Calibrating Human-AI Collaboration: Impact of Risk, Ambiguity and Transparency on Algorithmic Bias
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Transparency of data, methods, and outcomes can support collaborative algorithmic decision-making.
“In addition, transparency of data, methods, and outcomes can support collaborative algorithmic decision-making processes [61,110].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [110]
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References 61 · Cited by 26
- Authors
- Schmidt, P., & Biessmann, F.
- Affiliations
- Amazon (Germany) (DE)
- Einstein Center Digital Future (DE)
- Corresponding author
- Schmidt, Philipp
- ORCID
- Biessmann, Felix
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Volume
- LNCS-12279
- Pages
- 431-449
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3041078907
- Cited by
- 26
- References
- 61
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 5 · 2023: 6 · 2022: 4 · 2021: 2
- FWCI
- 1.14
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- algorithmic bias, human-AI collaboration, transparency, risk aversion, personality traits, ambiguity, trust in AI, model interpretability, human-AI interaction, responsible AI
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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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