A parameterized perspective on protecting elections

Dey, Palash; Misra, Neeldhara; Nath, Swaprava; Shakya, Garima

doi:10.1016/j.tcs.2021.05.006

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…This provides a backdrop into the nonexistence (as far as we could assess) of bespoke AI techniques for polling booth protection prioritization. There has been emerging theoretical AI work on prioritizing protection of "voter groups" to ensure result stability in elections (Dey et al 2019(Dey et al , 2021, under conditions that we regard as quite synthetic; these may need much adaptation to be used within the task of prioritizing polling booths to protect. Our choice of identifying problematic polling booths as an avenue for AI usage is strongly motivated by observations of burgeoning AI development in a task that is very similar in spirit, that of hot spot policing that seeks to identify crime-prone locations, which we describe below.…”

Section: Extant or Potential Ai Usagementioning

confidence: 99%

AI and core electoral processes: Mapping the horizons

Simoes,

MacCarthaigh

2023

AI Magazine

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It is well documented that there has been significant enthusiasm across the globe in respect of using AI for all forms of social activity. However, the electoral process – the time, place, and manner of elections within democratic nations – is one of few sectors in which there has been limited penetration of AI. 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 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 avenue, we lay down the context, illustrate current or potential usage of AI, and discuss extant or potential ramifications of AI usage, as well as potential directions for mitigating risks when considering AI usage. We believe that the scant current usage of AI within electoral processes provides a very rare opportunity to deliberate on the risks and mitigation possibilities prior to actual and widespread AI deployment. This paper is an attempt to map the horizons of risks and opportunities in using AI within electoral processes and to help shape the debate around the topic.

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Section: Extant or Potential Ai Usagementioning

confidence: 99%

AI and core electoral processes: Mapping the horizons

Simoes,

MacCarthaigh

2023

AI Magazine

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“…We show that $Bribery Is Safe is co-NP-complete for the k-approval voting rule for every constant k 3. We need the following structural result for k-approval voting rule which has been used before [2,12,14].…”

Section: Definition 21 (Exact Cover By 3-sets) Given a Universementioning

confidence: 99%

How Hard is Safe Bribery?

Karia¹,

Faraaz²,

Dey³

2022

Preprint

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Bribery in an election is one of the well-studied control problems in computational social choice. In this paper, we propose and study the safe bribery problem. Here the goal of the briber is to ask the bribed voters to vote in such a way that the briber never prefers the original winner (of the unbribed election) more than the new winner, even if the bribed voters do not fully follow the briber's advice. Indeed, in many applications of bribery, campaigning for example, the briber often has limited control on whether the bribed voters eventually follow her recommendation and thus it is conceivable that the bribed voters can either partially or fully ignore the briber's recommendation. We provide a comprehensive complexity theoretic landscape of the safe bribery problem for many common voting rules in this paper.

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