Restricted accessResearch articleFirst published online 2022-10
Two tales about the power of algorithms in online environments: on the need for transdisciplinary dialogue in the study of algorithms and digital capitalism
This commentary discusses an important disconnect between different literatures dealing with the power of algorithms – a disconnect that has important implications for the narratives about the role of algorithms in today’s societies that the humanities and social science construct. Theoretical work has regularly depicted algorithmic systems in the hands of large tech companies as powerful devices for effectively influencing behaviors through persuasive targeted information offers in online environments. However, this account goes against existing empirical evidence on the effectiveness of algorithm-based targeting. The paper highlights the different stories about the power of algorithms that these literatures tell and discusses why addressing this gap matters. In a nutshell, while the idea of people being steered by powerful algorithmic systems is arguably an intriguing aspect of digital-era capitalism, it risks distracting from more mundane, but also more relevant aspects of algorithms operating in online environments and how they can sustain power relations.
AralS (2020) The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health—And How We Must Adapt, 1st edn. New York: Currency.
2.
Baldwin-PhilippiJ (2017) The myths of data-driven campaigning. Political Communication34(4): 627–633.
3.
BarberáP (2020) Social media, echo chambers, and political polarization. In: PersilyNTuckerJA (eds) Social Media and Democracy: The State of the Field, Prospects for Reform, 1st edn. Cambridge: Cambridge University Press, pp.34–55.
4.
BarocasS (2012) The price of precision: Voter microtargeting and its potential harms to the democratic process. In: Proceedings of the first edition workshop on politics, elections and data (eds Weber I and Association for Computing Machinery). New York, NY: ACM Press, pp.31–36.
5.
BarocasSSelbstAD (2016) Big data’s disparate impact. California Law Review104: 671–732.
6.
BleierAEisenbeissM (2015) Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science34(5): 669–688.
7.
BroussardM (2018) Artificial Unintelligence: How Computers Misunderstand the World. Cambridge, MA: The MIT Press.
8.
BrunsA (2019) Are Filter Bubbles Real? Digital Futures. Medford, MA: Polity Press.
9.
BurgeCDWambleJJLairdCN (2020) Missing the mark? An exploration of targeted campaign advertising’s effect on Black political engagement. Politics Groups and Identities8(2): 423–437.
10.
BurrellJFourcadeM (2021) The society of algorithms. Annual Review of Sociology47(1): 213–237.
11.
CohenJ (2014) Studying law studying surveillance. Surveillance & Society13(1): 91–101.
12.
CohenJE (2012) Configuring the Networked Self: Law, Code, and the Play of Everyday Practice. New Haven, CT: Yale University Press.
13.
CouldryNMejiasUA (2019) The Costs of Connection: How Data is Colonizing Human Life and Appropriating it for Capitalism. Stanford, CA: Stanford University Press.
14.
CourtoisCSlechtenLCoenenL (2018) Challenging Google Search filter bubbles in social and political information: Disconforming evidence from a digital methods case study. Telematics and Informatics35(7): 2006–2015.
15.
CrawfordK (2021) Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven, CT: Yale University Press.
16.
CrawfordKCaloR (2016) There is a blind spot in AI research. Nature538(7625): 311–313.
17.
DobberTMetouiNTrillingD, et al (2021) Do (microtargeted) deepfakes have real effects on political attitudes?The International Journal of Press/Politics26(1): 69–91.
18.
DogruelL (2019) Too much information!? Examining the impact of different levels of transparency on consumers’ evaluations of targeted advertising. Communication Research Reports36(5): 383–392.
19.
DuboisEBlankG (2018) The echo chamber is overstated: The moderating effect of political interest and diverse media. Information Communication & Society21(5): 729–745.
20.
EvansNJWojdynskiBWGrubbs HoyM (2019) How sponsorship transparency mitigates negative effects of advertising recognition. International Journal of Advertising38(3): 364–382.
21.
EyalNHooverR (2014) Hooked: How to Build Habit-Forming Products. London: Portfolio Penguin.
22.
FarmanLComelloMLEdwardsJR (2020) Are consumers put off by retargeted ads on social media? Evidence for perceptions of marketing surveillance and decreased Ad effectiveness. Journal of Broadcasting & Electronic Media64(2): 298–319.
23.
FelzmannHVillarongaEFLutzC, et al (2019) Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns. Big Data & Society6(1): 205395171986054.
24.
FitzsimonsGJLehmannDR (2004) Reactance to recommendations: When unsolicited advice yields contrary responses. Marketing Science23(1): 82–94.
25.
FranzM (2013) Targeting campaign messages: Good for campaigns but bad for America? In: RidoutTN (ed.) New Directions in Media and Politics: New Directions in American Politics. New York: Routledge, Taylor & Francis Group, pp.113–131.
26.
GortonWA (2016) Manipulating citizens: How political campaigns’ use of behavioral social science harms democracy. New Political Science38(1): 61–80.
27.
HaimMGraefeABrosiusH-B (2018) Burst of the filter bubble?: Effects of personalization on the diversity ofGoogle News. Digital Journalism6(3): 330–343.
28.
HamC-D (2017) Exploring how consumers cope with online behavioral advertising. International Journal of Advertising36(4): 632–658.
29.
HwangT (2020) Subprime Attention Crisis: Advertising and the Time Bomb at the Heart of the Internet, 1st edn. New York: FSG Originals, Farrar, Straus and Giroux.
30.
JustNLatzerM (2017) Governance by algorithms: Reality construction by algorithmic selection on the Internet. Media Culture & Society39(2): 238–258.
KimTBaraszKJohnLK (2019) Why am I seeing this Ad? The effect of ad transparency on Ad effectiveness. Journal of Consumer Research45(5): 906–932.
33.
KrafftTDGamerMZweigKA (2018) What did you see?Personalization, regionalization and the question of the filter bubble in Google’s search engine. Available at: https://arxiv.org/abs/1812.10943 (accessed 5 June 2022).
34.
KrotzekL (2019) Inside the voter’s mind: The effect of psychometric microtargeting on feelings toward and propensity to vote for a candidate. International Journal of Communication13: 21.
35.
KruikemeierSSezginMBoermanSC (2016) Political microtargeting: Relationship between personalized advertising on Facebook and voters’ responses. Cyberpsychology Behavior and Social Networking19(6): 367–372.
36.
LanzingM (2019) “Strongly recommended” revisiting decisional privacy to judge hypernudging in self-tracking technologies. Philosophy & Technology32: 549–568.
37.
LavigneM (2021) Strengthening ties: The influence of microtargeting on partisan attitudes and the vote. Party Politics27(5): 965–976.
38.
LepriBOliverNLetouzéE, et al (2018) Fair, transparent, and accountable algorithmic decision-making processes: The premise, the proposed solutions, and the open challenges. Philosophy & Technology31(4): 611–627.
39.
LevyKChasalowKERileyS (2021) Algorithms and decision-making in the public sector. Annual Review of Law and Social Science17(1): 309–334.
40.
LiJLuoXLuX, et al (2021) The double-edged effects of E-commerce cart retargeting: Does retargeting too early backfire?Journal of Marketing85: 123–140.
41.
MalheirosMJennettCPatelS, et al (2012) Too close for comfort: A study of the effectiveness and acceptability of rich-media personalized advertising. In: Proceedings of the SIGCHI conference on human factors in computing systems, Austin, TX, 5 May 2012, pp.579–588. New York: ACM Press. DOI: 10.1145/2207676.2207758.
MittelstadtBDAlloPTaddeoM, et al (2016) The ethics of algorithms: Mapping the debate. Big Data & Society3(2): 1–21.
45.
PariserE (2011) The Filter Bubble: What the Internet is Hiding From You. New York, NY: Penguin Press.
46.
PasqualeF (2015) The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge, MA: Harvard University Press.
47.
RouvroyA (2013) The end(s) of critique: Data behaviorism versus due process. In: HildebrandtMVriesK (eds) Privacy, Due Process and the Computational Turn: The Philosophy of Law Meets the Philosophy of Technology. Milton Park: Routledge, pp.143–167.
ZaroualiBDobberTDe PauwG, et al (2020) Using a personality-profiling algorithm to investigate political microtargeting: Assessing the persuasion effects of personality-tailored ads on social media. Communication Research. Epub ahead of print 20 October 2020. DOI: 10.1177/0093650220961965.
59.
ZuboffS (2019) The Age of Surveillance Capitalism: The Fight for the Future at the New Frontier of Power. London: Profile Books.
60.
Zuiderveen BorgesiusFJMöllerJKruikemeierS, et al (2018) Online political microtargeting: Promises and threats for democracy. Utrecht Law Review14(1): 82–96.
61.
Zuiderveen BorgesiusFJTrillingDMöllerJ, et al (2016) Should we worry about filter bubbles?Internet Policy Review5(1): 1–16.
