Investigating Trust in Human-AI Collaboration for a Speech-Based Data Analytics Task

References

• Ahmed, A. M., & Salas, O. (2009). The relationship between behavioral and attitudinal trust: A cross-cultural study. Review of Social Economy, 67(4), 457–482. https://doi.org/10.1080/00346760902908625
   Google Scholar
• Alam, L., & Mueller, S. (2021). Examining the effect of explanation on satisfaction and trust in ai diagnostic systems. BMC Medical Informatics and Decision Making, 21(1), 178. https://doi.org/10.1186/s12911-021-01542-6
   PubMed Web of Science ®Google Scholar
• Alarcon, G. M., Lyons, J. B., Christensen, J. C., Klosterman, S. L., Bowers, M. A., Ryan, T. J., Jessup, S. A., & Wynne, K. T. (2018). The effect of propensity to trust and perceptions of trustworthiness on trust behaviors in dyads. Behavior Research Methods, 50(5), 1906–1920. https://doi.org/10.3758/s13428-017-0959-6
   PubMed Web of Science ®Google Scholar
• Aroyo, A. M., de Bruyne, J., Dheu, O., Fosch-Villaronga, E., Gudkov, A., Hoch, H., Jones, S., Lutz, C., Sætra, H., Solberg, M., … Tamò-Larrieux, A. others (2021). Overtrusting robots: Setting a research agenda to mitigate overtrust in automation. Paladyn, Journal of Behavioral Robotics, 12(1), 423–436. https://doi.org/10.1515/pjbr-2021-0029
   Google Scholar
• Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., & Müller, K.-R. (2010). How to explain individual classification decisions. The Journal of Machine Learning Research, 11, 1803–1831. https://dl.acm.org/doi/epdf/105555/1756006.1859912
   Web of Science ®Google Scholar
• Bansal, G., Nushi, B., Kamar, E., Weld, D. S., Lasecki, W. S., & Horvitz, E. (2019). Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff. In Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2429–2437. https://doi.org/10.1609/aaai.v33i01.33012429
   Google Scholar
• Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021). Does the whole exceed its parts? The effect of AI explanations on complementary team performance. In Proceedings of the 2021 CHI conference On Human Factors in Computing Systems (pp. 1–16). ACM. https://doi.org/10.1145/3411764.3445717
   Google Scholar
• Batrinca, L., Stratou, G., Shapiro, A., Morency, L.-P., & Scherer, S. (2013). Cicero-towards a multimodal virtual audience platform for public speaking training. In International Workshop on Intelligent Virtual Agents (pp. 116–128). Springer.
   Google Scholar
• Bawack, R. E., Wamba, S. F., & Carillo, K. D. A. (2021). Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis. International Journal of Information Management, 58, 102309. https://doi.org/10.1016/j.ijinfomgt.2021.102309
   Web of Science ®Google Scholar
• Blood, G. W., & Blood, I. M. (2007). Preliminary study of self-reported experience of physical aggression and bullying of boys who stutter: Relation to increased anxiety. Perceptual and Motor Skills, 104(3 Pt 2), 1060–1066. https://doi.org/10.2466/pms.104.4.1060-1066
   PubMed Web of Science ®Google Scholar
• Böckle, M., Yeboah-Antwi, K., & Kouris, I. (2021). Can you trust the black box? The effect of personality traits on trust in AI-enabled user interfaces. In International Conference on Human-Computer Interaction (pp. 3–20). Springer.
   Google Scholar
• Booth, B. M., Vrzakova, H., Mattingly, S. M., Martinez, G. J., Faust, L., & D’Mello, S. K. (2022). Toward robust stress prediction in the age of wearables: Modeling perceived stress in a longitudinal study with information workers. IEEE Transactions on Affective Computing, 13(4), 2201–2217. https://doi.org/10.1109/TAFFC.2022.3188006
   Web of Science ®Google Scholar
• Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions can reduce overreliance on ai in ai-assisted decision-making. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–21. https://doi.org/10.1145/3449287
   PubMedGoogle Scholar
• Chen, X. L., Ita Levitan, S., Levine, M., Mandic, M., & Hirschberg, J. (2020). Acoustic-prosodic and lexical cues to deception and trust: Deciphering how people detect lies. Transactions of the Association for Computational Linguistics, 8, 199–214. https://doi.org/10.1162/tacl_a_00311
   Google Scholar
• Cheng, H.-F., Wang, R., Zhang, Z., O’Connell, F., Gray, T., Harper, F. M., & Zhu, H. (2019). Explaining decision-making algorithms through UI: Strategies to help non-expert stakeholders. In Proceedings of the 2019 Chi Conference on Human Factors in Computing Systems (pp. 1–12). ACM.
   Google Scholar
• Chien, S.-Y., Sycara, K., Liu, J.-S., & Kumru, A. (2016). Relation between trust attitudes toward automation, hofstede’s cultural dimensions, and big five personality traits. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60(1), 841–845. https://doi.org/10.1177/1541931213601192
   Google Scholar
• Chollet, M., Wörtwein, T., Morency, L.-P., Scherer, S. (2016). A multimodal corpus for the assessment of public speaking ability and anxiety. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16) (pp. 488–495). ELRA.
   Google Scholar
• Chu, E., Roy, D., & Andreas, J. (2020). Are visual explanations useful? A case study in model-in-the-loop prediction. arXiv preprint arXiv:2007.12248
   Google Scholar
• D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., Hormozdiari, F., Houlsby, N., Hou, S., Jerfel, G., Karthikesalingam, A., Lucic, M., Ma, Y., McLean, C., Mincu, D., Mitani, A., … Beutel, A. (2022). Underspecification presents challenges for credibility in modern machine learning. The Journal of Machine Learning Research, 23(1), 10237–10297.
   Google Scholar
• de Brito Duarte, R., Correia, F., Arriaga, P., & Paiva, A. (2023). AI trust: Can explainable AI enhance warranted trust? Human Behavior and Emerging Technologies, 2023, 1–12. https://doi.org/10.1155/2023/4637678
   Google Scholar
• de Visser, E. J., Beatty, P. J., Estepp, J. R., Kohn, S., Abubshait, A., Fedota, J. R., & McDonald, C. G. (2018). Learning from the slips of others: Neural correlates of trust in automated agents. Frontiers in Human Neuroscience, 12, 309. https://doi.org/10.3389/fnhum.2018.00309
   PubMed Web of Science ®Google Scholar
• Dong, S.-Y., Kim, B.-K., Lee, K., & Lee, S.-Y. (2015). A preliminary study on human trust measurements by EEG for human-machine interactions. In Proceedings of the 3rd International Conference on Human-Agent Interaction (pp. 265–268). ACM. https://doi.org/10.1145/2814940.2814993
   Google Scholar
• Drnec, K., Marathe, A. R., Lukos, J. R., & Metcalfe, J. S. (2016). From trust in automation to decision neuroscience: Applying cognitive neuroscience methods to understand and improve interaction decisions involved in human automation interaction. Frontiers in Human Neuroscience, 10, 290. https://doi.org/10.3389/fnhum.2016.00290
   PubMed Web of Science ®Google Scholar
• Ebert, I. D., Steffens, M. C., Von Stülpnagel, R., & Jelenec, P. (2009). How to like yourself better, or chocolate less: Changing implicit attitudes with one IAT task. Journal of Experimental Social Psychology, 45(5), 1098–1104. https://doi.org/10.1016/j.jesp.2009.06.008
   Web of Science ®Google Scholar
• Ezer, N., Bruni, S., Cai, Y., Hepenstal, S. J., Miller, C. A., & Schmorrow, D. D. (2019). Trust engineering for human-AI teams. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63(1), 322–326. https://doi.org/10.1177/1071181319631264
   Google Scholar
• Fan, X., Oh, S., McNeese, M., Yen, J., Cuevas, H., Strater, L., & Endsley, M. R. (2008). The influence of agent reliability on trust in human-agent collaboration. In Proceedings of the 15th European Conference on Cognitive Ergonomics: The Ergonomics of Cool Interaction (pp. 1–8). ACM. https://doi.org/10.1145/1473018.1473028
   Google Scholar
• Geiskkovitch, D. Y., Thiessen, R., Young, J. E., & Glenwright, M. R. (2019). What? That’s not a chair!: How robot informational errors affect children’s trust towards robots. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 48–56). ACM. https://doi.org/10.1109/HRI.2019.8673024
   Google Scholar
• Gillespie, N., Lockey, S., Curtis, C., Pool, J., & Akbari, A. (2023). Trust in artificial intelligence: A global study. The University of Queensland and KPMG Australia. https://doi.org/10.14264/00d3c94
   Google Scholar
• Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
   Web of Science ®Google Scholar
• Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(Mar), 1157–1182.
   Google Scholar
• Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y., De Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53(5), 517–527. https://doi.org/10.1177/0018720811417254
   PubMed Web of Science ®Google Scholar
• Hauke, J., & Kossowski, T. (2011). Comparison of values of Pearson’s and Spearman’s correlation coefficient on the same sets of data. Quaestiones Geographicae, 30(2), 87–93. https://doi.org/10.2478/v10117-011-0021-1
   Google Scholar
• Hemmer, P., Schemmer, M., Vössing, M., & Kühl, N. (2021). Human-ai complementarity in hybrid intelligence systems: A structured literature review. PACIS, 78.
   Google Scholar
• Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57(3), 407–434. https://doi.org/10.1177/0018720814547570
   PubMed Web of Science ®Google Scholar
• Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
   Web of Science ®Google Scholar
• Jiang, X., & Pell, M. D. (2017). The sound of confidence and doubt. Speech Communication, 88, 106–126. https://doi.org/10.1016/j.specom.2017.01.011
   Web of Science ®Google Scholar
• John, O. P., & Srivastava, S. (1999). The big-five trait taxonomy: History, measurement, and theoretical perspectives (Vol. 2). University of California Berkeley.
   Google Scholar
• Kaindl, H., & Svetinovic, D. (2019). Avoiding undertrust and overtrust. Refsq Workshops.
   Google Scholar
• Kaplan, A. D., Kessler, T. T., Brill, J. C., & Hancock, P. (2023). Trust in artificial intelligence: Meta-analytic findings. Human Factors, 65(2), 337–359. https://doi.org/10.1177/00187208211013988
   PubMed Web of Science ®Google Scholar
• Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F. (2018). Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV). In International Conference on Machine Learning (pp. 2668–2677). ACM.
   Google Scholar
• Kraus, J., Scholz, D., & Baumann, M. (2020). What’s driving me? Exploration and validation of a hierarchical personality model for trust in automated driving. Human Factors, 63(6), 1076–1105. https://doi.org/10.1177/0018720820922653
   PubMed Web of Science ®Google Scholar
• Kulms, P., & Kopp, S. (2019). More human-likeness, more trust? The effect of anthropomorphism on self-reported and behavioral trust in continued and interdependent human-agent cooperation. In Proceedings of mensch und computer 2019 (pp. 31–42). ACM.
   Google Scholar
• Lai, V., & Tan, C. (2019). On human predictions with explanations and predictions of machine learning models: A case study on deception detection. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 29–38). ACM.
   Google Scholar
• Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
   PubMed Web of Science ®Google Scholar
• Levitan, S. I., An, G., Wang, M., Mendels, G., Hirschberg, J., Levine, M., & Rosenberg, A. (2015). Cross-cultural production and detection of deception from speech. In Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection (pp. 1–8). ACM. https://doi.org/10.1145/2823465.2823468
   Google Scholar
• Lou, Y., Caruana, R., & Gehrke, J. (2012). Intelligible models for classification and regression. In Proceedings of the 18th ACM Sigkdd International Conference on Knowledge Discovery and Data Mining (pp. 150–158). ACM. https://doi.org/10.1145/2339530.2339556
   Google Scholar
• Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4768–4777.
   Google Scholar
• Madhavan, P., Wiegmann, D. A., & Lacson, F. C. (2006). Automation failures on tasks easily performed by operators undermine trust in automated aids. Human Factors, 48(2), 241–256. https://doi.org/10.1518/001872006777724408
   PubMed Web of Science ®Google Scholar
• Merritt, S. M. (2011). Affective processes in human–automation interactions. Human Factors, 53(4), 356–370. https://doi.org/10.1177/0018720811411912
   PubMed Web of Science ®Google Scholar
• Merritt, S. M., Heimbaugh, H., LaChapell, J., & Lee, D. (2013). I trust it, but I don’t know why: Effects of implicit attitudes toward automation on trust in an automated system. Human Factors, 55(3), 520–534. https://doi.org/10.1177/0018720812465081
   PubMed Web of Science ®Google Scholar
• Merritt, S. M., & Ilgen, D. R. (2008). Not all trust is created equal: Dispositional and history-based trust in human-automation interactions. Human Factors, 50(2), 194–210. https://doi.org/10.1518/001872008X288574
   PubMed Web of Science ®Google Scholar
• Monarth, H., & Kase, L. (2007). Confident speaker. McGraw-Hill Professional Publishing.
   Google Scholar
• Müller, L., Mattke, J., Maier, C., Weitzel, T., & Graser, H. (2019). Chatbot acceptance: A latent profile analysis on individuals’ trust in conversational agents. In Proceedings of the 2019 on Computers and People Research Conference (pp. 35–42). ACM.
   Google Scholar
• Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2021). Explainable recommendation: When design meets trust calibration. World Wide Web, 24(5), 1857–1884. https://doi.org/10.1007/s11280-021-00916-0
   PubMedGoogle Scholar
• Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2023). How the different explanation classes impact trust calibration: The case of clinical decision support systems. International Journal of Human-Computer Studies, 169, 102941. https://doi.org/10.1016/j.ijhcs.2022.102941
   Web of Science ®Google Scholar
• Nori, H., Jenkins, S., Koch, P., & Caruana, R. (2019). Interpretml: A unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223
   Google Scholar
• Nourani, M., King, J., & Ragan, E. (2020). The role of domain expertise in user trust and the impact of first impressions with intelligent systems. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1), 112–121. https://doi.org/10.1609/hcomp.v8i1.7469
   Google Scholar
• Okamura, K., & Yamada, S. (2018). Adaptive trust calibration for supervised autonomous vehicles. In Adjunct proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 92–97). ACM. https://doi.org/10.1145/3239092.3265948
   Google Scholar
• Ollendick, T. H., & Hirshfeld-Becker, D. R. (2002). The developmental psychopathology of social anxiety disorder. Biological Psychiatry, 51(1), 44–58. https://doi.org/10.1016/s0006-3223(01)01305-1
   PubMed Web of Science ®Google Scholar
• Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
   Web of Science ®Google Scholar
• Payne, B. K., Govorun, O., & Arbuckle, N. L. (2008). Automatic attitudes and alcohol: Does implicit liking predict drinking? Cognition & Emotion, 22(2), 238–271. https://doi.org/10.1080/02699930701357394
   Web of Science ®Google Scholar
• Phillips-Wren, G. (2012). Ai tools in decision making support systems: A review. International Journal on Artificial Intelligence Tools, 21(02), 1240005. https://doi.org/10.1142/S0218213012400052
   Google Scholar
• Pop, V. L., Shrewsbury, A., & Durso, F. T. (2015). Individual differences in the calibration of trust in automation. Human Factors, 57(4), 545–556. https://doi.org/10.1177/0018720814564422
   PubMed Web of Science ®Google Scholar
• Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021). Manipulating and measuring model interpretability. In Proceedings of the 2021 Chi Conference on Human Factors in Computing Systems (pp. 1–52). ACM. https://doi.org/10.1145/3411764.3445315
   Google Scholar
• Ragni, M., Rudenko, A., Kuhnert, B., & Arras, K. O. (2016). Errare humanum est: Erroneous robots in human-robot interaction. In 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 501–506). IEEE. https://doi.org/10.1109/ROMAN.2016.7745164
   Google Scholar
• Rebekić, A., Lončarić, Z., Petrović, S., & Marić, S. (2015). Pearson’s or spearman’s correlation coefficient-which one to use? Poljoprivreda, 21(2), 47–54. https://doi.org/10.18047/poljo.21.2.8
   Google Scholar
• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). ACM.
   Google Scholar
• Sanchez, J., Rogers, W. A., Fisk, A. D., & Rovira, E. (2014). Understanding reliance on automation: Effects of error type, error distribution, age and experience. Theoretical Issues in Ergonomics Science, 15(2), 134–160. https://doi.org/10.1080/1463922X.2011.611269
   PubMedGoogle Scholar
• Schaefer, K. (2013). The perception and measurement of human-robot trust [Unpublished doctoral dissertation]. University of Central Florida.
   Google Scholar
• Schaefer, K. E., Billings, D. R., Szalma, J. L., Adams, J. K., Sanders, T. L., Chen, J. Y., & Hancock, P. A. (2014). A meta-analysis of factors influencing the development of trust in automation: Implications for human-robot interaction. Army Research Lab Aberdeen Proving Ground Md Human Research and Engineering.
   Google Scholar
• Sharan, N. N., & Romano, D. M. (2020). The effects of personality and locus of control on trust in humans versus artificial intelligence. Heliyon, 6(8), e04572. https://doi.org/10.1016/j.heliyon.2020.e04572
   PubMed Web of Science ®Google Scholar
• Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47–53.
   Google Scholar
• Sofianos, A. (2022). Self-reported & revealed trust: Experimental evidence. Journal of Economic Psychology, 88, 102451. https://doi.org/10.1016/j.joep.2021.102451
   Web of Science ®Google Scholar
• Tutul, A. A., Nirjhar, E. H., & Chaspari, T. (2021). Investigating trust in human-machine learning collaboration: A pilot study on estimating public anxiety from speech. In Proceedings of the 2021 international conference on multimodal interaction (pp. 288–296). ACM. https://doi.org/10.1145/3462244.3479926
   Google Scholar
• Ullrich, D., Butz, A., & Diefenbach, S. (2021). The development of overtrust: An empirical simulation and psychological analysis in the context of human–robot interaction. Frontiers in Robotics and AI, 8, 554578. https://doi.org/10.3389/frobt.2021.554578
   PubMed Web of Science ®Google Scholar
• Yadav, M., Sakib, M. N., Nirjhar, E. H., Feng, K., Behzadan, A., & Chaspari, T. (2020). Exploring individual differences of public speaking anxiety in real-life and virtual presentations. IEEE Transactions on Affective Computing, 13(3), 1168–1182. https://doi.org/10.1109/taffc.2020.3048299
   Web of Science ®Google Scholar
• Yang, F., Huang, Z., Scholtz, J., Arendt, D. L. (2020). How do visual explanations foster end users’ appropriate trust in machine learning? In Proceedings of the 25th International Conference on Intelligent User Interfaces (pp. 189–201). ACM.
   Google Scholar
• Zhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020). Effect of confidence and explanation on accuracy and trust calibration in ai-assisted decision making. In [Paper presentation]. (p. 295–305). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3351095.3372852
   Google Scholar