📓 Questions for Data

Audrey Watters writes down a series of questions to consider when thinking about data:

Is this meaningful data? Are “test scores” or “grades” meaningful units of measurement, for example? What can we truly know based on this data? Are our measurements accurate? Is our analysis, based on the data that we’ve collected, accurate? What sorts of assumptions are we making when we collect and analyze this data? Assumptions about bodies, for example. Assumptions about what to count. Assumptions and value judgments about “learning”. How much is science, and how much is marketing? Whose data is this? Who owns it? Who controls it? Who gets to see it? Is this data shared or sold? Is there informed consent? Are people being compelled to surrender their data? Are people being profiled based on this data? Are decisions being made about them based on this data? Are those decisions transparent? Are they done via algorithms – predictive modeling, for example, that tries to determine some future behavior based on past signals? Who designs the algorithms? What sorts of biases do these algorithms encode? How does the collection and analysis of data shape behavior? Does it incentivize certain activities and discourage others? Who decides what behaviors constitute “a good student” or “a good teacher” or “a good education”? source

Continuing this conversation, Jim Groom suggests that the key question is:

How do we get anyone to not only acknowledge this process of extraction and monetization (because I think folks have), but to actually feel empowered enough to even care source

Speaking about assemblages, Ian Guest posits that:

When data is viewed in different ways, with different machines, different knowledge may be produced. source

Benjamin Doxtdater makes the link between power and data:

The operation of power continues to evolve when Fitbits and Facebook track our data points, much like a schoolmaster tracks our attendance and grades.source

Kin Lane provides the cautionary tale of privacy and security violations via APIs, in which he suggests:

Make sure we are asking the hard questions about the security and privacy of data and content we are running through machine learning APIs. Make sure we are thinking deeply about what data and content sets we are running through the machine learning APIs, and reducing any unnecessary exposure of personal data, content, and media.source

Emily Talmage questions the intent behind platform economy and the desire for correlations that detach values from the human face:

For whatever reason – maybe because they are too far away from actual children – investors and their policy-makers don’t seem to see the wickedness of reducing a human child in all his wonder and complexity to a matrix of skills, each rated 1, 2, 3 or 4. source

Yael Grauer documents how researches at Yale Privacy Lab and French nonprofit Exodus Privacy have uncovered the proliferation of tracking software on smartphones, finding that weather, flashlight, ride-sharing, and dating apps, among others, are infested with dozens of different types of trackers collecting vast amounts of information to better target advertising.

“The real question for the companies is, what is their motivation for having multiple trackers?” asked O’Brien.source

Ben Williamson collects together a number of critical questions when addressing big data in education:

How is ‘big data’ being conceptualized in relation to education?
What theories of learning underpin big data-driven educational technologies?
How are machine learning systems used in education being ‘trained’ and ‘taught’?
Who ‘owns’ educational big data?
Who can ‘afford’ educational big data?
Can educational big data provide a real-time alternative to temporally discrete assessment techniques and bureaucratic policymaking?
Is there algorithmic accountability to educational analytics?
Is student data replacing student voice?
Do teachers need ‘data literacy’?
What ethical frameworks are required for educational big data analysis and data science studies?source

Discussing personal data, Kim Jaxon asked her students to consider the platforms they frequent:

I invited our class to look closely at Google, Facebook, Snapchat, Blackboard Learn, TurnItIn, and many other platforms they frequent or are asked to use, and to think critically about the collection and control of their data. Borrowing from Morris and Stommel’s work, we are asking: Who collects data? Who owns it? What do they do with it? Who profits or benefits? What is left out of the results: what is hidden?

Nicholas Carr wonders if we are data mines or data factories:

If I am a data mine, then I am essentially a chunk of real estate, and control over my data becomes a matter of ownership. Who owns me (as a site of valuable data), and what happens to the economic value of the data extracted from me? Should I be my own owner — the sole proprietor of my data mine and its wealth? Should I be nationalized, my little mine becoming part of some sort of public collective? Or should ownership rights be transferred to a set of corporations that can efficiently aggregate the raw material from my mine (and everyone else’s) and transform it into products and services that are useful to me? The questions raised here are questions of politics and economics.source

Chris Gilliard poses some questions to consider associated with the racial bias built into the surveillance state:

What would it look like to be constantly coded as different in a hyper-surveilled society — one where there was large-scale deployment of surveillant technologies with persistent “digital epidermalization” writing identity on to every body within the scope of its gaze? I’m thinking of a not too distant future where not only businesses and law enforcement constantly deploy this technology, as with recent developments in China, but also where citizens going about their day use it as well, wearing some version of Google Glass or Snapchat Spectacles to avoid interpersonal “friction” and identify the “others” who do or don’t belong in a space at a glance. What if Permit Patty or Pool Patrol Paul had immediate, real-time access to technologies that “legitimized” black bodies in a particular space?

Reflecting on Microsoft’s attempts to game Hacker News, Kicks Conder poses a few questions about data and algorithms:

does gaming the algorithm undermine the algorithm? Or is it the point of the algorithm? I’m asking all of you out there—is the algorithm designed to continue feeding us the same narrative that we are already upvoting? Or can the upvotes trend away?

Dave Cormier reflects on data and artificial intelligence:

What does it mean to know? How does a learner know what they want to know? What’s AI really? Who decides what a learner needs to learn when AI is only perceiving the learner? What does it mean when AI perceives what it means to know in a field? What are the implications if AI perceives both the learner and what it means to know?source

Cory Doctorow unpacks the difference between property and data:

Property rights work best when there are clear titles and good information, but personal information has neither. Your trip with your co-workers to a union meeting might be very valuable and sensitive information to all of you, but which one of you “owns” the fact that the meeting took place? Do you all own it together and thus all need to agree before it can be sold? Or can any one of you sell this, and thus lower the “value” of this “asset” for all of you. How about how the front of your house looks? Some people were very angry that Google “took” the images of streets without paying for them or asking permission, but are we prepared to allow homeowners to decide who can photograph their street? What about if the person taking the photo is a community activist trying to agitate for higher-density zoning and hoping to defeat the rich NIMBYs who insist on single-family dwellings?

Ben Werdmüller discusses the nature of pull requests and the focus on software, rather than the user experience. For Werdmüller, there is not enough recognition of the impact of this data use:

A key question to building any software in the modern age is: “In the wrong hands, who could this harm?”

Decades ago, software seemed harmless. In 2019, when facial recognition is used to deport refugees and data provided by online services have been used to jail journalists, understanding who you’re building for, and who your software could harm, are vital. These are ideas that need to be incorporated not just into the strategies of our companies and the design processes of our product managers, but the daily development processes of our engineers. These are questions that need to be asked over and over again.

Richard Seymour takes a look at Twitter and social media and asks the questions:

What is the incentive to engage in writing like this for hours each day? In a form of mass casualisation, writers no longer expect to be paid or given employment contracts. What do the platforms offer us, in lieu of a wage? What gets us hooked? Approval, attention, retweets, shares and likes.

Autumm Caines and Erin Glass share a collaboratively created syllabus statement designed to solve the problem of data privacy. In it, they pose the following questions:

• What types of personal data do you think are collected through your use of digital tools for educational activities?
• What value does your personal data have for different contexts and entities? Consider how your data might be valued by your instructor, the institution, yourself, and companies.
• Who owns your personal data, who can sell it, and who can use it?
• Do you have concerns about how your personal data can be used? If so, what are they?
• Are there aspects of your identity or life that you feel would put you in a place of special vulnerability if certain data were known about you or used against you?

source

Responding to the news that Instructure is to be acquired, Bill Fitzgerald questions how this is not about data:

We also need to understand what we mean when we say data in this context: data are the learning experiences of students and educators; the artifacts that they have created through their effort that track and document a range of interactions and intellectual growth. “Data” in this context are personal, emotional, and intellectual effort — and for everyone who had to use an Instructure product, their personal, emotional, and intellectual effort have become an asset that is about to be acquired by a private equity firm.

But, to return to the claims that the data have no real value: these claims about the lack of value of the underlying data are often accompanied by long descriptions of how companies function, and even longer descriptions about where the “real” value resides (hint: in these versions, it’s never the data).

Here is precisely where these arguments fall apart: if the data aren’t worth anything, why do companies refuse to delete them?(source)

Speaking with the Times Higher Education, Jesse Stommel shares some questions he considers:

“Companies can start off small and they say ‘we will be good stewards of this data, we’re small, we talk to each other’ but then that company achieves more and more success and it doesn’t necessarily have the standards in place to maintain that,” said Dr Stommel, speaking generally. “Then what happens when they are bought out? What are the ethics of the company that has purchased them? What happens to the student data then?”(source)

Thinking about the residue of data associated with learning, Ian O’Byrne questions the responsibilities of organisations:

We need to ask questions as learning institutions create plans that hinge on new data collection or adopt new technologies. What data will be collected? How will this data be used? How will this be protected? How will it be shared?

danah boyd poses the following questions in regards to visualising data:

The work of visualization — like the work of animation — is fundamentally about communication. Even if your data are nice and neat, the choices you make in producing a visualization of that data shape how those data will be perceived. You have the power to shape perception, whether you want to or not. There is no neutral visualization, just as there is no neutral data. Thus, in building your tools, you must account for your interlocutors. What are you trying to convey to them? When do you need to stretch the ball so that the viewer sees the information as intended?

Jennifer Ding explores the legacy associated with a photograph of Lena Forsén taken in 1972 and how difficult it is to forget online:

As the internet reaches its “dirty 30s,” what happens when pieces of digital culture that have been saved, screenshotted, and reposted for years need to retire?

David Truss wonders about the way in which apps share our data. In doing so, he poses the following questions:

Part of me thinks this is great, after all I did enjoy the videos and found them interesting. That’s why I like TikTok, it feeds me more interesting content than any 30 minute show I could possibly find on TV. But part of me wonders, what other data is being shared? How much do my apps know about what I do on other apps? How targeted is the advertising I see? What about when I google medication, or symptoms? What about the health apps I use?

Is anything private anymore, or when I agree to use an app, am I agreeing to share my whole life? I might have enjoyed the videos, but I don’t think TikTok should know what books I’m listening to, unless I’ve explicitly permitted it to.

David Truss poses a number of questions associated with measuring success:

Is success measured by what you did, or how you feel about it, or how others perceive you? What does success look like and feel like to you? How do you measure success?

How long does it last?

Who else benefited?

Where does happiness or fulfillment fit in? What achievements really matter? And how do you really measure these things?

Maha Bali discusses the need for cultivating critical AI literacy. She reflects on ideas and exercises that she has used as a part of her course on digital literacies and intercultural learning. After unpacking each of the areas, with elaborations and examples, she ends with a series of questions to consider:

I think we should always question the use of AI in education for several reasons. Can we position AI as a tutor that supports learning, when we know AI hallucinates often? Even when we train AI as an expert system that has expert knowledge, are we offering this human-less education to those less privileged while keeping the human-centric education to more privileged populations? Why are we considering using technology in the first place – what problems does it solve? What are alternative non-tech solutions that are more social and human? What do we lose from the human socioemotional dimensions of teacher-student and student-student interactions when we replace these with AI? Students, teachers, and policymakers need to develop critical AI literacy in order to make reasonable judgments about these issues.