Lay Perceptions of Selection Decision Aids in US and Non-US Samples

By Dalia L. Diab, S. Pui, M. Yankelevich, Scott Highhouse. 2011

116 citations recorded by the source corpus.

Subfields: Gender Studies, Organizational Behavior and Human Resource Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/abb31e3d2aed4a3878f40280fb98f217441328d5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Gender Studies

1. David E. Rapach

In Organizational Behavior and Human Resource Management

1. Ulrich Gunter
2. Evangelos Spiliotis
3. Philip Hans Franses
4. Donald R. Lehmann
5. D. Onkal
6. Vassilios Assimakopoulos

In Sociology and Political Science

1. Ulrich Gunter
2. Nigel Harvey
3. Noah Castelo
4. Pierre Pinson
5. Massimo Guidolin
6. Konstantinos Nikolopoulos
7. Spyros Makridakis
8. Philip Hans Franses

Citations in the corpus

• Forecasting: theory and practice (2020, background): “, 2020) and place higher trust on human advice (Diab et al., 2011; Eastwood et al., 2012).”
• Trust in AI: progress, challenges, and future directions (2024, unknown): “Many human investors would rather trust a human prediction than an algorithmic prediction (Diab et al., 2011), a phenomenon known as algorithm aversion because humans are more tolerant if a human is mistaken than if it is an algorithm.”
• Task-Dependent Algorithm Aversion (2019, background): “In the domain of employee selection and hiring decisions, Diab et al. (2011) found that participants thought that human interviews were more useful, professional, fair, personal, flexible, and precise than algorithms.”