target and optimize their campaigns and get the most out of their budgets.

According to Vox (2020), cookies were created in 1994 with the intention to bring memory to the web. First party cookies allow websites to save and retrieve user information, but only when the user is on that specific page. As the digital landscape has evolved, websites have become more populated with elements that are hosted by third-party cookies. This transformation has enabled information sharing across different websites and companies. Earlier this semester, we discussed third-party cookies and learned that they don’t come from sites that we intentionally visit, also shown in Vox’s YouTube video.

It’s fascinating to see how the online world has evolved. The original intention of inventing cookies to help websites store simple information, like what a user might add to their shopping cart, has completely transformed. Today, cookies enable companies to harbor extreme amounts of user data, leading to eerily accurate and targeted advertisements. With the main objectives of online advertising being to increase sales and improve brand awareness, this doesn’t seem all that bad. But, it makes me wonder what’s next for the world of online advertising? Could it possibly get more invasive, or will people become more intentional with the data they are willing to give up?

### Apr 2 Tue - Artificial Intelligence

It’s hard to imagine a world where Generative AI is all-powerful, and it’s almost impossible to predict exactly how much it will impact our lives. A Vox article by Heilweil (2023) states that AI already has the capability to automate general tasks and make some kinds of jobs obsolete, but the question is, will humans allow AI to get to a point where it can actually replace humans?

AI is developed with machine learning, and it's exposed to data which it trains on and learns to mimic. The statistical technique of predictive modeling isn’t new, but what is new is the scale on which we use it. Now, AI is trained with the intention to reproduce human behavior based on existing content. This is why AI is currently free, because the more training data we give to AI, the better the product becomes. One question this sparked for me was, outside of giving AI our own data, what exactly is being used to train AI? And who decides what is used to train it? Additionally, AI needs to be frequently updated to remain relevant and accurate, so I wonder how this will play out in the future.

One thing we can predict with AI is that it can potentially replace Google’s search engine, which powers one of the biggest digital ad businesses (Heilweil 2023). This reminded me of our discussion on ad blockers and the ongoing debate over their implications. I wonder how this debate might evolve if the platforms we use shift, but I believe it’s likely that the ad business will adapt to ChatGPT once its technology advances enough to start charging users for use of the platform.

Heilweil (2023) suggests that our biggest concern about AI should be that it's going to catch up and get ahead of humans before we have the chance to fully understand and learn how to deal with it. I agree with this, but it's a terrifying thought.

### Apr 5 Fri - Algorithmic Bias

When I think of predictive modeling, baseball is not among the list of things that come to mind. However, according to Cathy O’Neil in “Weapons of Math Destruction”, baseball is an ideal home for predictive mathematical modeling.

O’Neil (2016) describes models as nothing more than an abstract representation of a process. She explains that blind spots in these models are a product of the creator’s judgments and priorities, also known as biases. Understanding who designs these models and their intentions behind creating them is crucial for understanding potential biases within a model. This brought to mind our recent discussion about generative AI, where I questioned who trains AI models and how they get that authority. O’Neil (2016) explains that algorithmic models shut down millions of people and are generally unfair, begging the question: to what extent will we continue to sacrifice justice for efficiency? And is it worth it?

In an exploration of bias through the lens of Google, Rutherford & White (2016) shed light on Google’s racist tendencies. For example, they explain that if you research “hands” on Google, the only results are white hands. Luckily, this article is from 2016 and when I re-tested this, the results came up more diverse. However, bias still exists, and Rutherford & White (2016) further explain how media feeds into these results, circling back to our earlier discussion of AI bias and predictive modeling. Biases introduce complexities that continue to persist, and I wonder how we might limit this in the future. For now, all we can do is understand the motivations and perspectives of model creators to maintain awareness of the information we consume.

### Apr 12 Fri - Digital Language and Generations

In “Internet People”, Gretchen McCulloch (2019) explores internet dialects. To better understand and explain online communication patterns, McCulloch takes a look at past waves of Internet People. She begins with the initial wave of internet users, that she refers to as “Old Internet People”, and describes the tech adeptness required for online users back then. She claims that this group has the highest level of average technological skill.

McCulloch (2019) then transitions to the second wave of online users. It was during this wave that the internet started to become mainstream and regular people started accessing the internet. She explains a shift where technical skills are no longer the differentiator among online users, but instead, social choices. It was during this time period (1995) that internet dating was widely reported for the first time. It was surprising to learn how early internet dating was adopted, and that it was successful before the creation of social media platforms and dating apps. This raises the question, which version of internet dating is safer and/or more successful? While modern platforms offer more information on users, the same information has become much easier to fabricate, posing new and worse challenges.

McCulloch (2019) concludes with the third wave of Internet People, which include those who don’t have the option to avoid the internet. She splits this wave into two parts: pre internet people and post internet people, and suggests that there is little difference between a late-1990s teenager using AOL and a mid 2010-s teen, sending mundane but vital updates via Snapchat. It’s interesting to consider the speed at which the internet evolved and how much larger this gap becomes between following generations, prompting questions about an increasing amount of disparities between generations.

### Apr 16 Tue - Pushback

Smartphones have allowed us to remain constantly connected and in communication. The concept of pushing back against this constant connectivity has become a more prevalent trend, showcased by the emergence of groups like the Luddite club. According to Morrison & Gomez (2014), the Luddite club is a group of high schoolers who promote a lifestyle of “self-liberation from social media and technology”. Members of this group distance themselves from smartphones, a nice idea that proves to be challenging.

Logan Lane, a member of the Luddite club, exemplifies the challenges associated with balancing modern media and technology and the desire to be disconnected, or “pushback” against it. As a high school student, Logan’s parents are naturally concerned about her daily whereabouts. Though they initially appreciated Logan ditching her phone and regularly returning home for dinner to discuss what she did throughout her day, they quickly became distressed by their inability to check in on their daughter, especially when she went abroad.

Morrison & Gomez (2014) write about Logan’s parents being part of the “helicopter parent generation”, who are used to the conveniences offered by smartphones. The information and capabilities that come with the use of smartphones make it almost impossible to go backwards, and it makes sense that we grow distressed when that is taken away from us. Though a flip phone is a good temporary solution to this issue, it doesn’t quite solve every problem.

Overall, these two readings highlight a key trend where individuals who once embraced and encouraged connectivity are now resisting the idea of being permanently contactable. This prompts questions about the right way to balance these two things and whether there is a right way. In a digital age, is it even feasible to consider moving backwards?