Evidence map›Paper›PMID 42059986›Full record

ArticleScience and engineering ethics2026

The Complexities of AI for Social Good: An Explorative Study on Adversarial Machine Learning to Facilitate Deliberative Decision-Making in Elections.

Syafira Fitri Auliya, Olya Kudina, Aaron Yi Ding, Ibo Van de Poel

Abstract read
In one paragraph

Article in Science and engineering ethics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Syafira Fitri AuliyaSection Ethics and Philosophy of Technology, Department of Values, Technology, and Innovation, Faculty of Technology, Policy, and Management, TU Delft, Jaffalaan 5, Delft, 2628 BX, The Netherlands. s.f.auliya@tudelft.nl.ORCID http://orcid.org/0000-0001-8634-3677
Olya KudinaSection Ethics and Philosophy of Technology, Department of Values, Technology, and Innovation, Faculty of Technology, Policy, and Management, TU Delft, Jaffalaan 5, Delft, 2628 BX, The Netherlands.
Aaron Yi DingDepartment of Engineering Systems and Services, Faculty of Technology, Policy, and Management, TU Delft, Jaffalaan 5, Delft, 2628 BX, The Netherlands.
Ibo Van de PoelSection Ethics and Philosophy of Technology, Department of Values, Technology, and Innovation, Faculty of Technology, Policy, and Management, TU Delft, Jaffalaan 5, Delft, 2628 BX, The Netherlands.

Funding

Lembaga Pengelola Dana Pendidikan KEP581/LPDP/LPDP.3/2022
6 · The paper itself

Abstract

The proliferation and pervasive use of artificial intelligence (AI) pose significant challenges to our democracies. In particular, AI leverages microtargeting political campaigns by constructing detailed user profiles and inferring people's individual susceptibilities from their data. This capability enables highly targeted political messaging that can substantially influence voting decisions, potentially undermining citizens' ability to make deliberative decisions in elections. While existing research has proposed interventions to mitigate the negative impacts of political micro-targeting, many of these interventions may become less effective as AI continually yields more powerful and subliminal forms of micro-targeting. Assuming that technologies can play a role in mitigating these negative impacts of AI, we conducted an explorative study to identify the design principles of technological tools that could facilitate citizens' deliberative decision-making in elections in the continuously levelaged AI-based political campaigns. Using Indonesia's 2024 elections as a case study, we interviewed twenty citizens and four political actors to gain critical insights into how such technologies might be developed. Initially, we anticipated that privacy-enhancing AI, used to counterattack profiling AI used by political actors, might suffice to facilitate election deliberation. However, our findings reveal a more complex reality: addressing societal issues through technology is inherently challenging; no single solution can serve as a silver bullet. Instead, facilitating election deliberation requires integrating privacy with other conditions, including self-reflection, education, access to diverse information, critical thinking, and openness to others. These design principles might serve as concrete, actionable design principles to guide the development of technologies to enhance election deliberation.

Indexed as

Artificial IntelligenceDecision MakingMachine LearningPoliticsHumansPrivacyVotingAdversarial machine learningArtificial intelligenceDeliberative decision-makingElectionsMicro-targeting political campaignsPrivacy

Identifiers

PMID42059986
PMCPMC13337672

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.