skip to main content
research-article

Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process

Published: 08 November 2024 Publication History

Abstract

AI is anticipated to enhance human decision-making in high-stakes domains like aviation, but adoption is often hindered by challenges such as inappropriate reliance and poor alignment with users' decision-making. Recent research suggests that a core underlying issue is the recommendation-centric design of many AI systems, i.e., they give end-to-end recommendations and ignore the rest of the decision-making process. Alternative support paradigms are rare, and it remains unclear how the few that do exist compare to recommendation-centric support. In this work, we aimed to empirically compare recommendation-centric support to an alternative paradigm, continuous support, in the context of diversions in aviation. We conducted a mixed-methods study with 32 professional pilots in a realistic setting. To ensure the quality of our study scenarios, we conducted a focus group with four additional pilots prior to the study. We found that continuous support can support pilots' decision-making in a forward direction, allowing them to think more beyond the limits of the system and make faster decisions when combined with recommendations, though the forward support can be disrupted. Participants' statements further suggest a shift in design goal away from providing recommendations, to supporting quick information gathering. Our results show ways to design more helpful and effective AI decision support that goes beyond end-to-end recommendations.

References

[1]
Anne Kathrine Petersen Bach, Trine Munch Nørgaard, Jens Christian Brok, and Niels Van Berkel. 2023. If I had all the time in the world: ophthalmologists? perceptions of anchoring bias mitigation in clinical AI support. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). ACM, Hamburg, Germany, 16:1--16:14. https://doi.org/10.1145/3544548.3581513
[2]
Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 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 (CHI '21). ACM, Yokohama, Japan, 81:1--81:16. https://doi.org/10.1145/3411764.3445717
[3]
Ann Blandford, Dominic Furniss, and Stephann Makri. 2016. Analysing Data. In Qualitative HCI Research: Going Behind the Scenes, Ann Blandford, Dominic Furniss, and Stephann Makri (Eds.). Springer International Publishing, Cham, 51--60. https://doi.org/10.1007/978--3-031-02217--3_5
[4]
Jeanette Blomberg, Aly Megahed, and Ray Strong. 2018. Acting on analytics: accuracy, precision, interpretation, and performativity. Ethnographic Praxis in Industry Conference Proceedings, Vol. 2018, 1 (2018), 281--300. https://doi.org/10.1111/1559--8918.2018.01208
[5]
Eleanor R. Burgess, Ivana Jankovic, Melissa Austin, Nancy Cai, Adela Kapuscinska, Suzanne Currie, J. Marc Overhage, Erika S Poole, and Jofish Kaye. 2023. Healthcare AI treatment decision support: design principles to enhance clinician adoption and trust. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). ACM, Hamburg, Germany, 15:1--15:19. https://doi.org/10.1145/3544548.3581251
[6]
Adrian Bussone, Simone Stumpf, and Dympna O'Sullivan. 2015. The role of explanations on trust and reliance in clinical decision support systems. In Proceedings of the 2015 International Conference on Healthcare Informatics (ICHI 2015). IEEE, Dallas, TX, USA, 160--169. https://doi.org/10.1109/ICHI.2015.26
[7]
Zana Buçinca, Alexandra Chouldechova, Jennifer Wortman Vaughan, and Krzysztof Z. Gajos. 2022. Beyond end predictions: stop putting machine learning first and design human-centered AI for decision support. In Virtual Workshop on Human-Centered AI Workshop at NeurIPS (HCAI @ NeurIPS '22). Virtual Event, USA, 1--4.
[8]
Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z. Gajos. 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, Vol. 5, CSCW1 (April 2021), 188:1--188:21. https://doi.org/10.1145/3449287
[9]
Federico Cabitza, Andrea Campagner, and Carla Simone. 2021. The need to move away from agential-AI: empirical investigations, useful concepts and open issues. International Journal of Human-Computer Studies, Vol. 155 (Nov. 2021), 102696:1--102696:11. https://doi.org/10.1016/j.ijhcs.2021.102696
[10]
Carrie J. Cai, Martin C. Stumpe, Michael Terry, Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, and Greg S. Corrado. 2019. Human-centered tools for coping with imperfect algorithms during medical decision-making. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). ACM, Glasgow, Scotland, UK, 4:1--4:14. https://doi.org/10.1145/3290605.3300234
[11]
Longbing Cao. 2022. AI in finance: challenges, techniques, and opportunities. Comput. Surveys, Vol. 55, 3 (Feb. 2022), 64:1--64:38. https://doi.org/10.1145/3502289
[12]
Noah Castelo, Maarten W. Bos, and Donald R. Lehmann. 2019. Task-dependent algorithm aversion. Journal of Marketing Research, Vol. 56, 5 (Oct. 2019), 809--825. https://doi.org/10.1177/0022243719851788 Publisher: SAGE Publications Inc.
[13]
Lingwei Cheng and Alexandra Chouldechova. 2023. Overcoming algorithm aversion: a comparison between process and outcome control. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). ACM, Hamburg, Germany, 756:1--756:27. https://doi.org/10.1145/3544548.3581253
[14]
Berkeley J. Dietvorst, Joseph P. Simmons, and Cade Massey. 2015. Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, Vol. 144, 1 (2015), 114--126. https://doi.org/10.1037/xge0000033
[15]
Alan Dix. 2007. Designing for appropriation. In Proceedings of the 21st British HCI Group Annual Conference on People and Computers (BCS-HCI '07, Vol. 2). BCS Learning & Development Ltd., Lancaster, UK, 27--30. https://doi.org/10.14236/ewic/HCI2007.53
[16]
John J. Dudley and Per Ola Kristensson. 2018. A review of user interface design for interactive machine learning. ACM Transactions on Interactive Intelligent Systems, Vol. 8, 2 (July 2018), 8:1--8:37. https://doi.org/10.1145/3185517
[17]
EASA. 2023. Artificial Intelligence Roadmap 2.0: A human-centric approach to AI in aviation. Technical Report. European Union Aviation Safety Agency (EASA). 36 pages. https://www.easa.europa.eu/en/document-library/general-publications/easa-artificial-intelligence-roadmap-20
[18]
Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz. 2021. Expanding explainability: towards social transparency in AI systems. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). ACM, Yokohama, Japan, 82:1--82:19. https://doi.org/10.1145/3411764.3445188
[19]
Malin Eiband, Daniel Buschek, Alexander Kremer, and Heinrich Hussmann. 2019. The impact of placebic explanations on trust in intelligent systems. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). ACM, Glasgow, Scotland, UK, LBW0243:1--LBW0243:6. https://doi.org/10.1145/3290607.3312787
[20]
Sebastian S. Feger, Felix Ehrentraut, Christopher Katins, Philippe Palanque, and Thomas Kosch. 2022. HCI for general aviation: current state and research challenges. Interactions, Vol. 29, 6 (Nov. 2022), 60--65. https://doi.org/10.1145/3564040
[21]
Riccardo Fogliato, Shreya Chappidi, Matthew Lungren, Paul Fisher, Diane Wilson, Michael Fitzke, Mark Parkinson, Eric Horvitz, Kori Inkpen, and Besmira Nushi. 2022. Who goes first? Influences of human-AI workflow on decision making in clinical imaging. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22). Association for Computing Machinery, Seoul, Republic of Korea, 1362--1374. https://doi.org/10.1145/3531146.3533193
[22]
Raymond Fok and Daniel S. Weld. 2024. In search of verifiability: Explanations rarely enable complementary performance in AI-advised decision making. AI Magazine Early View (July 2024), 1--16. https://doi.org/10.1002/aaai.12182
[23]
Krzysztof Z. Gajos and Lena Mamykina. 2022. Do people engage cognitively with AI? Impact of AI assistance on incidental learning. In 27th International Conference on Intelligent User Interfaces (IUI '22). ACM, Helsinki, Finland, 794--806. https://doi.org/10.1145/3490099.3511138
[24]
Ben Green and Yiling Chen. 2019. The principles and limits of algorithm-in-the-loop decision making. Proceedings of the ACM on Human-Computer Interaction, Vol. 3, CSCW (Nov. 2019), 50:1--50:24. https://doi.org/10.1145/3359152
[25]
Hongyan Gu, Chunxu Yang, Mohammad Haeri, Jing Wang, Shirley Tang, Wenzhong Yan, Shujin He, Christopher Kazu Williams, Shino Magaki, and Xiang 'Anthony' Chen. 2023. Augmenting pathologists with NaviPath: design and evaluation of a human-AI collaborative navigation system. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). ACM, Hamburg, Germany, 349:1--349:19. https://doi.org/10.1145/3544548.3580694
[26]
Don Harris. 2007. A human-centred design agenda for the development of single crew operated commercial aircraft. Aircraft Engineering and Aerospace Technology, Vol. 79, 5 (Sept. 2007), 518--526. https://doi.org/10.1108/00022660710780650
[27]
Gaole He, Stefan Buijsman, and Ujwal Gadiraju. 2023. How stated accuracy of an AI system and analogies to explain accuracy affect human reliance on the system. Proceedings of the ACM on Human-Computer Interaction, Vol. 7, CSCW2 (Oct. 2023), 276:1--276:29. https://doi.org/10.1145/3610067
[28]
Robert L. Helmreich, Ashleigh C. Merritt, and John A. Wilhelm. 1999. The evolution of crew resource management training in commercial aviation. The International Journal of Aviation Psychology, Vol. 9, 1 (1999), 19--32. https://doi.org/10.1207/s15327108ijap0901_2
[29]
Patrick Hemmer, Monika Westphal, Max Schemmer, Sebastian Vetter, Michael Vössing, and Gerhard Satzger. 2023. Human-AI collaboration: the effect of AI delegation on human task performance and task satisfaction. In Proceedings of the 28th International Conference on Intelligent User Interfaces (IUI '23). ACM, Sydney, NSW, Australia, 453--463. https://doi.org/10.1145/3581641.3584052
[30]
Hans-Jürgen Hörmann. 1994. FOR-DEC - A prescriptive model for aeronautical decision making. In Proceedings of the 21st Conference of the European Association for Aviation Psychology (EAAP). Avebury Aviation, Dublin, Ireland, 17--23.
[31]
Maia Jacobs, Jeffrey He, Melanie F Pradier, Barbara Lam, Andrew C Ahn, Thomas H McCoy, Roy H Perlis, Finale Doshi-Velez, and Krzysztof Z Gajos. 2021. Designing AI for trust and collaboration in time-constrained medical decisions: a sociotechnical lens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). ACM, Yokohama, Japan, 659:1--659:14. https://doi.org/10.1145/3411764.3445385
[32]
Maia Jacobs, Melanie F. Pradier, Thomas H. McCoy, Roy H. Perlis, Finale Doshi-Velez, and Krzysztof Z. Gajos. 2021. How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection. Translational Psychiatry, Vol. 11, 1 (June 2021), 108:1--108:9. https://doi.org/10.1038/s41398-021-01224-x
[33]
Annika Kaltenhauser, Verena Rheinstädter, Andreas Butz, and Dieter P. Wallach. 2020. "You have to piece the puzzle together": implications for designing decision support in intensive care. In Proceedings of the 2020 ACM Designing Interactive Systems Conference (DIS '20). ACM, Eindhoven, Netherlands, 1509--1522. https://doi.org/10.1145/3357236.3395436
[34]
Anna Kawakami, Venkatesh Sivaraman, Hao-Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, and Kenneth Holstein. 2022. Improving human-AI partnerships in child welfare: understanding worker practices, challenges, and desires for algorithmic decision support. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22). ACM, New Orleans, LA, USA, 52:1--52:18. https://doi.org/10.1145/3491102.3517439
[35]
Anna Kawakami, Venkatesh Sivaraman, Logan Stapleton, Hao-Fei Cheng, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, and Kenneth Holstein. 2022. ?Why do I care What's similar?? Probing challenges in AI-assisted child welfare decision-making through worker-AI interface design concepts. In Designing Interactive Systems Conference (DIS '22). ACM, Virtual Event, Australia, 454--470. https://doi.org/10.1145/3532106.3533556
[36]
Sean Koon. 2022. A human-capabilities orientation for human-AI interaction design. In Virtual Workshop on Human-Centered AI Workshop at NeurIPS (HCAI @ NeurIPS '22). Virtual Event, USA, 1--5.
[37]
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015. Principles of explanatory debugging to personalize interactive machine learning. In Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI '15). Association for Computing Machinery, Atlanta, GA, USA, 126--137. https://doi.org/10.1145/2678025.2701399
[38]
Vivian Lai, Chacha Chen, Alison Smith-Renner, Q. Vera Liao, and Chenhao Tan. 2023. Towards a science of human-AI decision making: an overview of design space in empirical human-subject studies. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT '23). ACM, Chicago, IL, USA, 1369--1385. https://doi.org/10.1145/3593013.3594087
[39]
Vivian Lai and Chenhao Tan. 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 (FAT* '19). ACM, Atlanta, GA, USA, 29--38. https://doi.org/10.1145/3287560.3287590
[40]
Bridget A. Lewis, Valerie J. Gawron, Ehsan Esmaeilzadeh, Ralf H. Mayer, Felipe Moreno-Hines, Neil Nerwich, and Paulo M. Alves. 2021. Data-driven estimation of the impact of diversions due to in-flight medical emergencies on flight delay and aircraft operating costs. Aerospace Medicine and Human Performance, Vol. 92, 2 (Feb. 2021), 99--105. https://doi.org/10.3357/AMHP.5720.2021
[41]
Brian Y. Lim, Joseph P. Cahaly, Chester Y. F. Sng, and Adam Chew. 2023. Diagrammatization: Rationalizing with diagrammatic AI explanations for abductive-deductive reasoning on hypotheses. https://doi.org/10.48550/arXiv.2302.01241 arXiv:2302.01241 [cs].
[42]
Martin Lindvall, Claes Lundström, and Jonas Löwgren. 2021. Rapid assisted visual search: supporting digital pathologists with imperfect AI. In Proceedings of the 26th International Conference on Intelligent User Interfaces (IUI '21). ACM, College Station, TX, USA, 504--513. https://doi.org/10.1145/3397481.3450681
[43]
Han Liu, Vivian Lai, and Chenhao Tan. 2021. Understanding the effect of out-of-distribution examples and interactive explanations on human-AI decision making. Proceedings of the ACM on Human-Computer Interaction, Vol. 5, CSCW2 (Oct. 2021), 408:1--408:45. https://doi.org/10.1145/3479552
[44]
John M. McGuirl and Nadine B. Sarter. 2006. Supporting trust calibration and the effective use of decision aids by presenting dynamic system confidence information. Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 48, 4 (Dec. 2006), 656--665. https://doi.org/10.1518/001872006779166334
[45]
Tim Miller. 2023. Explainable AI is dead, long live explainable AI! Hypothesis-driven decision support using evaluative AI. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT '23). ACM, Chicago, IL, USA, 333--342. https://doi.org/10.1145/3593013.3594001
[46]
Joon Sung Park, Rick Barber, Alex Kirlik, and Karrie Karahalios. 2019. A slow algorithm improves users' assessments of the algorithm's accuracy. Proceedings of the ACM on Human-Computer Interaction, Vol. 3, CSCW (Nov. 2019), 102:1--102:15. https://doi.org/10.1145/3359204
[47]
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2021. Manipulating and measuring model interpretability. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). ACM, Yokohama, Japan, 237:1--237:52. https://doi.org/10.1145/3411764.3445315
[48]
Snehal Prabhudesai, Leyao Yang, Sumit Asthana, Xun Huan, Q. Vera Liao, and Nikola Banovic. 2023. Understanding uncertainty: how lay decision-makers perceive and interpret uncertainty in human-AI decision making. In Proceedings of the 28th International Conference on Intelligent User Interfaces (IUI '23). ACM, Sydney, NSW, Australia, 379--396. https://doi.org/10.1145/3581641.3584033
[49]
Andrew Prahl and Lyn Van Swol. 2017. Understanding algorithm aversion: when is advice from automation discounted? Journal of Forecasting, Vol. 36, 6 (2017), 691--702. https://doi.org/10.1002/for.2464
[50]
Stephan Raaijmakers. 2019. Artificial intelligence for law enforcement: challenges and opportunities. IEEE Security & Privacy, Vol. 17, 5 (Sept. 2019), 74--77. https://doi.org/10.1109/MSEC.2019.2925649
[51]
Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, and Richard Tomsett. 2022. Deciding fast and slow: the role of cognitive biases in AI-assisted decision-making. Proceedings of the ACM on Human-Computer Interaction, Vol. 6, CSCW1 (April 2022), 83:1--83:22. https://doi.org/10.1145/3512930
[52]
Amy Rechkemmer and Ming Yin. 2022. When confidence meets accuracy: exploring the effects of multiple performance indicators on trust in machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22). Association for Computing Machinery, New Orleans, LA, USA, 535:1--535:14. https://doi.org/10.1145/3491102.3501967
[53]
Emilie Roth, Devorah Klein, Christen Sushereba, Katie Ernst, and Lauren Militello. 2022. Methods and measures to evaluate technologies that influence aviator decision making and situation awareness. Contract Report USAARL-TECH-CR--2022--22. Applied Decision Science, Cincinnati, OH, USA. 80 pages.
[54]
Max Schemmer, Niklas Kuehl, Carina Benz, Andrea Bartos, and Gerhard Satzger. 2023. Appropriate reliance on AI advice: conceptualization and the effect of explanations. In Proceedings of the 28th International Conference on Intelligent User Interfaces (IUI '23). ACM, Sydney, NSW, Australia, 410--422. https://doi.org/10.1145/3581641.3584066
[55]
Stephan Schlögl, Claudia Postulka, Reinhard Bernsteiner, and Christian Ploder. 2019. Artificial intelligence tool penetration in business: adoption, challenges and fears. In Knowledge Management in Organizations (KMO 2019). Springer International Publishing, Zamora, Spain, 259--270. https://doi.org/10.1007/978--3-030--21451--7_22
[56]
Philipp Schmidt and Felix Biessmann. 2020. Calibrating human-AI collaboration: impact of risk, ambiguity and transparency on algorithmic bias. In Machine Learning and Knowledge Extraction (CD-MAKE 2020). Springer International Publishing, Dublin, Ireland, 431--449. https://doi.org/10.1007/978--3-030--57321--8_24
[57]
Scott Shappell, Cristy Detwiler, Kali Holcomb, Carla Hackworth, Albert Boquet, and Douglas A. Wiegmann. 2007. Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system. Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 49, 2 (April 2007), 227--242. https://doi.org/10.1518/001872007X312469 Publisher: SAGE Publications Inc.
[58]
Ben Shneiderman. 2020. Design lessons from AI's two grand goals: human emulation and useful applications. IEEE Transactions on Technology and Society, Vol. 1, 2 (June 2020), 73--82. https://doi.org/10.1109/TTS.2020.2992669
[59]
Venkatesh Sivaraman, Leigh A. Bukowski, Joel Levin, Jeremy M. Kahn, and Adam Perer. 2023. Ignore, trust, or negotiate: understanding clinician acceptance of AI-based treatment recommendations in health care. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). ACM, Hamburg, Germany, 754:1--754:18. https://doi.org/10.1145/3544548.3581075
[60]
P.J. Smith, C.E. McCoy, and C. Layton. 1997. Brittleness in the design of cooperative problem-solving systems: the effects on user performance. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol. 27, 3 (May 1997), 360--371. https://doi.org/10.1109/3468.568744
[61]
Alison Smith-Renner, Ron Fan, Melissa Birchfield, Tongshuang Wu, Jordan Boyd-Graber, Daniel S. Weld, and Leah Findlater. 2020. No explainability without accountability: an empirical study of explanations and feedback in interactive ML. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, Honolulu, HI, USA, 497:1--497:13. https://doi.org/10.1145/3313831.3376624
[62]
Niels van Berkel, Omer F. Ahmad, Danail Stoyanov, Laurence Lovat, and Ann Blandford. 2021. Designing visual markers for continuous artificial intelligence support: a colonoscopy case study. ACM Transactions on Computing for Healthcare, Vol. 2, 1 (Dec. 2021), 7:1--7:24. https://doi.org/10.1145/3422156
[63]
Julian Varghese. 2020. Artificial intelligence in medicine: chances and challenges for wide clinical adoption. Visceral Medicine, Vol. 36, 6 (Oct. 2020), 443--449. https://doi.org/10.1159/000511930
[64]
Helena Vasconcelos, Matthew Jörke, Madeleine Grunde-McLaughlin, Tobias Gerstenberg, Michael S. Bernstein, and Ranjay Krishna. 2023. Explanations can reduce overreliance on AI systems during decision-making. Proceedings of the ACM on Human-Computer Interaction, Vol. 7, CSCW1 (April 2023), 129:1--129:38. https://doi.org/10.1145/3579605
[65]
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019. Designing theory-driven user-centric explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). ACM, Glasgow, Scotland, UK, 601:1--601:15. https://doi.org/10.1145/3290605.3300831
[66]
Xinru Wang and Ming Yin. 2021. Are explanations helpful? A comparative study of the effects of explanations in AI-assisted decision-making. In Proceedings of the 26th International Conference on Intelligent User Interfaces (IUI '21). ACM, College Station, TX, USA, 318--328. https://doi.org/10.1145/3397481.3450650
[67]
David D. Woods. 1986. Paradigms for intelligent decision support. In Intelligent Decision Support in Process Environments (NATO ASI Series, Vol. 21). Springer Berlin, Heidelberg, San Miniato, Italy, 153--173. https://doi.org/10.1007/978--3--642--50329-0_11
[68]
Jakob Würfel, Boris Djartov, Anne Papenfuß, and Matthias Wies. 2023. Intelligent Pilot Advisory System: The journey from ideation to an early system design of an AI-based decision support system for airline flight decks. In Human Factors in Transportation (AHFE 2023, Vol. 95). AHFE Open Acces, San Francisco, CA, USA, 589--597. https://doi.org/10.54941/ahfe1003844
[69]
Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing biomedical literature to calibrate clinicians? trust in AI decision support systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). ACM, Hamburg, Germany, 14:1--14:14. https://doi.org/10.1145/3544548.3581393
[70]
Qian Yang, Aaron Steinfeld, and John Zimmerman. 2019. Unremarkable AI: fitting intelligent decision support into critical, clinical decision-making processes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). ACM, Glasgow, Scotland, UK, 238:1--238:11. https://doi.org/10.1145/3290605.3300468
[71]
Qian Yang, John Zimmerman, Aaron Steinfeld, Lisa Carey, and James F. Antaki. 2016. Investigating the heart pump implant decision process: opportunities for decision support tools to help. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). ACM, San Jose, CA, USA, 4477--4488. https://doi.org/10.1145/2858036.2858373
[72]
Kun-Hsing Yu, Andrew L. Beam, and Isaac S. Kohane. 2018. Artificial intelligence in healthcare. Nature Biomedical Engineering, Vol. 2, 10 (Oct. 2018), 719--731. https://doi.org/10.1038/s41551-018-0305-z Publisher: Nature Publishing Group.
[73]
Shao Zhang, Jianing Yu, Xuhai Xu, Changchang Yin, Yuxuan Lu, Bingsheng Yao, Melanie Tory, Lace M. Padilla, Jeffrey Caterino, Ping Zhang, and Dakuo Wang. 2024. Rethinking human-AI collaboration in complex medical decision making: a case study in sepsis diagnosis. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24). ACM, Honolulu, HI, USA, 445:1--445:18. https://doi.org/10.1145/3613904.3642343
[74]
Yunfeng Zhang, Q. Vera Liao, and Rachel K. E. Bellamy. 2020. Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). ACM, Barcelona, Spain, 295--305. https://doi.org/10.1145/3351095.3372852
[75]
Zelun Tony Zhang, Yuanting Liu, and Heinrich Hussmann. 2021. Forward reasoning decision support: toward a more complete view of the human-AI interaction design space. In CHItaly 2021: 14th Biannual Conference of the Italian SIGCHI Chapter (CHItaly '21). ACM, Bolzano, Italy, 18:1--18:5. https://doi.org/10.1145/3464385.3464696
[76]
Zelun Tony Zhang, Cara Storath, Yuanting Liu, and Andreas Butz. 2023. Resilience through appropriation: pilots' view on complex decision support. In Proceedings of the 28th International Conference on Intelligent User Interfaces (IUI '23). ACM, Sydney, NSW, Australia, 397--409. https://doi.org/10.1145/3581641.3584056

Cited By

View all
  • (2024)Machine Learning Systems are Bloated and VulnerableACM SIGMETRICS Performance Evaluation Review10.1145/3673660.365506452:1(37-38)Online publication date: 13-Jun-2024
  • (2024)Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596148:2(1-29)Online publication date: 15-May-2024
  • (2024)Sensing to Hear through MemoryProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595988:2(1-31)Online publication date: 15-May-2024
  • Show More Cited By

Index Terms

  1. Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image Proceedings of the ACM on Human-Computer Interaction
      Proceedings of the ACM on Human-Computer Interaction  Volume 8, Issue CSCW2
      CSCW
      November 2024
      5177 pages
      EISSN:2573-0142
      DOI:10.1145/3703902
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 08 November 2024
      Published in PACMHCI Volume 8, Issue CSCW2

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. AI-assisted decision-making
      2. aviation
      3. continuous support
      4. decision support paradigms
      5. decision support tools
      6. human-AI decision-making
      7. human-AI interaction
      8. imperfect AI
      9. intelligent decision support
      10. process-oriented support

      Qualifiers

      • Research-article

      Funding Sources

      • German Federal Ministry for Economic Affairs and Climate Action (BMWK)

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)195
      • Downloads (Last 6 weeks)26
      Reflects downloads up to 20 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Machine Learning Systems are Bloated and VulnerableACM SIGMETRICS Performance Evaluation Review10.1145/3673660.365506452:1(37-38)Online publication date: 13-Jun-2024
      • (2024)Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596148:2(1-29)Online publication date: 15-May-2024
      • (2024)Sensing to Hear through MemoryProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595988:2(1-31)Online publication date: 15-May-2024
      • (2024)Machine Learning Systems are Bloated and VulnerableAbstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems10.1145/3652963.3655064(37-38)Online publication date: 10-Jun-2024

      View Options

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media