Fair navigation planning: A resource for characterizing and designing fairness in mobile robots
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Optimizing a cumulative-cost approximation would lead to twice higher unfairness in this case.
“In [3] we show that optimizing a cumulative-cost approximation would lead to twice higher unfairness in this case.”
From Fair navigation planning: a humanitarian robot use case (2020) · cites this paper as [3]
The method used to generate these paths was based on evolutionary algorithms to compute Pareto-fronts, which do not offer optimality guarantees.
“Themethod we used to generate these paths is not a traditional one in robotics: it was based on evolutionary algorithms to compute Pareto-fronts [3], which are algorithms that do not offer optimality guarantees.”
From Fair navigation planning: a humanitarian robot use case (2020) · cites this paper as [3]
Cites
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
Why Ethics Matters for Autonomous Cars
Lin, P. (2015) · Autonomes Fahren · doi:10.1007/978-3-662-45854-9_4 · Cited by 189 · see citing works
Taati, B., Zhao, S., Ashraf, A. B., Asgarian, A., Browne, M. E., Prkachin, K. M., Mihailidis, A., & Hadjistavropoulos, T. (2019) · IEEE Access · doi:10.1109/access.2019.2900022 · Cited by 70 · see citing works
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Fair navigation planning: a humanitarian robot use case
Brandao, M. (2020) · arXiv · doi:10.48550/arxiv.2006.14479
“Themethod we used to generate these paths is not a traditional one in robotics: it was based on evolutionary algorithms to compute Pareto-fronts [3], which are algorithms that do not offer optimality guarantees.”
BackgroundMethodInfluentialSelf-citation
- Authors
- Brandão, M., Jirotka, M., Webb, H., & Luff, P.
- Affiliations
- University of Oxford (GB)
- King's College London (GB)
- Corresponding author
- Brandão, Martim
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 282
- Pages
- 103259
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W3011939999
- Cited by
- 35
- References
- 3
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 7 · 2023: 6 · 2022: 6 · 2021: 7 · 2020: 2
- FWCI
- 6.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1461 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Artificial Intelligence (publishedVersion)
- Oxford University Research Archive (ORA) (University of Oxford) (acceptedVersion, cc-by-nc-nd)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- fairness, autonomous systems, mobile robots, distributive justice, ethical design, rescue robots, indirect discrimination, equity-efficiency trade-off, privacy, responsible innovation
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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