AI and elections: An introduction to the special issue
Notice bibliographique
Résumé
A vibrant democracy relies on engaged voters making informed decisions about their representatives and keeping them accountable employing reliable information and secure election infrastructure. Significant and continuous effort is needed in improving a democracy and elections are a key part of that. Democracy at a practical level means empowering the voter with a right to choose and providing multiple capabilities, including knowledge about candidates, campaign finance, voting, processing votes, and so forth. Artificial Intelligence and machine learning have transformed modern society. It also impacts how elections are conducted in democracies, with mixed outcomes. For example, digital marketing campaigns have enabled candidates to connect with voters at scale and communicate remotely during COVID-19, but there remains widespread concern about the spread of election disinformation as the result of AI-enabled bots and aggressive strategies. In response, we conducted the first workshop at Neurips 2021 to examine the challenges of credible elections globally in an academic setting with apolitical discussion of significant issues. The speakers, panels, and reviewed papers discussed current and best practices in holding elections, tools available for candidates, and the experience of voters. They highlighted gaps and experience regarding AI-based interventions and methodologies. To ground the discussion, the invited speakers and panelists were drawn from three International geographies: US—representing one of the world's oldest democracies; India—representing the largest democracy in the world; and Estonia—representing a country using digital technologies extensively during elections and as a facet of daily life. The workshop had contributions on all technological and methodological aspects of elections and voting. At AAAI 2023, we ran the second edition of the workshop. It focused on topics of interest to election candidates like organizing candidate campaigns and detecting, informing, and managing mis- and disinformation; for election organizers, identifying and validating voters and informing people about election information; for voters, knowing about election procedures, verifying individual and community votes, navigating candidates and issues; and cross-cutting. Issues like promoting transparency in the election process, technology for data management and validation, and case studies of success or failure, and the reasons thereof. This time, additional speakers discussed experiences from Brazil, Canada, and Ireland. The workshop discussed AI trends, security gaps in elections and the lack of a standard secure stack to build trusted data-driven applications for elections, how AI and technology are already being used to make the election process work and how to improve, the role of journalists with AI and what policy steps are needed to adopt technology for a better-informed citizen. This special issue on AI for elections highlights some of the insightful perspectives from the two workshops. This includes a review of AI and core electoral processes, how chatbots could be used to promote voter participation, understanding attempts for voter polarization, detecting election frauds, and a new form of voting for user surveys. We hope they promote more community engagement for a multi-disciplinary research collaboration between AI, security, journalism, political science, and law for democracies around the world. Biplav Srivastava (University of South Carolina), Anita Nikolich (University of Illinois-Urbana Champaign), Huan Liu (Arizona State University), Natwar Modani (Adobe Research), Tarmo Koppel (University of South Carolina and Tallinn University of Technology) served as cochairs of the first workshop at Neurips 2021. Biplav Srivastava (University of South Carolina), Anita Nikolich (University of Illinois-Urbana Champaign), Andrea Hickerson (University of Mississippi), Tarmo Koppel (Tallinn University of Technology), Chris Dawes (New York University), and Sachindra Joshi (IBM Research) served as cochairs of the second workshop at AAAI 2023. The authors declare that there is no conflict. Biplav Srivastava is a professor of computer science at the AI Institute and Department of Computer Science and Engineering at the University of South Carolina, USA. Anita Nikolich is a research scientist at the School of Information Sciences at the University of Illinois at Urbana-Champaign, USA. Tarmo Koppel is a member of the faculty at the business school of Tallinn University of Technology, Estonia.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,010 | 0,008 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,006 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,067 | 0,033 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».