Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Marie-Pierre Dargnies

dblp:121/8328 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0001-5788-2823ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Theory of computation · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities › algorithmic decision-making
algorithmic hiring		 0.812024
Behavioral measures improve AI hiring: A field experiment · EC 2024
Data mining
predictive modeling		 0.212024
Behavioral measures improve AI hiring: A field experiment · EC 2024

Methods — techniques the papers use, named apart from their topics

field experiment · 1.5cognitive and behavioral measures · 1.5
YearPublicationVenuePosition
2024 Behavioral measures improve AI hiring: A field experiment
abstract
The adoption of Artificial Intelligence (AI) for hiring processes is often impeded by a scarcity of comprehensive employee data. We hypothesize that the inclusion of behavioral measures elicited from applicants can enhance the predictive accuracy of AI in hiring. We study this hypothesis in the context of microfinance credit officers. We focus on cognitive and behavioral measures commonly used in economics and psychology.
Marie-Pierre Dargnies, Rustamdjan Hakimov, Dorothea Kübler
 EC1