Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 84

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty

Jiang, Jinglu, Kahai, Surinder, Yang, Ming·2022·International Journal of Human-Computer Studies

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 · 10.1016/j.ijhcs.2022.102839

Cites

37 of 37 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

Show all 37 references

Research works printed but not linked: 60

  1. Abedin, B., 2021. Managing the tension between opposing effects of explainability of artificial intelligence–A contingency theory perspective. Internet Research. · DOI
  2. Anjomshoae, 2019, Explainable agents and robots–Results from a systematic literature review robotics track, 1078
  3. Asan, 2020, Artificial intelligence and human trust in healthcare–Focus on clinicians, J. Med. Internet Res., 22, e15154, 10.2196/15154 · DOI · Paper page
  4. Bedue, 2021, Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption, J. Enterp. Inf. Manag.
  5. Benbasat, 2005, Trust in and adoption of online recommendation agents, J. Assoc. Inf. Syst., 6, 4
  6. Breakwell, 2020, Mistrust, uncertainty and health risks, Contemp. Soc. Sci., 15, 504, 10.1080/21582041.2020.1804070 · DOI
  7. Canty, A., Ripley, B., 2021. boot–Bootstrap R (S-Plus) functions, R package version, pp. 3–28.
  8. Cheng, 2020, There is hope after all–Quantifying opinion and trustworthiness in neural networks, Front. artif. intell., 3, 54, 10.3389/frai.2020.00054 · DOI
  9. Chromik, M., Eiband, M., Völkel, S.T., Buschek, D., 2019. Dark patterns of explainability, transparency, and user control for intelligent systems, IUI workshops.
  10. Confalonieri, 2021, A historical perspective of explainable Artificial Intelligence, Wiley Interdiscip. Rev., 11
  11. Cunneen, 2020, Artificial intelligence assistants and risk–Framing a connectivity risk narrative, AI Soc., 35, 625, 10.1007/s00146-019-00916-9 · DOI
  12. Dalal, 2010, What types of advice do decision-makers prefer?, Organ. Behav. Hum. Decis. Process., 112, 11, 10.1016/j.obhdp.2009.11.007 · DOI
  13. Davis, 1989, User acceptance of computer technology–A comparison of two theoretical models, Manag. Sci., 35, 982, 10.1287/mnsc.35.8.982 · DOI · Paper page
  14. Dazeley, 2021, Levels of explainable artificial intelligence for human-aligned conversational explanations, Artif. Intell., 299
  15. Felzmann, 2019, Robots and transparency–The multiple dimensions of transparency in the context of robot technologies, IEEE Robot. Autom. Mag., 26, 71, 10.1109/MRA.2019.2904644 · DOI
  16. Fischer, 2011, Selective exposure, decision uncertainty, and cognitive economy–A new theoretical perspective on confirmatory information search, Soc. Personal. Psychol. Compass, 5, 751, 10.1111/j.1751-9004.2011.00386.x · DOI
  17. Forman, R., Jit, M., Mossialos, E., 2021. Divergent vaccination policies could fuel mistrust and hesitancy. The Lancet. · doi:10.1016/s0140-6736(21)01106-5
  18. Guidotti, 2018, A survey of methods for explaining black box models, ACM Comput. Surv (CSUR), 51, 1, 10.1145/3236009 · DOI · Paper page
  19. Ha, 2021, Examining the effects of power status of an explainable artificial intelligence system on users' perceptions, Behav. Inf. Technol.
  20. Hayes, 2007, Using heteroskedasticity-consistent standard error estimators in OLS regression–An introduction and software implementation, Behav. Res. Methods, 39, 709, 10.3758/BF03192961 · DOI · Paper page
  21. Holzinger, 2021, Toward human–AI interfaces to support explainability and causability in medical AI, Computer (Long Beach Calif), 54, 78
  22. Jøsang, 2007, A survey of trust and reputation systems for online service provision, Decis. Support Syst., 43, 618, 10.1016/j.dss.2005.05.019 · DOI · Paper page
  23. Kim, 2020, Improved explanatory efficacy on human affect and workload through interactive process in artificial intelligence, IEEE Access, 8, 189013, 10.1109/ACCESS.2020.3032056 · DOI
  24. Knijnenburg, 2012, Explaining the user experience of recommender systems, User Model User-Adapt. Interact., 22, 441, 10.1007/s11257-011-9118-4 · DOI · Paper page
  25. Köhler, 2011, Consumer acceptance of recommendations by interactive decision aids–The joint role of temporal distance and concrete versus abstract communications, J. Manag. Inf. Syst., 27, 231, 10.2753/MIS0742-1222270408 · DOI
  26. Langer, 2021, What do we want from Explainable Artificial Intelligence (XAI)? - A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research, Artif. Intell., 296
  27. Liang, 2006, Personalized content recommendation and user satisfaction–Theoretical synthesis and empirical findings, J. Manag. Inf. Syst., 23, 45, 10.2753/MIS0742-1222230303 · DOI · Paper page
  28. Mantelero, 2018, AI and Big Data–A blueprint for a human rights, social and ethical impact assessment, Comput. Law Secur. Rev., 34, 754, 10.1016/j.clsr.2018.05.017 · DOI · Paper page
  29. McFadden, 1974, The measurement of urban travel demand, J. Public Econ., 3, 303, 10.1016/0047-2727(74)90003-6 · DOI · Paper page
  30. Mehmood, 2016, Self-medication–An emerging trend, Br. J. Pharm. Res., 14, 1, 10.9734/BJPR/2016/30333 · doi:10.9734/bjpr/2016/30333
  31. Meske, 2021, Explainable Artificial Intelligence–Objectives, stakeholders, and future research opportunities, Inf. Syst. Manag.
  32. Nagler, 2014, Adverse outcomes associated with media exposure to contradictory nutrition messages, J. Health Commun., 19, 24, 10.1080/10810730.2013.798384 · DOI · Paper page
  33. Oberkampf, 2004, Challenge problems–Uncertainty in system response given uncertain parameters, Reliab. Eng. Syst. Saf., 85, 11, 10.1016/j.ress.2004.03.002 · DOI
  34. Oliver, 1993, Cognitive, affective, and attribute bases of the satisfaction response, J. Consum. Res., 20, 418, 10.1086/209358 · DOI · Paper page
  35. Paez, 2019, The pragmatic turn in Explainable Artificial Intelligence (XAI), Minds Mach., 29, 441, 10.1007/s11023-019-09502-w · DOI
  36. Preece, 2018, Asking "Why' in AI–Explainability of intelligent systems-perspectives and challenges, Intell. Syst. Account. Finance Manag., 25, 63
  37. Rai, 2020, Explainable AI–From black box to glass box, J. Acad. Mark. Sci., 48, 137, 10.1007/s11747-019-00710-5 · DOI · Paper page
  38. Riveiro, 2021, That's (not) the output I expected!" On the role of end user expectations in creating explanations of AI systems, Artif. Intell., 298, 10.1016/j.artint.2021.103507 · DOI
  39. Robbins, 2019, A misdirected principle with a catch–Explicability for AI, Minds Mach., 29, 495, 10.1007/s11023-019-09509-3 · DOI
  40. Saffiotti, 1987, An AI view of the treatment of uncertainty, Knowl. Eng. Rev., 2, 75, 10.1017/S0269888900000795 · DOI
  41. Sah, 2013, Cheap talk and credibility–The consequences of confidence and accuracy on advisor credibility and persuasiveness, Organ. Behav. Hum. Decis. Process., 121, 246, 10.1016/j.obhdp.2013.02.001 · DOI
  42. Schmidt, 2020, Transparency and trust in artificial intelligence systems, J. Decis. Syst., 29, 260, 10.1080/12460125.2020.1819094 · DOI · Paper page
  43. Schul, 2015, Influences of Distrust (and Trust) on Decision Making, Soc. Cognit., 33, 414, 10.1521/soco.2015.33.5.414 · DOI
  44. Shaban-Nejad, 2021, Explainable AI–Towards fairness, accountability, transparency and trust in healthcare, IEEE J. Biomed. Health Inform., 25, 2374, 10.1109/JBHI.2021.3088832 · DOI · Paper page
  45. Shafto, 2012, Epistemic trust–Modeling children's reasoning about others' knowledge and intent, Dev. Sci., 15, 436, 10.1111/j.1467-7687.2012.01135.x · DOI
  46. Shibl, 2013, Factors influencing decision support system acceptance, Decis. Support Syst., 54, 953, 10.1016/j.dss.2012.09.018 · DOI
  47. Shin, 2021, The effects of explainability and causability on perception, trust, and acceptance–Implications for explainable AI, Int. J. Hum. Comput. Stud., 146, 10.1016/j.ijhcs.2020.102551 · DOI · Paper page
  48. Su, 2008, The effect of expertise on consumers' satisfaction with the use of interactive recommendation agents, Psychol. Mark., 25, 859, 10.1002/mar.20244 · DOI
  49. Sugumar, 2021, Do I desire chatbots to be like humans? Exploring factors for adoption of chatbots for financial services, J. Int. Technol. Inf. Manag., 30, 38
  50. Tenney, 2008, The benefits of knowing what you know (and what you don't)–How calibration affects credibility, J. Exp. Soc. Psychol., 44, 1368, 10.1016/j.jesp.2008.04.006 · DOI
  51. Tomsett, 2020, Rapid trust calibration through interpretable and uncertainty-aware, AI. Patterns, 1
  52. van der Waa, 2021, Evaluating XAI–A comparison of rule-based and example-based explanations, Artif. Intell., 291
  53. van der Waa, 2020, Interpretable confidence measures for decision support systems, Int. J. Hum. Comput. Stud., 144
  54. Van Swol, 2009, The effects of confidence and advisor motives on advice utilization, Commun. Res., 36, 857, 10.1177/0093650209346803 · DOI
  55. Venkatesh, 2003, User acceptance of information technology–Toward a unified view, MIS Q., 425, 10.2307/30036540 · DOI · Paper page
  56. Wong, 2019, Characterizing the impact of health literacy, computer ability, patient demographics, and portal usage on patient satisfaction with a patient portal, JAMIA Open, 2, 456, 10.1093/jamiaopen/ooz058 · DOI
  57. Xiao, 2007, E-commerce product recommendation agents–Use, characteristics, and impact, MIS Q., 137, 10.2307/25148784 · DOI · Paper page
  58. Xie, 2021, Explanation of machine-learning solutions in air-traffic management, Aerospace, 8, 10.3390/aerospace8080224 · DOI
  59. Zhang, 2014, Impact of health literacy on medication adherence–A systematic review and meta-analysis, Ann. Pharmacother., 48, 741, 10.1177/1060028014526562 · DOI · Paper page
  60. Zucco, 2018, Internet and social media use for antibiotic-related information seeking: Findings from a survey among adult population in Italy, Int. J. Med. Inform., 111, 131, 10.1016/j.ijmedinf.2017.12.005 · DOI

Cited by

Metadata

Authors
Jiang, J., Kahai, S., & Yang, M.
Venue
International Journal of Human-Computer Studies
Publisher
Elsevier BV
Published
2022-09
Volume
165
Pages
102839
ISSN
1071-5819
Cited by
84
References
37
Language
en
Funders
National Natural Science Foundation of China (71874215, 72172168)

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.