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Serhiy Kandul

dblp:319/3513 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2023
0000-0001-8175-2252ORCID · 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 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 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%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
reliance on AI
0.612022
Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making · CHI 2022
Human-AI interaction › responsible AI
responsibility attribution
0.612022
Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making · CHI 2022
Human-AI interaction
trust in AI
0.612022
Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making · CHI 2022
Human-AI interaction › human decision-making
moral decision-making
0.212022
Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making · CHI 2022

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

vignette study · 0.6survey · 0.6
YearPublicationVenuePosition
2023 Human control redressed: Comparing AI and human predictability in a real-effort task
Serhiy Kandul, Vincent Micheli, Juliane Beck, Thomas Burri, François Fleuret, Markus Kneer, Markus Christen
CogSci1
2022 Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making
abstract
While artificial intelligence (AI) is increasingly applied for decision-making processes, ethical decisions pose challenges for AI applications. Given that humans cannot always agree on the right thing to do, how would ethical decision-making by AI systems be perceived and how would responsibility be ascribed in human-AI collaboration? In this study, we investigate how the expert type (human vs. AI) and level of expert autonomy (adviser vs. decider) influence trust, perceived responsibility, and reliance. We find that participants consider humans to be more morally trustworthy but less capable than their AI equivalent. This shows in participants’ reliance on AI: AI recommendations and decisions are accepted more often than the human expert’s. However, AI team experts are perceived to be less responsible than humans, while programmers and sellers of AI systems are deemed partially responsible instead.
Suzanne Tolmeijer, Markus Christen, Serhiy Kandul, Markus Kneer, Abraham Bernstein
CHI3