1: The Age of Algorithms

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Algorithms — rule-based processes for solving problems — predate computers. It was not, however, until the word “Google” became synonymous with “to search online” in the early 2000s⁷ that the idea of algorithms entered the public consciousness.⁸ That was when we began to notice how clever computer code influenced our daily lives by recommending Netflix films,⁹ remembering preferences for Amazon purchases, and finding our friends on Facebook’s precursors, such as Friendster and MySpace. Within a few years of its founding in 1998, Google needed a profitable business model, so it began to make use of the digital trails we all leave behind to profit from personalized advertising. Facebook soon followed. The behemoth social media platform built its reputation and advertising might on its “social graph,” the interconnections among people online, enriched by metrics of “friends” and “likes.”¹⁰ During the same time, the news industry began to struggle as startups like Craigslist began to cannibalize classified ad revenues and subscriptions dwindled as readers enjoyed free news online.¹¹ News organizations were forced to negotiate fraught relationships with platforms that increasingly dominated both digital advertising and monopolized audience attention.¹² Fast forward to 2015, when controversies around “fake news” and the splintering of global audiences into polarized camps led the public to view algorithms as powerful, efficient — and often questionable — drivers of innovation and social change. The rise of what is widely known as the “age of algorithms” has had a profound impact on society,¹³ on politics,¹⁴ on the news,¹⁵ and on epistemology.^16,17 And yet, most algorithms are easy to ignore since we cannot see, hear, or touch them. While they are hard at work, many of us do not give much thought to the hidden minutiae of their constantly changing proprietary formulas. Their lines of complex and opaque code make lightning-fast decisions for and about us in both helpful and unhelpful ways. Algorithms are not inherently good or bad. Rather, their effects depend on what they are programmed to do, who’s doing the programming and to what end, how the algorithms operate in practice, how users interact with them, and what is done with the huge amount of personal data they feed on.¹⁸ On the plus side, these mysterious black boxes can answer in seconds a question that formerly required hours in a library (though the answer may not necessarily be entirely accurate). Social media platforms like Facebook, Twitter, and Instagram let us share photos, personal news, and links with strangers across the globe whose interests align with ours. We can organize disaster relief or a grassroots social movement from far away. We can teach machines to pinpoint the location of brain tumors or help reduce traffic congestion.¹⁹ But algorithms also have influence we may not anticipate, since their use increasingly has political and societal dimensions.²⁰ Using incomplete datasets to predict odds of success, algorithms may determine who does and does not get into college based on their zip code rather than their academic efforts.²¹ Algorithms may be programmed to decide who is invited to interview and, ultimately, who gets a job offer.²² They might recommend which loan applicants are a good credit risk.²³ These invisible lines of code may even establish the length of a criminal sentence.²⁴

In our daily lives, algorithms are often used to filter the news we see about the world,²⁵ potentially swaying decisions about what we buy and how we vote.²⁶ They may determine the results students get from searches in their college or university library.²⁷ At worst, data swept up by these algorithms can be used by state actors, criminals, or trolls bent on disruption or sabotage.²⁸

References

Virginia Heffernan (15 November 2017), “Just Google it: A short history of a newfound verb,” Wired, www.wired.com/story/just-goo...newfound-verb/
Tarleton Gillespie (2017), “Algorithm,” Digital keywords: A vocabulary of information society and culture, Benjamin Peters (Ed.), Princeton University Press, 18-29.
Clive Thompson (21 November 2008), “If you liked this, you’re sure to love that,” The New York Times Magazine, www.nytimes. com/2008/11/23/magazine/23Netflix-t.html
Jonathan Haidt and Tobias Rose-Stockwell (December 2019), “The dark psychology of social networks,” The Atlantic, www.theatlantic. com/magazine/archive/2019/12/social-media-democracy/600763/
Ángel Arrese (2016), “From gratis to paywalls: A brief history of a retro-innovation in the press’s business,” Journalism Studies 17(8), 1051– 1067, DOI: doi.org/10.1080/1461670X.2015.1027788
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Cathy O’Neil (2016), Weapons of math destruction: How big data increases inequality and threatens democracy, Crown; Safiya Umoja Noble (2018). Algorithms of oppression: How search engines reinforce racism, New York University Press; Virginia Eubanks (2018). Automating inequality: How hightech tools profile, police, and punish the poor, St. Martin’s Press; Sofia C. Olhede and Patrick J. Wolfe (2019), “The growing ubiquity of algorithms in society: Implication, impact and innovation,” Philosophical Transactions of the Royal Society 376(128), DOI: https://doi.org/10.1098/rsta.2017.0364
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John P. Wihbey (2019), The social fact: News and knowledge in a networked world, MIT Press, especially chapter 5, “Bias in network architecture and platforms”; Jihii Jolly (20 May 2014), “How algorithms decide the news you see,” Columbia Journalism Review, https://archives.cjr.org/ news_literacy/algorithms_filter_bubble.php; Laura Hazard Owen (15 March 2019), “One year in, Facebook’s big algorithm change has spurred an angry, Fox News-dominated — and very engaged! — News Feed,” NeimanLab, www.niemanlab.org/2019/03/on...ged-news-feed/
Lee McIntyre (2018), Post-truth. MIT Press; Jonathan Zittrain (2019), op. cit.
For a list of keywords and definitions used in this discussion and throughout the report, see p. 49.
Hannah Fry (2018), Hello world: Being human in the age of algorithms, W. W. Norton & Company.
“Computer program beats doctors at distinguishing brain tumors from radiation changes”(16 September 2016), Neuroscience News, https:// neurosciencenews.com/ai-brain-cancer-neurology-5058/; Francesca Baker (12 December 2018), “The technology that could end traffic jams,” BBC Future, http://www.bbc.com/future/story/2018...d-traffic-jams
Lawrence Lessig (1999), Code and other laws of cyberspace, Basic Books; Mark MacCarthy (19 March 2019), “The ethical character of algorithms— and what It means for fairness, the character of decision-making, and the future of news,” The Ethical Machine, Shorenstein Center, Harvard University, ai.shorensteincenter.org/ide...-of-news-yak6m; Langdon Winner (Winter 1980), “Do artifacts have politics?” Daedalus 109(1), www. researchgate.net/publication/213799991_Do_Artifacts_Have_Politics
Bridget McCrea (16 November 2015), “Science develops an algorithm for college selection—but does it work?” Ecampus News, www. ecampusnews.com/2015/11/16/algorithm-college-selection-329/
Gideon Mann and Cathy O’Neil (9 December 2016), “Hiring algorithms are not neutral,” Harvard Business Review, hbr.org/2016/12/hiring-algor...re-not-neutral; Drew Harwell (6 November 2019), “HireVue’s AI face-scanning algorithm increasingly decides whether you get the job,” Washington Post, https:// www.washingtonpost.com/technology/2019/10/22/ai-hiring-face-scanning-algorithm-increasingly-decides-whether-you-deserve-job/
Cathy O’Neil, (2016), op. cit.
Julia Dressel and Hany Farid (2018), “The accuracy, fairness, and limits of predicting recidivism,” Science Advances4(1), DOI: https://doi.org/10.1126/sciadv.aao5580
David R. Brake (2017), “The invisible hand of the unaccountable algorithm: How Google, Facebook and other tech companies are changing journalism,” Digital Technology and Journalism, Jingrong Tong and Shih-Hung Lo (Eds.), Palgrave Macmillan, 25-46.
Robert Epstein and Ronald E. Robertson (2015), “The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections,” Proceedings of the National Academy of Sciences 112(33), E4512-E4521, DOI: https://doi.org/10.1073/pnas.1419828112
Matthew Reidsma (11 March 2016), “Algorithmic bias in library discovery systems,” Reidsma Working Notes, https://matthew.reidsrow.com/ articles/173
Bruce Schneier (2018), Click here to kill everybody: Security and survival in a hyper-connected world, Norton.

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