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Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title “Garbage In, Garbage Out”: Mitigating Human Biases in Data Entry by Means of Artificial Intelligence
Organization Unit
Authors
  • Sven Eckhardt
  • Merlin Knaeble
  • Andreas Bucher
  • Dario Staehelin
  • Mateusz Dolata
  • Doris Agotai
  • Gerhard Schwabe
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
ISBN 978-3-031-42286-7
ISSN 0302-9743
Page Range 27 - 48
Event Title INTERACT 2023 19th IFIP TC13 International Conference
Event Type conference
Event Location York, UK
Event Start Date August 28 - 2023
Event End Date September 1 - 2023
Series Name Lecture Notes in Computer Science
Number 14144
Publisher Springer
Abstract Text Current HCI research often focuses on mitigating algorithmic biases. While such algorithmic fairness during model training is worthwhile, we see fit to mitigate human cognitive biases earlier, namely during data entry. We developed a conversational agent with voice-based data entry and visualization to support financial consultations, which are human-human settings with information asymmetries. In a pre-study, we reveal data-entry biases in advisors by a quantitative analysis of 5 advisors consulting 15 clients in total. Our main study evaluates the conversational agent with 12 advisors and 24 clients. A thematic analysis of interviews shows that advisors introduce biases by “feeling” and “forgetting” data. Additionally, the conversational agent makes financial consultations more transparent and automates data entry. These findings may be transferred to various dyads, such as doctor visits. Finally, we stress that AI not only poses a risk of becoming a mirror of human biases but also has the potential to intervene in the early stages of data entry.
Digital Object Identifier 10.1007/978-3-031-42286-7_2
Other Identification Number merlin-id:24108
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