Trust in elections is paramount for a democracy and citizens are more likely to cast ballots and support election results when they perceive election processes as trustworthy. However, with the advent of greater dependence on algorithms in election processes, we ask does a reliance on algorithms or a hybrid system for verifying signatures allay or increase citizens’ confidence in using them in elections? To answer this, we use unique survey experiments to first determine respondents’ comfort level in using such systems in elections and then to assess the circumstances which bound this trust. We find that respondents similarly trust automated and non-automated systems, but do not have a clear conception of the confidence threshold, set by policymakers, necessary for rejecting ballots. Additionally, respondents blame election officials more than algorithms when mistakes are made, although this result is contingent on the type of error and respondents’ partisanship. These results have significant implications for confidence in signature verification and other election processes that rely on artificial intelligence.
AndersonChristopherLoTempioAndrew. 2002. “Winning, Losing and Political Trust in America.” British Journal of Political Science32 (2): 335–551.
2.
ArceneauxKevinVander WielenR. J. R.. 2013. “The Effects of Need for Cognition and Need for Affect on Partisan Evaluations.” Political Psychology34 (1): 23–42.
3.
ArjonRoxana. 2020. Signature Verification and Mail Ballots: Guaranteeing Access while, Preserving Integrity: A Case Study of California’s Every Vote Counts Act. Policy Practicum: Every Vote Counts (Law 806Z). Stanford Law School, Law and Policy Lab.
4.
BansakKirk. 2021. “Estimating Causal Moderation Effects with Randomized Treatments and Non-randomized Moderators.” Journal of the Royal Statistical Society, Statistics in Society Series A184: 65–86. doi:10.1111/rssa.12614.
5.
BenderSarah M.2022. “Algorithmic Elections.” Michigan Law Review121 (3): 489–522.
6.
BerlinskiNicolasDoyleMargaretGuessAndrew M.LevyGabrielleLyonsBenjaminMontgomeryJacob M.NyhanBrendanReiflerJason. 2023. “The Effects of Unsubstantiated Claims of Voter Fraud on Confidence in Elections.” Journal of Experimental Political Science10 (1): 34–49.
7.
BowlerShaun Thomas BrunellToddDonovanPaulGronke. 2015. “Election Administration and Perceptions of Fair Elections.” Electoral Studies38: 1–9.
8.
BrayneSarah. 2020. Predict and Surveil: Data, Discretion, and the Future of Policing. Oxford University Press.
9.
BrownAdam R.2010. “Are Governors Responsible for the State Economy? Partisanship, Blame, and Divided Federalism.” The Journal of Politics72 (3): 605–615.
10.
BryantLisa. 2020. “Seeing Is Believing: An Experiment on Absentee Ballots and Voter Confidence.” American Politics Research48 (6): 700–704.
CoppockAlexanderGreenDonald P. 2015. “Is Voting Habit Forming? New Evidence from Experiments and Regression Discontinuities.” American Journal of Political Science60 (4): 1044–1062.
16.
CoppockAlexanderMcClellanOliver A.. 2019. “Validating the Demographic, Political, Psychological, and Experimental Results Obtained from a New Source of Online Survey Respondents.” Research & Politics6 (1): 2053168018822174.
17.
CoppockAlexanderLeeperT. JMullinixK. J. 2018. “Generalizability of Heterogenous Treatment Effect Estimates across Samples.” Proceedings of the National Academy of Sciences of the United States of America11549: 12441–12446.
18.
DawesRobyn M.1979. “The Robust Beauty of Improper Linear Models in Decision Making.” American Psychologist34 (7): 571–582.
19.
de Benedictis-KessnerJustin. 2018. “How Attribution Inhibits Accountability: Evidence from Train Delays.” The Journal of Politics80 (4): 1417–1422.
20.
DietvorstBerkeley J.SimmonsJoseph P.MasseyCade. 2015. “Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err.” Journal of Experimental Psychology: General144 (1): 114–126.
21.
DietvorstBerkeley J.SimmonsJoseph P.MasseyCade. 2016. “Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms if They Can (Even Slightly) Modify Them.” Management Science64 (3): 1155–1170.
22.
ElhamdadiHamzaGabaAimenKimYea-SeulXiongCindy. 2022. “How Do We Measure Trust in Visual Data Communication?” In IEEE Evaluation and Beyond-Methodological Approaches for Visualization (BELIV), 85–92. IEEE.
GerberAlanGreenDonald. 2008. “Field Experiments and Natural Experiments.” In Handbook of Political Methodology, Chapter 15, edited by Box-SteffensmeierJ. M.BradyH. E.CollierD., 357–381. Oxford University Press.
28.
GogollJanUhlMatthias. 2018. “Rage against the Machine: Automation in the Moral Domain.” Journal of Behavioral and Experimental Economics74: 97–103.
29.
GrahamDavid. 2020. “Signed, Sealed, Delivered - Then Discarded.” The Atlantic.
30.
GrantMDFloresAPedersenEJShermanDKVan BovenL. 2021. “When Election Expectations Fail: Polarized Perceptions of Election Legitimacy Increase with Accumulating Evidence of Election Outcomes and with Polarized Media.” PLoS ONE16 (12): e0259473.
31.
GreenBenFranklin-HodgeJascha. 2019. “2 the Livable City: The Limits and Dangers of New Technology.” In The Smart Enough City: Putting Technology in its Place to Reclaim Our Urban Future, 15–37. MIT Press.
32.
HallT.Quinn MonsonJ.KellyP.. 2009. “The Human Dimension of Elections: How Poll Workers Shape Public Confidence in Elections.” Public Research Quarterly62 (3): 507–522.
33.
HayesDannyGrossKimberly. 2021. “Republicans Don’t Trust Their Election Officials, Research Finds, Especially in States that Biden Won.” The Washington Post; Monkey Cage (July 26).
34.
JamesG.WittenD.HastieT.TibshiraniR.. 2013. An Introduction to Statistical Learning: With Applications in R. Springer.
35.
JamesTobyHollyGarnettLeotineLoeber.Carolien van HamC.. 2019. “Electoral Management and the Organisational Determinants of Electoral Integrity.” International Political Science Review40 (3): 295–312. doi:10.1177/0192512119828206.
36.
JanoverWilliamWestphalTom. 2020. “Signature Verification and Mail Ballots: Guaranteeing Access while Preserving Integrity - A Case Study of California’s Every Vote Counts Act.” Election Law Journal19 (3): 321–343.
37.
KennedyRyanWaggonerPhilipWardMathew. 2022. “Trust in Public Policy Algorithms.” Journal of Politics84 (2): 1132–1148.
38.
KrugerJustinDunningDavid. 1999. “Unskilled and Unaware of it: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments.” Journal of Personality and Social Psychology76 (6): 1121–1134.
39.
LeeMin Kyung. 2018. “Understanding Perception of Algorithmic Decisions: Fairness, Trust, and Emotion in Response to Algorithmic Management.” Big Data and Society5 (1): 2053951718756684.
40.
LoeberLeontine. 2011. “Political Trust and Trust in the Election Process.” Caltech/MIT Voting Technology Project; RPEAVT Paper 19.
41.
LoggJennifer Marie. 2016. When Do People Rely on Algorithms?PhD Thesis. University of California.
42.
MalhotraNeilKuoAlexander. 2008. “Attributing Blame: The Public’s Response to Hurricane Katrina.” The Journal of Politics70 (1): 120–135.
43.
MouraT.GomesA.. 2017. “Blockchain Voting and its Effects on Election Transparency and Voter Confidence.” In Proceedings of the 18th Annual International Conference on Digital Government Research. 10.1145/3085228.3085263.
44.
MoynihanDonald R.LavertuStephen. 2012. “Cognitive Biases in Governing: Technology Preferences in Election Administration.” Public Administration Review72: 68–77; January-February.
NorrisPippa. 2017. Why American Elections Are Flawed (And How to Fix Them). Cornell University Press.
47.
OzerAdamWaggonerPhilipKennedyRyan. 2024. “The Paradox of Algorithms and Blame on Public Decision-Makers.” Business and Politics26 (6): 200–217.
48.
PalanStefanSchitterChristian. 2018. “Prolific: A Subject Pool for Online Experiments.” Journal of Behavioral and Experimental Finance17: 22–27.
49.
PeerEyalBrandimarteLauraSamatSonamAcquistiAlessandro. 2017. “Beyond the Turk: Alternative Platforms for Crowdsourcing Behavioral Research.” Journal of Experimental Social Psychology70: 153–163.
50.
PeerEyalRothschildDavidGordonAndrewEverndenZakDamerEkaterina. 2022. “Data Quality of Platforms and Panels for Online Behavioral Research.” Behavior Research Methods54 (4): 1643–1662.
51.
PerakslisChristine. 2020. “Exposing Technowashing: To Mitigate Technosocial Inequalities.” IEEE Technology and Society Magazine39 (1): 88. doi:10.1109/MTS.2020.2967517.
52.
PersilyNathanielStewartCharlesIII. 2021. The Miracle and Tragedy of the 2020 Election. doi:10.2139/ssrn.3790904. Available at SSRN: https://ssrn.com/abstract=3790904
53.
SancesMichael W.StewartCharlesIII. 2015. “Partisanship and Confidence in the Vote Count: Evidence from U.S. National Elections since 2000.” Electoral Studies40: 176–188.
54.
ShaneJanelle. 2019. You Look like a Thing and I Love You. Little, Brown and Company.
55.
ShinoE.Suttmann-LeaM.SmithD. A.. 2022. “Determinants of Rejected Mail Ballots in Georgia’s 2018 General Election.” Political Research Quarterly75 (1): 231–243. doi:10.1177/1065912921993537.
56.
SurdenHarry. 2021. “Machine Learning and Law: An Overview.” In Research Handbook on Big Data Law, edited by VoglRoland. Edward Elgar Publishing.
57.
UdaniAdrianoKimballDavid C.FogartyBrian. 2018. “How Local Media Coverage of Voter Fraud Influences Partisan Perceptions in the United States.” State Politics & Policy Quarterly18 (2): 193–210. doi:10.1177/1532440018766907.
U.S. General Office of Accountability (GAO). 2007. “Elections: All Levels of Government Are Needed to Address Electronic Voting System Challenges.” In: Testimony before the Subcommittee on Information Policy, Census, and National Archives, House Committee on Oversight and Government Reform. GAO-07-741T.
XiongCPadillaL.GraysonK.FranconeriS.. 2019. “Examining the Components of Trust in Map-Based Visualizations.” In: EuroVis Workshop on Trustworthy Visualization, TrustVis, 19–23. Eurographics Association.
62.
ZeideAlana. 2021. “The Silicon Ceiling: How Artificial Intelligence Constructs an Invisible Barrier to Opportunity.” UMKC Law Review91: 403.
63.
ZwaldZacharyKennedyRyanOzerAdam. 2024. “Public Trust in Autonomous Weapons Systems.” Journal of Peace Researchforthcoming.
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