5.3: The Big Picture (Infographic)

The world of information has been transformed in unexpected ways in the past decade. These changes can be explained, in part, by the impact of algorithms. Figure 1 summarizes some of the factors driving these changes.

Figure 1: Life in the age of algorithms: A conceptualization

The impact of several convergent technologies and social trends helps explain how and why the world of information has changed so dramatically. A list of some of the most significant of these technological trends shows how these changes affect society.

1. Data collection is happening invisibly and constantly. We carry computers in our pockets that gather and share information about our daily lives, including where we go, who we associate with, what news catches our attention, and what questions we ask. These data streams may be combined with information from data brokers²⁹ and harvested from our cars and household gadgets, like baby monitors, internet connected thermostats, refrigerators, vacuums, and voice-activated assistants such as Alexa, Siri, and Google Home.³⁰
2. Advances in data science allow technologists and systems to collect and process data in real time, rapidly and on a vast scale (a development often called “big data”).³¹ This computational ability to quickly correlate enormous amounts of fine-grained, exhaustive data collected from numerous sources has opened up opportunities for companies and researchers — but also many Pandora’s boxes.
3. Automated decision-making systems are being applied to social institutions³² and processes³³ that, for better or worse, determine all kinds of things: who gets a job, a mortgage, or a loan, access to social services, admission to school or educational services.
4. Machine learning (ML) and artificial intelligence (AI), increasingly used in software products that make very significant decisions, often rely on biased or incomplete data sets. AI systems are “trained”³⁴ using existing, often human-edited³⁵ datasets, which means they can learn and amplify bias. This has implications such as teaching autonomous cars to avoid pedestrians³⁶ or recommending a prison sentence based on data from a criminal justice system that has a history of racial discrimination.³⁷
5. The disaggregation of published information and its redistribution through search and social media platforms makes evaluation of what used to be distinct sources, like articles published in an academic journal or stories in a local newspaper, all the more difficult. This disaggregation leads to an individualized presentation of information that sorts results based on inferences drawn from personal data trails. We do not all see the same information when we search and with original context missing, it is not obvious where it came from.
6. There has been a rise of the “attention economy” or “surveillance capitalism”: profitable industries gather “data exhaust” from our interaction with computers to personalize results, predict and drive behavior, target advertising, political persuasion, and social behavior at a large scale.
7. These industries appear to have difficulty anticipating or responding to unintended consequences. This may be because companies are influenced by Silicon Valley cultural values³⁸ that, among other things, consist of a belief in meritocracy, indifference to or ignorance of perspectives different from those of affluent White males,³⁹ a global reach coupled with a lack of cultural competence,⁴⁰ and magical thinking about the preeminent goodness of individualism and free speech.⁴¹
8. Decades of media consolidation, deregulation, and economic trends combined with the rise of social media platforms that are designed for persuasion but have no ethical duty of care, have contributed to engineered distrust of established knowledge traditions such as journalism and scholarship, and the global destabilization of political and social institutions.

The technical infrastructure that influences how we acquire information and shapes our knowledge and beliefs has changed dramatically in ways that are largely invisible to the public — by design. We are facing a lack of public knowledge⁴² about who holds power over information systems and how that power is wielded, a gap in understanding that educators need to begin to address.⁴³ Given this sea change, questions about what it means for students to be information literate today, and whether they know how information works in the age of algorithms, are of paramount importance.

What exactly is information literacy?

The term “information literacy” is sometimes mistakenly conflated with “library instruction,” but its meaning is really much broader. Information literacy is a collective effort of librarians, media specialists, technologists, and educators across the educational spectrum. It incorporates elements of media literacy, digital literacy, news literacy, and critical thinking.

Taken together, information literacy is an integrated set of skills, knowledge, practices, and dispositions that prepares students to discover, interpret, and create information ethically while gaining a critical understanding of how information systems interact to produce and circulate news, information, and knowledge.*

* The Association of College and Research Libraries (11 January 2016) has developed a “Framework for information literacy for higher education” that offers a discussion of the phrase and outlines core concepts for college-level students, http://www.ala.org/acrl/ standards/ilframework

References

29. Molly Wood (7 March 2019), “Data brokers are in the business of selling your online information.” Marketplace, www.marketplace. org/2019/03/07/when-it-comes-to-intrusive-data-collection-facebook-might-be-the-least-of-our-problems-data-brokers/
30. Bruce Schneier (2015), Data and Goliath: The hidden battles to collect your data and control your world, Norton.
31. For a thorough introduction to the relationship of big data to knowledge, see Rob Kitchin (2014), “Big data, new epistemologies and paradigm shifts,” Big Data & Society 1(1), 1-12, DOI: https://doi.org/10.1177/2053951714528481
32. See for example Virginia Eubanks 2018, op. cit.
33. An accessible introduction to how algorithms influence society can be found in Cathy O’Neil 2017, op. cit.
34. Karen Hao (4 February 2019), “This is how AI bias really happens - and why it’s so hard to fix,” MIT Technology Review, www. technologyreview.com/s/612876/this-is-how-ai-bias-really-happensand-why-its-so-hard-to-fix/
35. Mary L. Gray and Siddharth Suri (2019), Ghost work: How to stop Silicon Valley from building a new global underclass, Houghton Mifflin Harcourt.
36. Sigal Samuel (6 March 2019), “A new study finds a potential risk with self-driving cars: Failure to detect dark-skinned pedestrians,” Vox, https://www.vox.com/future-perfect/2...icle-dark-skin
37. Stephanie Wykstra (12 July 2018), “Can racial bias ever be removed from criminal justice algorithms?” Pacific Standard, https://psmag.com/ social-justice/removing-racial-bias-from-the-algorithm
38. Alice E. Marwick (2013). Status Update: Celebrity, publicity, and branding in the social media age, Yale University Press.
39. Marie Hicks (2017), Programmed inequality: How Britain discarded women technologists and lost its edge in computing, MIT Press; Ruha Benjamin (2019), Race after technology: Abolitionist tools for the new Jim Code, Wiley.
40. Alexandra Stevenson (6 November 2018), “Facebook admits it was used to incite violence in Myanmar,” The New York Times, nyti. ms/2yVAO5X
41. Fred Turner (2010), From counterculture to cyberculture: Stewart Brand, the Whole Earth Network, and the rise of digital utopianism, University of Chicago Press; Timothy Garton Ash (2016), Free Speech: Ten Principles for a Connected World, Yale University Press.
42. The implications of this unequal “division of learning” are explored in Chapter 6 of Shoshana Zuboff (2019), The age of surveillance capitalism: The fight for a human future at the new frontier of power, PublicAffairs.
43. Annemaree Lloyd (26 September 2019), “Chasing Frankenstein’s monster: Information literacy in the black box society,” Journal of Documentation 75(6), 1475-1485. DOI: doi.org/10.1108/JD-02-2019-0035

Contributors and Attributions

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