Cited by 103Ethics and Social Impacts of AIAI in Service InteractionsSocial Robot Interaction and HRI

Gender Bias in Chatbot Design

Feine, Jasper, Gnewuch, Ulrich, Morana, Stefan, Maedche, Alexander·2020·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-030-39540-7_6

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

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Claims other papers make about this one

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  • A gender bias has been identified in chatbot design.

    “a gender bias has been identified in chatbot design [33]”
  • Due to technological limitations and design choices, chatbots can perpetuate and reinforce existing biases in society, exclude or discriminate against some user groups, and over-represent or enshrine specific values.

    “due to technological limitations and design choices, they can be the means to perpetuate and even reinforce existing biases in society, exclude or discriminate against some user groups (e.g. [33, 51]) and over-represent or enshrine specific values”

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Authors
Feine, J., Gnewuch, U., Morana, S., & Maedche, A.
Affiliations
Karlsruhe Institute of Technology (DE)
Corresponding author
Feine, Jasper
Venue
Lecture Notes in Computer Science
Published
2020
Volume
11970
Pages
79-93
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W2999241551
Cited by
99
References
36
Citations by year
2026: 11 · 2025: 23 · 2024: 18 · 2023: 20 · 2022: 15 · 2021: 7 · 2020: 5
FWCI
12.19
Type
conference paper
Language
en
Open access
closed
Locations
  • Lecture notes in computer science (publishedVersion)
  • Lecture notes in computer science
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Keywords
gender bias, chatbot design, face recognition, customer service chatbots, gender stereotypes

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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