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David Almog

dblp:367/4802 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0006-8772-6171ORCID · 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.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 100%
Artificial intelligence
1 paper
Trustworthy machine learning · 50% Multi-agent systems · 50%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning
fairness
0.212024
AI Oversight and Human Mistakes: Evidence from Centre Court · EC 2024
Knowledge, reasoning and agents › Multi-agent systems › human-agent interaction
human-AI decision making
0.212024
AI Oversight and Human Mistakes: Evidence from Centre Court · EC 2024

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

field study · 1.5
YearPublicationVenuePosition
2024 AI Oversight and Human Mistakes: Evidence from Centre Court
abstract
Powered by the increasing predictive capabilities of machine learning algorithms, artificial intelligence (AI) systems have acquired the ability to improve outcomes by overruling human mistakes. However, to assess the full impact of adding AI oversight, we must understand whether its presence alters human decision-making. While a large amount of research has focused on how humans respond when being assisted by AI, very little is known about how humans respond when their decisions might be overruled by AI. Because being overruled can carry psychological costs (e.g., shame and embarrassment of being overruled) and psychological benefits (e.g., relief at having their mistakes fixed), individuals might alter their decision-making under AI oversight. We provide the first field evidence that AI oversight carries psychological costs that can impact human decision-making.
David Almog, Romain Gauriot, Lionel Page
EC1