Robust Intelligence and Trust in Autonomous Systems
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Mittu et al.'s study revealed that when an example supported the decision of a self-driving car abruptly changing lanes, passengers were more comfortable.
“Mittu et al. [55].study revealed that when an example supported the decision of a self-driving car abruptly changing lanes, passengers were more comfortable.”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [55]
In a 2016 Tesla autopilot incident, the vehicle was unable to discern between the white side of the truck and a brightly lit sky, preventing the need to brake.
“According to the National Highway Traffic Safety Administration, the vehicle was unable to discern between the white side of the truck and a brightly lit sky, preventing the need to brake that may have caused the passengers to suffer significant injuries [55].”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [55]
Cites
Merritt, S. M., & Ilgen, D. R. (2008) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872008x288574 · Cited by 527 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,062 · see citing works
“According to the National Highway Traffic Safety Administration, the vehicle was unable to discern between the white side of the truck and a brightly lit sky, preventing the need to brake that may have caused the passengers to suffer significant injuries [55].”
BackgroundA Survey of Data-driven and Knowledge-aware eXplainable AI
Li, X.-H., Cao, C. C., Shi, Y., Bai, W., Gao, H., Qiu, L., Wang, C., Gao, Y., Zhang, S., Xue, X., et al. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2983930 · Cited by 224 · see citing works
- Authors
- Mittu, R., Sofge, D., Wagner, A. R., & Lawless, W. F.
- Affiliations
- United States Naval Research Laboratory (US)
- Georgia Tech Research Institute (US)
- Paine College (US)
- Venue
- Springer eBooks
- Publisher
- Springer US
- Published
- 2016
- OpenAlex
- W2495843840
- Cited by
- 78
- References
- 1
- Citations by year
- 2026: 1 · 2025: 13 · 2024: 13 · 2023: 10 · 2022: 6 · 2021: 10 · 2020: 13 · 2019: 5 · 2018: 4 · 2017: 2 · 2016: 1
- FWCI
- 4.26
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- DIAL (Catholic University of Leuven) (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Keywords
- trust, autonomous systems, human-automation interaction
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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