Note: I'm writing this post on my personal blog as I'm still learning about GDPR. This is me thinking out loud, rather than making official Moodle pronouncements. 'Enjoyment' and 'compliance-focused courses' are rarely uttered in the same breath. I have, however, enjoyed my second week of learning from Futurelearn's
Tag: Data
Data about exercise routes shared online by soldiers can be used to pinpoint overseas facilities
Hey! Here's an idea! Let's use FitBits in education! What could ever go wrong!https://t.co/wReKVD2E9t
To all the "I have nothing to hide" crowd: You never know when the right combination of details will change that, dramatically.
— Bill Fitzgerald (@funnymonkey) January 28, 2018
Also, the debacle of Fitbit data exposing the location of military bases highlights one of the many issues with data flowing to third parties.
Across large datasets, patterns emerge that aren't obvious from a single record, or smaller datasets.
— Bill Fitzgerald (@funnymonkey) January 28, 2018
This problem is made worse when data from multiple sources are combined: more patterns, more predictive possibilities, more potential misuse, and/or unintended consequences.
Folks who have "nothing to hide" learn the opposite, pretty fast.
— Bill Fitzgerald (@funnymonkey) January 28, 2018
Arvind Narayanan also wrote a series of tweets:
Now that the dust has settled a bit on the Strava heatmap privacy story, what lessons can we learn? I was interviewed about this on CBC radio. Here are the highlights. https://t.co/ljekLDd8X4
— Arvind Narayanan (@random_walker) February 3, 2018
Jordan Erica Webber explores whether our privacy has been compromised by the tech giants whose business models depend on harvesting and monetising our data. We speak to cyborg rights activist Aral Balkan; the executive director of UK charity Privacy International Gus Hosein; and to Kevin Kelly, founding executive editor of Wired magazine and author of The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future.
Martin McKay from Texthelp discusses the use of data from 12 million users to develop a set of nationalised writing norms.
Google has gathered so much data, in so many areas, that itās now crunching it together and creating features that Apple canāt makeāsurrounding Google Maps with a moat of time
Google’s is in fact making data out of data:
Googleās buildings are byproducts of its Satellite/Aerial imagery. And some of Googleās places are byproducts of its Street View imagery.
For a different take on Google Earth’s 3D imagery, watch this video from the [Nat and Friends]:
https://youtu.be/suo_aUTUpps
Reply to Chris Betcher and Location Tracking
The tracker allows marketers to use machine learning to discover personas, uses cross-device ID, and even uses behavioral analysis to guess when a user is sleeping, and a probabilistic matching algorithm to match identities across devices.
What is disconcerting is that it may not be the application designed for location which provides a company with location information.
š Questions for Data
Is this meaningful data? Are ātest scoresā or āgradesā 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 weāve 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 ālearningā. 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 ā 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 āa good studentā or āa good teacherā or āa good educationā? 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 ā maybe because they are too far away from actual children ā investors and their policy-makers donāt 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}(https://emilytalmage.com/2017/07/31/how-data-is-destroying-our-schools/)
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.
āThe real question for the companies is, what is their motivation for having multiple trackers?ā asked OāBrien.source
Ben Williamson collects together a number of critical questions when addressing big data in education:
How is ābig dataā 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 ātrainedā and ātaughtā?
Who āownsā educational big data?
Who can āaffordā 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 ādata literacyā?
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 Stommelās 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 ā 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 elseās) 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 ā one where there was large-scale deployment of surveillant technologies with persistent ādigital epidermalizationā writing identity on to every body within the scope of its gaze? Iām 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 āfrictionā and identify the āothersā who do or donāt belong in a space at a glance. What if Permit Patty or Pool Patrol Paul had immediate, real-time access to technologies that ālegitimizedā 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? Iām asking all of you out thereāis the algorithm designed to continue feeding us the same narrative that we are already upvoting? Or can the upvotes trend away?