On the Possibility of Preserved Privacy: The Case of Google Photos
Today we live in a hyper-connected world. The daily routines of the post-modern individual is furnished and marked by the myriad of online services, apps, platforms that they now depend on for so many of their regular tasks and means of leisure. They send and receive e-mails, check their bank account, read the news, listen to music, buy tickets to a concert, search for a specific piece of information and do much more, all on the internet. The proliferation of smart phones has been a critical turning point in that continuous online presence became a possibility for the average citizen, and the door to countless new media channels and platforms was opened. This cornerstone development led to a new phenomenon dubbed as “Big Data”. Boyd and Crawford, in their influential article of 2012 titled “Critical Questions for Big Data”, draw attention to the fact that Big Data, despite the excitement and appetite around the data abundance that it represents; also brings along various key questions of ethical, socio-cultural, privacy-related relevance that warrant consideration. (Boyd and Crawford 2012)
What is Big Data?
Big Data generally denotes the accumulation of great quantities of data, many times unstructured and messy, mostly of and about the netizens (Boyd and Crawford 2012, 662) through the various new media channels and affordances offered thereto (Whitehead 1981). Although the definition of Big Data varies due to its use in a wide range of contexts, De Mauro et al. propose the following description: “Big Data represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value.” (De Mauro, Greco, and Grimaldi 2015)
“Big Data represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value.“
Big Data and Google
Google is certainly one of the biggest players in Big Data, owing to its vast scope of data collection capabilities (Gitelman 2013, 2). In this piece I will look at Google Photos as a contemporary media service and discuss, particularly, privacy considerations around it.
Google as a tech-company has become so ubiquitous1 with its endless string of deeply-interconnected services (most of which happen to be free of charge) in our daily online lives that it almost feels creepingly omni-present. Its services range from internet search to e-mail, online calendar to video hosting, navigation to cloud storage, music streaming to online payment. The distinguishing convenience and fundamental draw of Google comes from its foundationally interwoven network of services that are fully integrated to one another. This might also be the scariest thing about Google; as it lends the tech giant unimaginable levels of data collection opportunities. With its all-encompassing width of scope, it is today part of daily online traffic –by way of one service or another- for many professionals. It’s become increasingly difficult to imagine a life without the plethora of services Google provides that we use and have become dependent on for our everyday lives.
Google Photos is one among its many services, the basic functional affordance (Stanfill 2015, 1063) of which is the possibility to store one’s photos and videos on the cloud. It is available both on IOS and Android, furthermore it actually comes as one of the apps that are pre-installed on Android phones. (Android too belongs to Google, and approximately 70% of all the smartphones use Android as their operating system2, which gives an idea regarding the reach and penetration of Google Photos.)
The average user’s main motivation for the use of this service is twofold. One; to have the option to access said images from anywhere anytime through an internet connection; and secondly as a back-up measure against the loss of files together with the phone in case of theft, braking etc. This back-up (sync) functionality is actually provided by the app on an automatic (in the background) basis with a single switch in the app. These affordances of the app in actuality play into the giant data collection mechanism of Google.
Big Data & AI
Big Data and artificial intelligence (AI) go hand in hand3. As to be expected, Google, much like its major competitors such as Facebook, Apple, Amazon and Microsoft, has been long developing and improving its AI technologies (Lewis-Kraus 2016). Among such capabilities, image recognition constitute an indispensable element.
The might of image recognition algorithms are at full display in Google Photos. Uploaded images are automatically scanned for faces, animals, things, text and so forth; and tagged accordingly. It is then possible to search in and filter the images by “tangible” tags such as “bike”, “cat”, “river” and even abstract concepts such as “happy” or “history”; which will pull up pictures that contain some element that feels historic; as landmarks, old pictures of people, antiques etc.
Concerns for Privacy
A private person, who prefers not that Google, or any other company for that matter, learns the details of their private life, would possibly choose not to tag the people -such as family or close friends- in their uploaded photos, annex location data thereto or attach other sorts of additional information that would provide context for the image. But even for such a person with high regard for their own privacy, it is impossibly difficult to escape this indexing of their personal files. Through the scanning of personal images, Google is able to identify what people a person socializes with, where they live, what places they visit and much more.
Let’s assume one takes a random picture in their living room without giving it much thought, and on the table happened to lie their prescription drug. Through image recognition, it could then mean that even a part of an individual’s personal medical history that they would not want to share with Google or any of its business partners in data trade, is now captured.
This is but a small example of the ways through which every facet of our lives have now become vulnerable; exposed targets for data analytics processes of commercial and possible political purposes. Our lives are being mapped to the last inch; our interests, inclinations, values indexed and quantified. And there’s increasingly less left that is truly private to ourselves. This way, we are being primed for nudging (Yeung 2017, 121–24); and possibly being manipulated into buying product X, hating group Y or voting for party Z.
In an age where the individual has to fight to preserve their sovereignty over their own choices and thoughts, the risks associated with the capabilities Big Data presents for big tech marks a clear point of concern for the everyday citizen. While it may not be plausible to think of reversing this global trend, it would be wise to divert more focus on training everyday users on online literacy and possible personal measures to at least partially safeguard what’s left our personal privacy in a world where that is, reportedly “no longer the social norm”.4
Boyd, Danah, and Kate, Crawford. 2012. “CRITICAL QUESTIONS FOR BIG DATA: Provocations for a Cultural, Technological, and Scholarly Phenomenon.” Information, Communication & Society 15 (5): 662–79. https://doi.org/10.1080/1369118X.2012.678878.
Chandra, Harsh. 2021. “Artificial Intelligence (AI) vs Machine Learning (ML) vs Big Data.” Medium. September 24, 2021. https://heartbeat.comet.ml/artificial-intelligence-ai-vs-machine-learning-ml-vs-big-data-909906eb6a92.
De Mauro, Andrea, Marco Greco, and Michele Grimaldi. 2015. “What Is Big Data? A Consensual Definition and a Review of Key Research Topics.” In , 97–104. Madrid, Spain. https://doi.org/10.1063/1.4907823.
Gitelman, Lisa, ed. 2013. “Raw Data” Is an Oxymoron. Infrastructures Series. Cambridge, Massachusetts ; London, England: The MIT Press.
Lewis-Kraus, Gideon. 2016. “The Great A.I. Awakening.” The New York Times, December 14, 2016, sec. Magazine. https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html.
Stanfill, Mel. 2015. “The Interface as Discourse: The Production of Norms through Web Design.” New Media & Society 17 (7): 1059–74. https://doi.org/10.1177/1461444814520873.
Whitehead, Bruce A. 1981. “James J. Gibson: The Ecological Approach to Visual Perception. Boston: Houghton Mifflin, 1979, 332 Pp.” Behavioral Science 26 (3): 308–9. https://doi.org/10.1002/bs.3830260313.
Yeung, Karen. 2017. “‘Hypernudge’: Big Data as a Mode of Regulation by Design.” Information, Communication & Society 20 (1): 118–36. https://doi.org/10.1080/1369118X.2016.1186713.
1 “Google Went from a Simple Webpage to an Ubiquitous Internet Giant in 23 Years.” 2021. Xda-Developers (blog). September 27, 2021. https://www.xda-developers.com/google-simple-webpage-ubiquitous-internet-giant/. (Accessed on October 3, 2021)
2 “Mobile Operating System Market Share Worldwide.” n.d. StatCounter Global Stats. https://gs.statcounter.com/os-market-share/mobile/worldwide. (Accessed on October 3, 2021)
3 “Big Data and Artificial Intelligence: How They Work Together.” 2017. Maryville Online (blog). July 21, 2017. https://online.maryville.edu/blog/big-data-is-too-big-without-ai/. (Accessed on October 3, 2021)
4 Johnson, Bobbie, and Las Vegas. 2010. “Privacy No Longer a Social Norm, Says Facebook Founder.” The Guardian, January 11, 2010, sec. Technology. https://www.theguardian.com/technology/2010/jan/11/facebook-privacy. (Accessed on October 3, 2021)