The DOI matches its Crossref record.
Hopes and fears for intelligent machines in fiction and reality
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Chiu, T. K. F., & Chai, C.-S. (2020) · Sustainability · doi:10.3390/su12145568 · Cited by 448
Cave, S., & Dihal, K. (2020) · Philosophy & Technology · doi:10.1007/s13347-020-00415-6 · Cited by 279
Self-citationPark, J., & Woo, S. E. (2022) · The Journal of Psychology · doi:10.1080/00223980.2021.2012109 · Cited by 243
Speeding up to keep up: exploring the use of AI in the research process
Chubb, J., Cowling, P., & Reed, D. (2022) · AI & SOCIETY · doi:10.1007/s00146-021-01259-0 · Cited by 218
Pataranutaporn, P., Liu, R., Finn, E., & Maes, P. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00720-7 · Cited by 192
Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022) · Psychology Learning & Teaching · doi:10.1177/14757257211037149 · Cited by 153
Artificial intelligence in fiction: between narratives and metaphors
Hermann, I. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01299-6 · Cited by 134
AI ‘Everywhere and Nowhere’: Addressing the AI Intelligibility Problem in Public Service Journalism
Jones, B., Jones, R., & Luger, E. (2022) · Digital Journalism · doi:10.1080/21670811.2022.2145328 · Cited by 84
The ethical challenges of artificial intelligence‐driven digital pathology
McKay, F., Williams, B. J., Prestwich, G., Bansal, D., Hallowell, N., & Treanor, D. (2022) · The Journal of Pathology: Clinical Research · doi:10.1002/cjp2.263 · Cited by 63
Understanding Public Perceptions of AI Conversational Agents: A Cross-Cultural Analysis
Liu, Z., Li, H., Chen, A., Zhang, R., & Lee, Y.-C. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642840 · Cited by 60
Public preferences for governing AI technology: Comparative evidence
Ehret, S. (2022) · Journal of European Public Policy · doi:10.1080/13501763.2022.2094988 · Cited by 28
“Good” and “Bad” Machine Agency in the Context of Human-AI Communication: The Case of ChatGPT
Brandtzaeg, P. B., You, Y., Wang, X., & Lao, Y. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-48057-7_1 · Cited by 25
Schaetz, N., & Schjøtt, A. (2026) · Digital Journalism · doi:10.1080/21670811.2024.2443163 · Cited by 17
Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction
Fernandes, D., Villa, S., Nicholls, S., Haavisto, O., Buschek, D., Schmidt, A., Kosch, T., Shen, C., & Welsch, R. (2024) · arXiv · doi:10.48550/arxiv.2409.16708 · Cited by 71
Metadata
- Authors
- Cave, S., & Dihal, K.
- Venue
- Nature Machine Intelligence
- Published
- 2019-02-11
- Volume
- 1
- Issue
- 2
- Pages
- 74-78
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W2914511770
- Cited by
- 355
- References
- 0
- FWCI
- 41.99
- Type
- journal article
- Language
- en
- Open access
- green
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.