mlsanityData to 7 Oct 2026

AI and Core Electoral Processes: Mapping the Horizons

arXiv 2302.03774 ↗First posted 7 Feb 2023cs.CY cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Significant enthusiasm around AI uptake has been witnessed across societies globally. The electoral process -- the time, place and manner of elections within democratic nations -- has been among those very rare sectors in which AI has not penetrated much. Electoral management bodies in many countries have recently started exploring and deliberating over the use of AI in the electoral process. In this paper, we consider five representative avenues within the core electoral process which have potential for AI usage, and map the challenges involved in using AI within them. These five avenues are: voter list maintenance, determining polling booth locations, polling booth protection processes, voter authentication and video monitoring of elections. Within each of these avenues, we lay down the context, illustrate current or potential usage of AI, and discuss extant or potential ramifications of AI usage, and potential directions for mitigating risks while considering AI usage. We believe that the scant current usage of AI within electoral processes provides a very rare opportunity, that of being able to deliberate on the risks and mitigation possibilities, prior to real and widespread AI deployment. This paper is an attempt to map the horizons of risks and opportunities in using AI within the electoral processes and to help shape the debate around the topic.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 140 references that are in the map. First posted on arXiv 7 Feb 2023.

Builds on

20 of 140 references.

References · 20

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2017One pixel attack for fooling deep neural networkscited by 579
  3. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  4. 2020Deep Learning for Person Re-identification: A Survey and Outlookcited by 547
  5. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  6. 2021The Values Encoded in Machine Learning Researchcited by 172
  7. 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 100
  8. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  9. 2020Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditingcited by 89
  10. 2020The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Researchcited by 67
  11. 2020Individual Fairness for k-Clusteringcited by 56
  12. 2020Fair clustering via equitable group representationscited by 43
8 more
  1. 2020A Notion of Individual Fairness for Clusteringcited by 20
  2. 2021Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognitioncited by 10
  3. 2020Representativity Fairness in Clusteringcited by 5
  4. 2020On the Complexity of Learning from Label Proportionscited by 4
  5. 2019A Parameterized Perspective on Protecting Electionscited by 1
  6. 2020Selecting Voting Locations for Fun and Profitcited by 1
  7. 2021FiSH: Fair Spatial Hotspotscited by 1
  8. 2022Locating ballot drop boxescited by 1

Built on by

Not cited yet.