Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 48

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making

Zheng, Chengbo, Wu, Yuheng, Shi, Chuhan, Ma, Shuai, Luo, Jiehui, Ma, Xiaojuan·2023·Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3544548.3581131
Open this paper in CoChat — free account

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3544548.3581131

Cites

27 of 27 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

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

Cites

Research works printed but not linked: 68

  1. 10.1609/aaai.v35i13.17359 · DOI
  2. 10.1145/3377325.3377498 · DOI · Paper page
  3. 10.1145/3449287 · doi:10.1145/3449287 · Paper page
  4. Hao-Fei Cheng, Logan Stapleton, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, and Haiyi Zhu. 2022. How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions. In CHI Conference on Human Factors in Computing Systems. 1–22.
  5. Jared R Curhan, Jennifer R Overbeck, Yeri Cho, Teng Zhang, and Yu Yang. 2022. Silence is golden: Extended silence, deliberative mindset, and value creation in negotiation.Journal of applied psychology 107, 1 (2022), 78.
  6. Gerardine DeSanctis and R Brent Gallupe. 1987. A foundation for the study of group decision support systems. Management science 33, 5 (1987), 589–609.
  7. Muzaffer Eroğlu and Meltem Karatepe Kaya. 2022. Impact of Artificial Intelligence on Corporate Board Diversity Policies and Regulations. European Business Organization Law Review(2022), 1–32.
  8. 10.1145/358916.359001 · DOI
  9. 10.1145/633292.633378 · doi:10.1145/633292.633378
  10. 10.1109/ICASSP.2013.6638947 · DOI · Paper page
  11. Greg Guest Kathleen M MacQueen and Emily E Namey. 2011. Applied thematic analysis. sage publications. · DOI · Paper page
  12. 10.1145/3236009 · DOI · Paper page
  13. 10.5555/646247.685022 · doi:10.5555/646247.685022
  14. 10.1111/j.1440-172X.2012.02017.x · DOI · Paper page
  15. Nicholas Hertz and Eva Wiese. 2018. Under pressure: Examining social conformity with computer and robot groups. Human factors 60, 8 (2018), 1207–1218.
  16. 10.1007/s10997-020-09519-9 · DOI
  17. James L Hilton and William Von Hippel. 1996. Stereotypes. Annual review of psychology 47, 1 (1996), 237–271.
  18. Randy Y Hirokawa and Marshall Scott Poole. 1996. Communication and group decision making. Sage Publications.
  19. 10.24251/HICSS.2022.030 · DOI
  20. 10.1145/3173574.3173640 · DOI
  21. 10.1145/3411764.3445424 · DOI
  22. Floyd Hunter. 2017. Community power structure: A study of decision makers. UNC Press Books.
  23. Holtzblatt Karen and Jones Sandra. 2017. Contextual inquiry: A participatory technique for system design. In Participatory design. CRC Press, 177–210.
  24. 10.1145/3512902 · DOI
  25. Saif Khairat, David Marc, William Crosby, Ali Al Sanousi, 2018. Reasons for physicians not adopting clinical decision support systems: critical analysis. JMIR medical informatics 6, 2 (2018), e8912.
  26. Eugene C Kim. 2007. YouTube: Testing the safe harbors of digital copyright law. S. Cal. Interdisc. LJ 17(2007), 139.
  27. 10.1145/3313831.3376785 · DOI · Paper page
  28. 10.1145/3449161 · DOI
  29. Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. Human decisions and machine predictions. The quarterly journal of economics 133, 1 (2018), 237–293.
  30. 10.18653/v1/2022.naacl-main.106 · DOI
  31. 10.1145/3411764.3445659 · DOI · Paper page
  32. Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 (2017).
  33. 10.1145/3491102.3517527 · DOI · Paper page
  34. 10.1561/116.00000050 · DOI · Paper page
  35. 10.1145/3313831.3376590 · DOI · Paper page
  36. Q Vera Liao and Kush R Varshney. 2021. Human-centered explainable ai (xai): From algorithms to user experiences. arXiv preprint arXiv:2110.10790(2021). · arXiv
  37. 10.1016/j.jm.2003.02.002 · DOI · Paper page
  38. Harold A Linstone, Murray Turoff, 1975. The delphi method. Addison-Wesley Reading, MA.
  39. Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 4765–4774. http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf
  40. Scott M Lundberg, Bala Nair, Monica S Vavilala, Mayumi Horibe, Michael J Eisses, Trevor Adams, David E Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kim, 2018. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature biomedical engineering 2, 10 (2018), 749–760.
  41. Shuai Ma, Mingfei Sun, and Xiaojuan Ma. 2022. Modeling Adaptive Expression of Robot Learning Engagement and Exploring its Effects on Human Teachers. ACM Transactions on Computer-Human Interaction (2022).
  42. 10.1145/3290605.3300701 · DOI · Paper page
  43. 10.1145/3491102.3517482 · DOI
  44. 10.1145/3411764.3445355 · DOI
  45. Yaoli Mao Dakuo Wang Michael Muller Kush R Varshney Ioana Baldini Casey Dugan and Aleksandra Mojsilović. 2019. How data scientistswork together with domain experts in scientific collaborations: To find the right answer or to ask the right question?Proceedings of the ACM on Human-Computer Interaction 3 GROUP(2019) 1–23. · DOI
  46. Andrew Monk. 2003. Common ground in electronically mediated communication: Clark’s theory of language use. HCI models, theories, and frameworks: Toward a multidisciplinary science (2003), 265–289.
  47. 10.1016/j.chb.2017.02.067 · DOI · Paper page
  48. 10.1109/ISTAS48451.2019.8937920 · DOI
  49. 10.1145/153571.255960 · DOI · Paper page
  50. 10.1145/3290605.3300328 · DOI
  51. Martin Petrin. 2019. Corporate Management in the Age of AI. Colum. Bus. L. Rev. (2019) 965. · DOI
  52. 10.1145/3411764.3445406 · DOI
  53. Margaret Potter, Sandy Gordon, and Peter Hamer. 2004. The nominal group technique: a useful consensus methodology in physiotherapy research. NZ Journal of Physiotherapy 32, 3 (2004), 126–130.
  54. 10.5898/JHRI.1.1.Riek · DOI · Paper page
  55. 10.1145/3498326 · DOI
  56. 10.1007/BF00055564 · DOI · Paper page
  57. 10.1145/3492832 · DOI
  58. 10.1145/3173574.3173965 · DOI
  59. Chuhan Shi, Fei Nie, Yicheng Hu, Yige Xu, Lei Chen, Xiaojuan Ma, and Qiong Luo. 2022. MedChemLens: An Interactive Visual Tool to Support Direction Selection in Interdisciplinary Experimental Research of Medicinal Chemistry. IEEE Transactions on Visualization and Computer Graphics 29, 1(2022), 63–73.
  60. Ben Shneiderman. 2022. Human-Centered AI. Oxford University Press. · DOI · Paper page
  61. 10.18653/v1/D16-1193 · DOI · Paper page
  62. 10.1007/s40685-020-00133-x · DOI
  63. 10.1016/j.chb.2018.09.009 · DOI · Paper page
  64. 10.1145/3489465 · DOI
  65. 10.1145/3411764.3445526 · DOI
  66. 10.1145/3064663.3064712 · DOI
  67. 10.1007/978-3-319-09767-1_62 · DOI
  68. 10.1145/3491102.3517615 · DOI

Cited by

See all 48

Metadata

Authors
Zheng, C., Wu, Y., Shi, C., Ma, S., Luo, J., & Ma, X.
Affiliations
  • Hong Kong University of Science and Technology, China
  • Chinese University of Hong Kong, Shenzhen, China
  • The Hong Kong University of Science and Technology, China
  • Hong Kong University of Science and Technology, Hong Kong
ORCID
Venue
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2023-04-19
Pages
1-19
DOI
10.1145/3544548.3581131
Cited by
48
References
27
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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.