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Please follow the guideline very carefully and write correctly.If you have any question please ask me.Please write as better as possible.Thank you! student_instructions___week_8_85c_spring_2020_202005221803231.docx gillespie_2014_the_relevance_of_algorithms.pdf gregg_and_nafus_data_2017.pdf Unformatted Attachment Preview STUDENT INSTRUCTIONS for Week 8 Greetings, Flm&Mda 85C students! Here are your instructions for week 8: Step 1. Watch the “Week 8 Introduction” video https://drive.google.com/file/d/1bfWZ6HNQDwpcTkY9kDkoAzO8KYCWXO5m/view?usp=sharing Step 2. Read “Data” (2017) by Melissa Gregg and Dawn Nafus. While you read, pay attention to: - What do we mean when we talk about “data”? What are some of the qualities of data? What is the relationship between data and media? Step 3. Watch the first guest lecture video “Talking Data Feminism with Catherine D’Ignazio and Lauren F . Klein” (2020). Note: You only need to watch until minute 41:00; the rest is Q&A. While you watch, pay attention to: - What is “data feminism”? What does it mean to do data science in a feminist way? How can data be used for both positive and problematic purposes? Step 4. Read “The Relevance of Algorithms” (2014) by Tarleton Gillespie. Note: Focus on pages 167 182; you can skim pages 183 - 192. While you read, pay attention to: - What are algorithms? What elements of day-to-day life with technology do they affect? Why is it important to look critically at algorithms, rather than assuming they are objective? Step 5. Watch the second guest lecture video “Algorithms of Oppression” (2018). Note: There’s a lot in this video; if you don’t understand or agree with it all, that’s ok. While you watch, pay attention to: - What do most people think about search engines? For example, do they trust them? How can algorithmic systems promote bias, such as through search engines and image search? Step 6. Watch the “Week 8 Lecture” video https://drive.google.com/file/d/1LHzxC6N-7NvTJiIA7zFpZ-7frhXRiwmO/view?usp=sharing Step 7. Write and submit your weekly reflection (due Saturday, May 23 by 11:59 pm). Note: For this week, your reflection only needs to be 400 - 500 words. This week’s reflection is in two parts: - For the first part of your reflection, look at your Google ad personalization settings list (to view, visit https://adssettings.google.com/ while logged into your Google account). Write a paragraph about what you find. Who does Google think you are and what you’re interested in? Are they right? Wrong? Does Google think some things are more/less important in your life than you do? How does looking at this list make you think differently about the data you put on the internet (whether on purpose or inadvertently) and how Google makes sense of it? Discuss by using specific examples from the personalization settings list. - For the second part of your reflection, look at the tailored ads on your social media accounts. This could be Twitter, Instagram, Facebook, etc. (If you don’t use social media, look at tailored recommendations on Amazon, Netflix, etc.) Write a paragraph about the patterns you see. For example, do the ads “get you” or do they miss the mark? Do they seem to know things about you you didn’t put online? How does looking at these ads make you think differently about how you present yourself online or how your data is interpreted by algorithms? Step 8. Read and respond to one of your peers’ reflections (due Tuesday, May 26 by 2:00 pm). Follow the standard instructions for peer comments. Each comment should be roughly 100 words in length. ← Note: this week’s comments are due Tuesday, not Monday, because of Memorial Day. Please follow the guideline very carefully and write correctly. If you have any question please ask me. Please write as better as possible. Thank you! Peer reflection: You only need to respond one of them. Donghee Lim According to Google, I am a 18-24 year old male who enjoys video games, films, technology, world news, and cats. This much is true and I know exactly why each of these are on Google’s personalized ad list. The more peculiar interests Google thinks I have, however, is information on business, sports, and celebrity news. Anybody who knows me will testify that I find the business mind wrenchingly boring and I have no interest in sports. As for celebrity news, the only celebrity that I can reliably recognize is Nick Cage. For the most part, Google has a fair idea of who I am and what things I place importance in with the exceptions of a few outliers. For instance, the top of my list is films and video games which are most definitely where I spend most of my time. As for the outliers, they range from random pieces of information that I put on the internet to completely random things. As an example, one of the things Google thinks I place high importance in is Australian football, which I don’t even know why there’s a distinction from this and American football (or is it talking about soccer?). I never put any information online concerning Australia other than following an Australian comedian, and definitely never associated myself with anything concerning football. I literally have zero clue as to why Google thinks this is my interest let alone one of my highest priority ones. The Google ad personalization does make me a bit more aware of what information that is put online about me. For instance, my mom uses my Amazon prime to buy her makeup and other merchandise which explains why Google may think that beauty and apparel are one of my interests. Honestly, though I don’t really mind that Google has this false information about me. Since Google uses AI to track information about me, it doesn’t really have a way of distinguishing between what interests others are using my accounts or just peculiar interests that I have. Due to this, their image of me is a bit inaccurate and, considering our week 6 topic, this actually makes me rather happy. As for social media, the only one that I use is Instagram and the personalized ads it provides me are rather interesting. I only get two types of personalized ads on Instagram: ads concerning video games and anime. While these two are of interest to me, they’re not something that I would purchase merchandise for and a platform that moditors me closely like Google would know that since I’ve never purchased any merchandise with these two topics in mind. From what I can see, it seems that I don’t use Instagram enough for it to get an accurate image of me so it pulls interests from my friends and the people that I follow in order to decide what type of ads I get. Since all of my friends are either huge video game fanatics or anime fans, it makes sense that these are the ads I’m provided with. One thing that does bring my attention is how Instagram seems to have caught on to my sense of humor. Recently, I received an ad about a hoodie with “idiot” written in Japanese with a rabbit with hand sanitizer slapping another rabbit who’s hoarding toilet paper. I found this extremely funny and I purchased the hoodie (against my better judgement). The only way that I can imagine how Instagram managed to figure out my sense of humor is through my posts on the platform. I post very erratically and it’s always some kind of joke or funny scenario I find myself in. I’m guessing they have some form of algorithm that reads the images/videos I post and finds similarities in them to figure out my interests and my sense of what I find funny is something it’s managed to catch. Jingwen Zhou Based on the personal information I have added to my Google Account, as well as data from the advertisers collaborating with Google, I believe that my Google ad personalization settings list is just about accurate. Google plays a critical role in estimating my interests by offering what I would like as part of my age bracket. One of the critical parts of the list is the choice of films and genres, which I would like. The list incorporates other aspects associated with the books I would like to read, which I believe emanates from the searches of the books for diverse studies. From the ad list, I can say that Google is right and wrong. In the first aspect, Google is right about what might interest me in terms of the choice of movies. On the other hand, Google is wrong in omitting other things, which I believe are highly important to me such as the choice of food I would like to consume. The choices of joints I would like to visit, as well as the nations I would like to go to as part of the dire to travel across the world are also missing from the list. Critically, Google thinks that some things are more/less important in my life than I do, based on the presentation of the personalized list. Looking at this list makes me think that the data I offer on the internet, purposely or inadvertently, offers leeway for Google to track my interests in the estimation of what I might be into in terms of the things I procured on the online platforms to make sense of the situation. Critically, the internet is one of the major ways, which I use today for communication. In this aspect, I sought to explore my Facebook page in the determination of the tailored ads on my social media account. What did I learn about this? Most of the ads on my social media account related to my searches on that platform. For example, recently, I was looking at the best laptops available in the market with the right or affordable prices. My Facebook account did present ads on the electronics concerning laptops, desktops, and accessories. I have to admit that the ads on my social media page got me. Marketers seem to know the things about my interests or searchers for by assessing the prints I leave on the internet or social media platforms. I believe that they also look at the pages I visit or the people I interact with in ensuring that they estimate the interests in terms of the ads available or suggested pages on the online platforms. Looking at these ads makes me think differently about what social media accounts offer me. This is through using the information I present on the online platform to filter what I might need or desire in terms of ads. I feel bad about the use of the algorithms and interpretation of such information to determine what I want. I would like to have a vast opportunity to explore different products or services in the market. Marketers should not use my hobbies, interaction with friends, and pages that I visit consistently to estimate my interest while filtering out what they believe are not important to me on the online platforms. PROPERTY OF MIT PRESS: FOR PROOFREADING AND INDEXING PURPOSES ONLY 9 The Relevance of Algorithms Tarleton Gillespie Algorithms play an increasingly important role in selecting what information is considered most relevant to us, a crucial feature of our participation in public life. Search engines help us navigate massive databases of information, or the entire web. Recommendation algorithms map our preferences against others, suggesting new or forgotten bits of culture for us to encounter. Algorithms manage our interactions on social networking sites, highlighting the news of one friend while excluding another’s. Algorithms designed to calculate what is “hot” or “trending” or “most discussed” skim the cream from the seemingly boundless chatter that’s on offer. Together, these algorithms not only help us find information, they also provide a means to know what there is to know and how to know it, to participate in social and political discourse, and to familiarize ourselves with the publics in which we participate. They are now a key logic governing the flows of information on which we depend, with the “power to enable and assign meaningfulness, managing how information is perceived by users, the ‘distribution of the sensible.’” (Langlois 2013) Algorithms need not be software: in the broadest sense, they are encoded procedures for transforming input data into a desired output, based on specified calculations. The procedures name both a problem and the steps by which it should be solved. Instructions for navigation may be considered an algorithm, or the mathematical formulas required to predict the movement of a celestial body across the sky. “Algorithms do things, and their syntax embodies a command structure to enable this to happen” (Goffey 2008, 17). We might think of computers, then, fundamentally as algorithm machines—designed to store and read data, apply mathematical procedures to it in a controlled fashion, and offer new information as the output. But these are procedures that could conceivably be done by hand—and in fact were (Light 1999). 9042_009.indd 167 8/2/13 10:52 AM PROPERTY OF MIT PRESS: FOR PROOFREADING AND INDEXING PURPOSES ONLY 168 Tarleton Gillespie But as we have embraced computational tools as our primary media of expression, and have made not just mathematics but all information digital, we are subjecting human discourse and knowledge to these procedural logics that undergird all computation. And there are specific implications when we use algorithms to select what is most relevant from a corpus of data composed of traces of our activities, preferences, and expressions. These algorithms, which Ill call public relevance algorithms, are—by the very same mathematical procedures—producing and certifying knowledge. The algorithmic assessment of information, then, represents a particular knowledge logic, one built on specific presumptions about what knowledge is and how one should identify its most relevant components. That we are now turning to algorithms to identify what we need to know is as momentous as having relied on credentialed experts, the scientific method, common sense, or the word of God. What we need is an interrogation of algorithms as a key feature of our information ecosystem (Anderson 2011), and of the cultural forms emerging in their shadows (Striphas 2010), with a close attention to where and in what ways the introduction of algorithms into human knowledge practices may have political ramifications. This essay is a conceptual map to do just that. I will highlight six dimensions of public relevance algorithms that have political valence: 1. Patterns of inclusion: the choices behind what makes it into an index in the first place, what is excluded, and how data is made algorithm ready. 2. Cycles of anticipation: the implications of algorithm providers’ attempts to thoroughly know and predict their users, and how the conclusions they draw can matter. 3. The evaluation of relevance: the criteria by which algorithms determine what is relevant, how those criteria are obscured from us, and how they enact political choices about appropriate and legitimate knowledge. 4. The promise of algorithmic objectivity: the way the technical character of the algorithm is positioned as an assurance of impartiality, and how that claim is maintained in the face of controversy. 5. Entanglement with practice: how users reshape their practices to suit the algorithms they depend on, and how they can turn algorithms into terrains for political contest, sometimes even to interrogate the politics of the algorithm itself. 6. The production of calculated publics: how the algorithmic presentation of publics back to themselves shape a public’s sense of itself, and who is best positioned to benefit from that knowledge. 9042_009.indd 168 8/2/13 10:52 AM PROPERTY OF MIT PRESS: FOR PROOFREADING AND INDEXING PURPOSES ONLY The Relevance of Algorithms 169 Considering how fast these technologies and the uses to which they are put are changing, this list must be taken as provisional, not exhaustive. But as I see it, these are the most important lines of inquiry into understanding algorithms as emerging tools of public knowledge and discourse. It would also be seductively easy to get this wrong. In attempting to say something of substance about the way algorithms are shifting our public discourse, we must firmly resist putting the technology in the explanatory driver’s seat. While recent sociological study of the Internet has labored to undo the simplistic technological determinism that plagued earlier work, that determinism remains an alluring analytical stance. A sociological analysis must not conceive of algorithms as abstract, technical achievements, but must unpack the warm human and institutional choices that lie behind these cold mechanisms. I suspect that a more fruitful approach will turn as much to the sociology of knowledge as to the sociology of technology—to see how these tools are called into being by, enlisted as part of, and negotiated around collective efforts to know and be known. This might help reveal that the seemingly solid algorithm is in fact a fragile accomplishment. It also should remind us that algorithms are now a communication technology; like broadcasting and publishing technologies, they are now “the scientific instruments of a society at large,” (Gitelman 2006, 5) and are caught up in and are influencing the ways in which we ratify knowledge for civic life, but in ways that are more “protocological” (Galloway 2004), in other words, organized computationally, than any medium before. Patterns of Inclusion Algorithms are inert, meaningless machines until paired with databases on which to function. A sociological inquiry into an algorithm must always grapple with the databases to which it is wedded; failing to do so would be akin to studying what was said at a public protest, while failing to notice that some speakers had been stopped at the park gates. For users, algorithms and databases are conceptually conjoined: users typically treat them as a single, working apparatus. And in the eyes of the market, the creators of the database and the providers of the algorithm are often one and the same, or are working in economic and often ideological concert. “Together, data structures and algorithms are two halves of the ontology of the world according to a computer” (Manovich 1999, 84). Nevertheless, we can treat the two as analytically distinct: before results can be algorithmically provided, information must be collected, readied for the algorithm, and sometimes excluded or demoted. 9042_009.indd 169 8/2/13 10:52 AM PROPERTY OF MIT PRESS: FOR PROOFREADING AND INDEXING PURPOSES ONLY 170 Tarleton Gillespie Collection We live in a historical moment in which, more than ever before, nearly all public activity includes keeping copious records, cataloging activity, and archiving documents—and we do more and more of it on a communication network designed such that every login, every page view, and every click leaves a digital trace. Turning such traces into databases involves a complex array of information practices (Stalder and Mayer 2009): Google, for example, crawls the web indexing websites and their metadata. It digitizes real-world information, from library collections to satellite images to comprehensive photo records of city streets. It invites users to provide personal and social details as part of their Google+ profile. It keeps exhaustive logs of every search query entered and every result clicked. It adds local information based on each user’s computer’s data. It stores the traces of web surfing practices gathered through their massive advertising networks. Understanding what is included in such databases requires an attention to the collection policies of information services, but should also extend beyond to the actual practices involved. This is not just to spot cases of malfeasance, though there are some, but to understand how an information provider thinks about the data collection it undertakes. The political resistance to Google’s StreetView project in Germany and India reminds us that the answer to the question, “What does this street corner look like?” has different implications for those who want to go there, those who live there, and those who believe that the answer should not be available in such a public way. But it also reveals what Google thinks of as “public,” an interpretation that ... Purchase answer to see full attachment