Transparent-AI Blueprint: Developing a Conceptual Tool to Support the Design of Transparent AI Agents

References

• Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018). Trends and trajectories for explainable, accountable and intelligible systems: An HCI research agenda [Paper presentation]. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–18), Montreal, QC, Canada. https://doi.org/10.1145/3173574.3174156
   Google Scholar
• Alonso, V., & de la Puente, P. (2018). System transparency in shared autonomy: A mini review. Frontiers in Neurorobotics, 12, 83. https://doi.org/10.3389/fnbot.2018.00083
   PubMed Web of Science ®Google Scholar
• Amarsy, N. (2015). Why and how organizations around the world apply the business model canvas. https://www.strategyzer.com/blog/posts/2015/2/9/why-and-how-organizations-around-the-world-apply-the-business-model-canvas
   Google Scholar
• Amershi, S., Inkpen, K., Teevan, J., Kikin-Gil, R., Horvitz, E., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., & Bennett, P. N. (2019). Guidelines for human-AI interaction [Paper presentation]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19 (pp. 1–13), Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300233
   Google Scholar
• Anderson, J., Arbour, B., Arnold, R., Kadiofsky, T., Keeley, T., MacLeod, M., Bourdon, S., Crootof, R., Matsumura, J., Mayer, C., Roorda, M., Saariluoma, P., Scharre, P., Suarez, B., Sulzbachner, C., Theunissen, E., Tolk, A., Williams, A., & Zinner, C. (2015). Autonomous systems: Issues for defence policymakers. /paper/Autonomous-Systems%3A-Issues-for-Defence-Policymakers-Anderson-Arbour/c2abe9ec4538e532b50982729b9d0e3cf454104e
   Google Scholar
• Anderson, A., Dodge, J., Sadarangani, A., Juozapaitis, Z., Newman, E., Irvine, J., Chattopadhyay, S., Olson, M., Fern, A., & Burnett, M. (2020). Mental models of mere mortals with explanations of reinforcement learning. ACM Transactions on Interactive Intelligent Systems, 10(2), 1–37. https://doi.org/10.1145/3366485
   Web of Science ®Google Scholar
• Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K. (2019). Explainable agents and robots: Results from a systematic literature review. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (pp. 1078–1088). IFAAMAS.
   Google Scholar
• Ashktorab, Z., Jain, M., Liao, Q. V., & Weisz, J. D. (2019). Resilient chatbots: Repair strategy preferences for conversational breakdowns [Paper presentation]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–12), Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300484
   Google Scholar
• Augstein, M., Neumayr, T., Kurschl, W., Kern, D., Burger, T., & Altmann, J. (2017). A personalized interaction approach: Motivation and use case [Paper presentation]. Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 221–226), Bratislava, Slovakia. https://doi.org/10.1145/3099023.3099051
   Google Scholar
• Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. ICLR.
   Google Scholar
• Baraka, K., Paiva, A., & Veloso, M. (2016). Expressive lights for revealing mobile service robot state. In L. P. Reis, A. P. Moreira, P. U. Lima, L. Montano, & V. Muñoz-Martinez (Eds.), Robot 2015: Second Iberian robotics conference (pp. 107–119). Springer International Publishing. https://doi.org/10.1007/978-3-319-27146-0_9
   Google Scholar
• Barria-Pineda, J. (2020). Exploring the need for transparency in educational recommender systems [Paper presentation]. Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 376–379), Genoa, Italy. https://doi.org/10.1145/3340631.3398676
   Google Scholar
• Bass, E. J., Baumgart, L. A., & Shepley, K. K. (2013). The effect of information analysis automation display content on human judgment performance in noisy environments. Journal of Cognitive Engineering and Decision Making, 7(1), 49–65. https://doi.org/10.1177/1555343412453461
   PubMedGoogle Scholar
• Bastani, O., Kim, C., & Bastani, H. (2017). Interpreting blackbox models via model extraction. ArXiv, arXiv:1705.08504 [cs.LG].
   Google Scholar
• Beller, J., Heesen, M., & Vollrath, M. (2013). Improving the driver–automation interaction: An approach using automation uncertainty. Human Factors, 55(6), 1130–1141. https://doi.org/10.1177/0018720813482327
   PubMed Web of Science ®Google Scholar
• Bellotti, V., & Edwards, K. (2001). Intelligibility and accountability: Human considerations in context-aware systems. Human–Computer Interaction, 16(2–4), 193–212. https://doi.org/10.1207/S15327051HCI16234_05
   Web of Science ®Google Scholar
• Beneteau, E., Richards, O. K., Zhang, M., Kientz, J. A., Yip, J., & Hiniker, A. (2019). Communication breakdowns between families and alexa [Paper presentation]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–13), Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300473
   Google Scholar
• Betzing, J. H., Tietz, M., vom Brocke, J., & Becker, J. (2020). The impact of transparency on mobile privacy decision making. Electronic Markets, 30(3), 607–625. https://doi.org/10.1007/s12525-019-00332-3
   Web of Science ®Google Scholar
• Bhaskara, A., Skinner, M., & Loft, S. (2020). Agent transparency: A review of current theory and evidence. IEEE Transactions on Human-Machine Systems, 50(3), 215–224. https://doi.org/10.1109/THMS.2020.2965529
   Web of Science ®Google Scholar
• Blass, J. A. (2018). Legal, ethical, customizable artificial intelligence [Paper presentation]. AIES '18: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 358–359). New Orleans, LA, USA. https://doi.org/10.1145/3278721.3278793
   Google Scholar
• Boell, S., & Cecez-Kecmanovic, D. (2014). A hermeneutic approach for conducting literature reviews and literature searches. Communications of the Association for Information Systems, 34(1), 257–286. https://doi.org/10.17705/1CAIS.03412
   Google Scholar
• Bondarenko, A., Borisov, A., & Zmanovska, T. (2010, June 25). Decompositional rules extraction methods from neural networks. Mendel. 16th International Conference on Soft Computing, Brno, Czech Republic.
   Google Scholar
• Boring, R. L. (2001). User-interface design principles for experimental control software [Paper presentation]. CHI ’01 Extended Abstracts on Human Factors in Computing Systems (pp. 399–400), Seattle, Washington. https://doi.org/10.1145/634067.634302
   Google Scholar
• Bostandjiev, S., O’Donovan, J., & Höllerer, T. (2012). TasteWeights: A visual interactive hybrid recommender system [Paper presentation]. Proceedings of the Sixth ACM Conference on Recommender Systems (pp. 35–42), Dublin, Ireland. https://doi.org/10.1145/2365952.2365964
   Google Scholar
• Bostandjiev, S., O’Donovan, J., & Höllerer, T. (2013). LinkedVis: Exploring social and semantic career recommendations [Paper presentation]. Proceedings of the 2013 International Conference on Intelligent User Interfaces (pp. 107–116), Santa Monica, California, USA. https://doi.org/10.1145/2449396.2449412
   Google Scholar
• Braun, M., Li, J., Weber, F., Pfleging, B., Butz, A., & Alt, F. (2020). What if your car would care? Exploring use cases for affective automotive user interfaces [Paper presentation]. 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (pp. 1–12), Oldenburg, Germany. https://doi.org/10.1145/3379503.3403530
   Google Scholar
• Bruns, S., Valdez, A. C., Greven, C., Ziefle, M., & Schroeder, U. (2015). What should i read next? A personalized visual publication recommender system. In S. Yamamoto (Ed.), Human interface and the management of information. Information and knowledge in context (pp. 89–100). Springer International Publishing. https://doi.org/10.1007/978-3-319-20618-9_9
   Google Scholar
• Bunt, A., Lount, M., & Lauzon, C. (2012). Are explanations always important? A study of deployed, low-cost intelligent interactive systems [Paper presentation]. Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces (pp. 169–178), Lisbon, Portugal. https://doi.org/10.1145/2166966.2166996
   Google Scholar
• Burke, M., Amento, B., & Isenhour, P. (2006). Error correction of voicemail transcripts in SCANMail [Paper presentation]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 339–348), Montréal, Québec, Canada. https://doi.org/10.1145/1124772.1124823
   Google Scholar
• Cai, C. J., Jongejan, J., & Holbrook, J. (2019). The effects of example-based explanations in a machine learning interface [Paper presentation]. Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 258–262), Marina del Rey, California. https://doi.org/10.1145/3301275.3302289
   Google Scholar
• Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., Stumpe, M. C., & Terry, M. (2019). Human-centered tools for coping with imperfect algorithms during medical decision-making [Paper presentation]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–14), Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300234
   Google Scholar
• Chatterjee, S., & Hadi, A. S. (1988). Sensitivity analysis in linear regression (1st ed.). Wiley.
   Google Scholar
• Chen, J. Y. C., Barnes, M. J., Selkowitz, A. R., Stowers, K., Lakhmani, S. G., & Kasdaglis, N. (2016). Human-autonomy teaming and agent transparency [Paper presentation]. Companion Publication of the 21st International Conference on Intelligent User Interfaces (pp. 28–31), Sonoma, California, USA. https://doi.org/10.1145/2876456.2879479
   Google Scholar
• Chen, J. Y. C., Lakhmani, S. G., Stowers, K., Selkowitz, A. R., Wright, J. L., & Barnes, M. (2018). Situation awareness-based agent transparency and human-autonomy teaming effectiveness. Theoretical Issues in Ergonomics Science, 19(3), 259–282. https://doi.org/10.1080/1463922X.2017.1315750
   Web of Science ®Google Scholar
• Chen, J., Procci, K., Boyce, M., Wright, J., Garcia, A., & Barnes, M. (2014). Situation awareness–based agent transparency. Army Research Laboratory.
   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 [Paper presentation]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–12), Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300789
   Google Scholar
• Cho, M., Lee, S., & Lee, K.-P. (2019). Once a kind friend is now a thing: Understanding how conversational agents at home are forgotten [Paper presentation]. Proceedings of the 2019 on Designing Interactive Systems Conference (pp. 1557–1569), San Diego, CA, USA. https://doi.org/10.1145/3322276.3322332
   Google Scholar
• Choi, E., Bahadori, M. T., Sun, J., Kulas, J., Schuetz, A., & Stewart, W. (2016). RETAIN: An Interpretable predictive model for healthcare using reverse time attention mechanism. NIPS.
   Google Scholar
• Choi, J. (2020). Interpreting and explaining deep neural networks: A perspective on time series data [Paper presentation]. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3563–3564), Virtual Event, CA, USA. https://doi.org/10.1145/3394486.3406478
   Google Scholar
• Chuang, L. L., Manstetten, D., Boll, S., & Baumann, M. (2017). 1st Workshop on understanding automation: Interfaces that facilitate user understanding of vehicle automation [Paper presentation]. Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct (pp. 1–8), Oldenburg, Germany. https://doi.org/10.1145/3131726.3131729
   Google Scholar
• Clark, L., Pantidi, N., Cooney, O., Doyle, P., Garaialde, D., Edwards, J., Spillane, B., Gilmartin, E., Murad, C., Munteanu, C., Wade, V., & Cowan, B. R. (2019). What makes a good conversation? Challenges in designing truly conversational agents [Paper presentation]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–12), Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300705
   Google Scholar
• Cramer, H., Evers, V., Ramlal, S., van Someren, M., Rutledge, L., Stash, N., Aroyo, L., & Wielinga, B. (2008). The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-Adapted Interaction, 18(5), 455–496. https://doi.org/10.1007/s11257-008-9051-3
   Web of Science ®Google Scholar
• Craven, M. W. (1996). Extracting comprehensible models from trained neural networks [PhD]. The University of Wisconsin - Madison.
   Google Scholar
• Defense Science Board (2016). Defense science board summer study on autonomy. Defense Science Board.
   Google Scholar
• Department of Health (2000). No secrets: Guidance on protecting vulnerable adults in care. https://dementiapartnerships.com/resource/no-secrets-guidance-on-protecting-vulnerable-adults-in-care/
   Google Scholar
• Donkers, T., Kleemann, T., & Ziegler, J. (2020). Explaining recommendations by means of aspect-based transparent memories [Paper presentation]. Proceedings of the 25th International Conference on Intelligent User Interfaces (pp. 166–176), Cagliari, Italy. https://doi.org/10.1145/3377325.3377520
   Google Scholar
• Doshi-Velez, F., & Kim, B. (2017). A roadmap for a rigorous science of interpretability. /paper/A-Roadmap-for-a-Rigorous-Science-of-Doshi-Velez-Kim/7f2d2d14f0c07e4b3d9d636d904afa7087673b62
   Google Scholar
• Du, M., Liu, N., & Hu, X. (2019). Techniques for interpretable machine learning. Communications of the ACM, 63(1), 68–77. https://doi.org/10.1145/3359786
   Web of Science ®Google Scholar
• Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., & Beck, H. P. (2003). The role of trust in automation reliance. International Journal of Human-Computer Studies, 58(6), 697–718. https://doi.org/10.1016/S1071-5819(03)00038-7
   Web of Science ®Google Scholar
• Edwards, J., Perrone, A., & Doyle, P. R. (2020). Transparency in language generation: Levels of automation [Paper presentation]. Proceedings of the 2nd Conference on Conversational User Interfaces (pp. 1–3), Bilbao, Spain. https://doi.org/10.1145/3405755.3406136
   Google Scholar
• Edwards, L., & Veale, M. (2018). Enslaving the algorithm: From a ‘right to an explanation’ to a ‘right to better decisions?’ IEEE Security & Privacy, 16(3), 46–54. https://doi.org/10.1109/MSP.2018.2701152
   Web of Science ®Google Scholar
• Eiband, M., Schneider, H., Bilandzic, M., Fazekas-Con, J., Haug, M., & Hussmann, H. (2018). Bringing transparency design into practice [Paper presentation]. 23rd International Conference on Intelligent User Interfaces (pp. 211–223), Tokyo, Japan. https://doi.org/10.1145/3172944.3172961
   Google Scholar
• Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 32–64. https://doi.org/10.1518/001872095779049543
   Web of Science ®Google Scholar
• Engineering and Physical Sciences Research Council. (2010). Principles of robotics. https://epsrc.ukri.org/research/ourportfolio/themes/engineering/activities/principlesofrobotics/
   Google Scholar
• Eslami, M. (2017). Understanding and designing around users’ interaction with hidden algorithms in sociotechnical systems [Paper presentation]. Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 57–60), Portland, Oregon, USA. https://doi.org/10.1145/3022198.3024947
   Google Scholar
• Eslami, M., Vaccaro, K., Lee, M. K., Elazari Bar On, A., Gilbert, E., & Karahalios, K. (2019). User attitudes towards algorithmic opacity and transparency in online reviewing platforms [Paper presentation]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–14), Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300724
   Google Scholar
• Felzmann, H., Villaronga, E. F., Lutz, C., & Tamò-Larrieux, A. (2019). Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns. Big Data & Society, 6(1), 205395171986054. https://doi.org/10.1177/2053951719860542
   Web of Science ®Google Scholar
• Feng, S., & Boyd-Graber, J. (2019). What can AI do for me? Evaluating machine learning interpretations in cooperative play [Paper presentation]. Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 229–239), Marina del Rey, California. https://doi.org/10.1145/3301275.3302265
   Google Scholar
• Fortuny, E. J., & de Martens, D. (2015). Active learning-based pedagogical rule extraction. IEEE Transactions on Neural Networks and Learning Systems, 26(11), 2664–2677. https://doi.org/10.1109/TNNLS.2015.2389037
   PubMed Web of Science ®Google Scholar
• Frosst, N., & Hinton, G. E. (2017). Distilling a neural network into a soft decision tree. ArXiv, arXiv:1711.09784 [cs.LG].
   Google Scholar
• General Data Protection Regulation (GDPR) Compliance Guidelines (2016). GDPR.Eu. https://gdpr.eu/
   Google Scholar
• Gentner, D., & Stevens, A. L. (2014). Mental models. Psychology Press.
   Google Scholar
• Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249–264. https://doi.org/10.1016/S0304-3800(02)00257-0
   Web of Science ®Google Scholar
• Grgic-Hlaca, N., Zafar, M., Gummadi, K., & Weller, A. (2018). Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning. AAAI-18, 32(1), 51–60. https://doi.org/10.1609/aaai.v32i1.11296
   Google Scholar
• Hancock, P. A. (2017). Imposing limits on autonomous systems. Ergonomics, 60(2), 284–291. https://doi.org/10.1080/00140139.2016.1190035
   PubMed Web of Science ®Google Scholar
• Harbers, M., van den Bosch, K., & Meyer, J. J. C. (2011). A theoretical framework for explaining agent behavior [Paper presentation]. 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2011, 29 July 2011 through 31 July 2011, Noordwijkerhout, 228, Noordwijkerhout, The Netherlands.
   Google Scholar
• He, C., Parra, D., & Verbert, K. (2016). Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications, 56(2016), 9–27. https://doi.org/10.1016/j.eswa.2016.02.013
   Google Scholar
• Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018). Metrics for explainable AI: Challenges and prospects. ArXiv E-Prints, 1812, arXiv:1812.04608.
   Google Scholar
• Hohman, F., Park, H., Robinson, C., & Chau, D. H. P. (2020). Summit: Scaling deep learning interpretability by visualizing activation and attribution summarizations. IEEE Transactions on Visualization and Computer Graphics, 26(1), 1096–1106. https://doi.org/10.1109/TVCG.2019.2934659
   PubMed Web of Science ®Google Scholar
• Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? [Paper presentation]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–16), Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300830
   Google Scholar
• Hopkin, V. D. (1995). Human factors in air traffic control (1st ed.). CRC Press.
   Google Scholar
• Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000). Foundations for an empirically determined scale of trust in automated systems. International Journal of Cognitive Ergonomics, 4(1), 53–71. https://doi.org/10.1207/S15327566IJCE0401_04
   Google Scholar
• Jin, Y., Seipp, K., Duval, E., & Verbert, K. (2016). Go with the flow: Effects of transparency and user control on targeted advertising using flow charts [Paper presentation]. Proceedings of the International Working Conference on Advanced Visual Interfaces (pp. 68–75), Bari, Italy. https://doi.org/10.1145/2909132.2909269
   Google Scholar
• Johnson, H., & Johnson, P. (1993). Explanation facilities and interactive systems [Paper presentation]. Proceedings of the 1st International Conference on Intelligent User Interfaces (pp. 159–166), Orlando, Florida, USA. https://doi.org/10.1145/169891.169951
   Google Scholar
• Kallinen, K. (2017). The effects of transparency and task type on trust, stress, quality of work, and co-worker preference during human-autonomous system collaborative work [Paper presentation]. Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 153–154), Vienna, Austria. https://doi.org/10.1145/3029798.3038386
   Google Scholar
• Kangasrääsiö, A., Glowacka, D., & Kaski, S. (2015). Improving controllability and predictability of interactive recommendation interfaces for exploratory search [Paper presentation]. Proceedings of the 20th International Conference on Intelligent User Interfaces (pp. 247–251), Atlanta, Georgia, USA. https://doi.org/10.1145/2678025.2701371
   Google Scholar
• Kaptein, F., Broekens, J., Hindriks, K., & Neerincx, M. (2019). Evaluating cognitive and affective intelligent agent explanations in a long-term health-support application for children with type 1 diabetes [Paper presentation]. 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 1–7), Cambridge, UK. https://doi.org/10.1109/ACII.2019.8925526
   Google Scholar
• Karahasanovic, A., Brandtzaeg, P. B., Vanattenhoven, J., Lievens, B., Nielsen, K. T., & Pierson, J. (2009). Ensuring trust, privacy, and etiquette in web 2.0 applications. Computer Magazine. 42(6), 42–49. https://doi.org/10.1109/MC.2009.186
   Web of Science ®Google Scholar
• Kaufman, L., & Rousseeuw, P. J. (1987). Clustering by means of medoids. In Y. Dodge (Ed.), Statistical data analysis based on the L1-norm and related methods. (pp. 405–416).
   Google Scholar
• Kim, B. H., Koh, S., Huh, S., Jo, S., & Choi, S. (2020). Improved explanatory efficacy on human affect and workload through interactive process in artificial intelligence. IEEE Access, 8, 189013–189024. https://doi.org/10.1109/ACCESS.2020.3032056
   Web of Science ®Google Scholar
• Kim, B., Khanna, R., & Koyejo, O. (2016). Examples are not enough, learn to criticize! Criticism for interpretability [Paper presentation]. Proceedings of the 30th International Conference on Neural Information Processing Systems (pp. 2288–2296), Barcelona, Spain.
   Google Scholar
• Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., & Sayres, R. (2018). Interpretability beyond feature attribution: Quantitative Testing with Concept Activation Vectors (TCAV). International Conference on Machine Learning (pp. 2668–2677I), Stockholm, Sweden. http://proceedings.mlr.press/v80/kim18d.html
   Google Scholar
• Kizilcec, R. F. (2016). How much information? Effects of transparency on trust in an algorithmic interface [Paper presentation]. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 2390–2395), San Jose, California, USA. https://doi.org/10.1145/2858036.2858402
   Google Scholar
• Koelle, M., Ananthanarayan, S., & Boll, S. (2020). Social acceptability in HCI: A survey of methods, measures, and design strategies [Paper presentation]. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–19), Honolulu, HI, USA. https://doi.org/10.1145/3313831.3376162
   Google Scholar
• Krauss, C., Merceron, A., & Arbanowski, S. (2019). The timeliness deviation: A novel approach to evaluate educational recommender systems for closed-courses [Paper presentation]. Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 195–204), Tempe, AZ, USA. https://doi.org/10.1145/3303772.3303774
   Google Scholar
• Krishnan, R., Sivakumar, G., & Bhattacharya, P. (1999). Extracting decision trees from trained neural networks. Pattern Recognition, 32(12), 1999–2009. https://doi.org/10.1016/S0031-3203(98)00181-2
   Web of Science ®Google Scholar
• Kuner, C., Svantesson, D. J. B., Cate, F. H., Lynskey, O., & Millard, C. (2017). Machine learning with personal data: Is data protection law smart enough to meet the challenge? International Data Privacy Law, 7(1), 1–2. https://doi.org/10.1093/idpl/ipx003
   Web of Science ®Google Scholar
• Lagström, T., & Malmsten Lundgren, V. (2016). AVIP-Autonomous vehicles’ interaction with pedestrians-An investigation of pedestrian-driver communication and development of a vehicle external interface [Master’s thesis].
   Google Scholar
• Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016). Interpretable decision sets: A joint framework for description and prediction [Paper presentation]. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1675–1684), San Francisco, California, USA. https://doi.org/10.1145/2939672.2939874
   Google Scholar
• Lane, H. C., Core, M. G., van Lent, M., Solomon, S., Gomboc, D. (2005). Explainable artificial intelligence for training and tutoring [Paper presentation]. Proceedings of the 2005 Conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (pp. 762–764), Amsterdam, The Netherlands.
   Google Scholar
• Large, D. R., Clark, L., Burnett, G., Harrington, K., Luton, J., Thomas, P., & Bennett, P. (2019). ‘It’s small talk, Jim, but not as we know it’: Engendering trust through human-agent conversation in an autonomous, self-driving car [Paper presentation]. Proceedings of the 1st International Conference on Conversational User Interfaces (pp. 1–7), Dublin, Ireland. https://doi.org/10.1145/3342775.3342789
   Google Scholar
• Lauff, C., Menold, J., & Wood, K. L. (2019). Prototyping canvas: Design tool for planning purposeful prototypes. Proceedings of the Design Society: International Conference on Engineering Design, 1(1), 1563–1572. https://doi.org/10.1017/dsi.2019.162
   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
• Lee, M. K., & Baykal, S. (2017). Algorithmic mediation in group decisions: Fairness perceptions of algorithmically mediated vs. discussion-based social division [Paper presentation]. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 1035–1048), Portland, Oregon, USA. https://doi.org/10.1145/2998181.2998230
   Google Scholar
• Lee, M. K., Jain, A., Cha, H. J., Ojha, S., & Kusbit, D. (2019). Procedural justice in algorithmic fairness: Leveraging transparency and outcome control for fair algorithmic mediation. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–182. https://doi.org/10.1145/3359284
   PubMedGoogle Scholar
• Lee, S., Cho, M., & Lee, S. (2020). What if conversational agents became invisible? Comparing users’ mental models according to physical entity of AI speaker. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1–24. https://doi.org/10.1145/3411840
   PubMedGoogle Scholar
• Lee, S., Kim, S., & Lee, S. (2019). ‘What does your Agent look like?’: A drawing study to understand users’ perceived persona of conversational agent [Paper presentation]. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–6), Glasgow, Scotland, UK. https://doi.org/10.1145/3290607.3312796
   Google Scholar
• Lettl, B., & Schulte, A. (2013). Self-explanation capability for cognitive agents on-board of UCAVs to improve cooperation in a manned-unmanned fighter team [Paper presentation]. AIAA Infotech@Aerospace (I@A) Conference (Vol. 1–0). American Institute of Aeronautics and Astronautics, Boston, MA. https://doi.org/10.2514/6.2013-4898
   Google Scholar
• Lewis, J. W. (1983). An effective graphics user interface for rules and inference mechanisms [Paper presentation]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 139–143), Boston, Massachusetts, USA. https://doi.org/10.1145/800045.801598
   Google Scholar
• Li, T. J.-J., Chen, J., Xia, H., Mitchell, T. M., & Myers, B. A. (2020). Multi-modal repairs of conversational breakdowns in task-oriented dialogs [Paper presentation]. Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (pp. 1094–1107), Virtual Event, USA. https://doi.org/10.1145/3379337.3415820
   Google Scholar
• Lim, B. Y., & Dey, A. K. (2009). Assessing demand for intelligibility in context-aware applications [Paper presentation]. Proceedings of the 11th International Conference on Ubiquitous Computing (pp. 195–204), Orlando, Florida, USA. https://doi.org/10.1145/1620545.1620576
   Google Scholar
• Lim, B. Y., & Dey, A. K. (2011). Design of an intelligible mobile context-aware application [Paper presentation]. Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (pp. 157–166), Stockholm, Sweden. https://doi.org/10.1145/2037373.2037399
   Google Scholar
• Lim, B. Y., Dey, A. K., & Avrahami, D. (2009). Why and why not explanations improve the intelligibility of context-aware intelligent systems [Paper presentation]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2119–2128), Boston, MA, USA. https://doi.org/10.1145/1518701.1519023
   Google Scholar
• Lim, B., Sarkar, A., Smith-Renner, A., & Stumpf, S. (2019 ). ExSS: Explainable smart systems 2019 [Paper presentation]. Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (pp. 125–126), Marina del Rey, California. https://doi.org/10.1145/3308557.3313112
   Google Scholar
• Lim, B., Yang, Q., Abdul, A., & Wang, D. (2019). Why these explanations? Selecting intelligibility types for explanation goals. IUI Workshops, Los Angeles, CA, USA.
   Google Scholar
• Lindberg, S., Wärnestål, P., Nygren, J., & Svedberg, P. (2014 ). Designing digital peer support for children: Design patterns for social interaction [Paper presentation]. Proceedings of the 2014 Conference on Interaction Design and Children (pp. 47–56), Aarhus, Denmark. https://doi.org/10.1145/2593968.2593972
   Google Scholar
• Lipton, Z. C. (2018). The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31–57. https://doi.org/10.1145/3236386.3241340
   Google Scholar
• Lombrozo, T. (2009). Explanation and categorization: How “why?” informs “what? Cognition, 110(2), 248–253. https://doi.org/10.1016/j.cognition.2008.10.007
   PubMed Web of Science ®Google Scholar
• Lou, Y., Caruana, R., & Gehrke, J. (2012). Intelligible models for classification and regression [Paper presentation]. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 150–158), Beijing, China. https://doi.org/10.1145/2339530.2339556
   Google Scholar
• Luger, E., & Sellen, A. (2016). ‘ Like having a really bad PA’: The Gulf between user expectation and experience of conversational agents [Paper presentation]. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 5286–5297), San Jose, California, USA. https://doi.org/10.1145/2858036.2858288
   Google Scholar
• Lyons, J. (2013). Being transparent about transparency: A Model for human-robot interaction. AAAI Spring Symposium: Trust and Autonomous Systems, Menlo Park, CA.
   Google Scholar
• Maaten, L., & van der Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605. https://www.jmlr.org/papers/v9/vandermaaten08a.html
   Google Scholar
• Marcinkowski, F., Kieslich, K., Starke, C., & Lünich, M. (2020). Implications of AI (un-)fairness in higher education admissions: The effects of perceived AI (un-)fairness on exit, voice and organizational reputation [Paper presentation]. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 122–130), Barcelona, Spain. https://doi.org/10.1145/3351095.3372867
   Google Scholar
• Markie, P. (2017). Rationalism vs. Empiricism. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Fall 2017). Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/fall2017/entries/rationalism-empiricism/
   Google Scholar
• Mars, N. J. I. (1995). Towards very large knowledge bases (1st ed.). IOS Press.
   Google Scholar
• Maurya, A. (2012). Running lean: Iterate from plan A to a plan that works (2nd ed.). O’Reilly Media.
   Google Scholar
• Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 115.
   Google Scholar
• Merat, N., Louw, T., Madigan, R., Wilbrink, M., & Schieben, A. (2018). What externally presented information do VRUs require when interacting with fully Automated Road Transport Systems in shared space? Accident; Analysis and Prevention, 118, 244–252. https://doi.org/10.1016/j.aap.2018.03.018
   PubMed Web of Science ®Google Scholar
• Mercado, J. E., Rupp, M. A., Chen, J. Y. C., Barnes, M. J., Barber, D., & Procci, K. (2016). Intelligent agent transparency in human–agent teaming for multi-UxV management. Human Factors, 58(3), 401–415. https://doi.org/10.1177/0018720815621206
   PubMed Web of Science ®Google Scholar
• Miglani, A., Diels, C., & Terken, J. (2016). Compatibility between trust and non-driving related tasks in UI design for highly and fully automated driving [Paper presentation]. Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 75–80), Ann Arbor, MI, USA. https://doi.org/10.1145/3004323.3004331
   Google Scholar
• Mikhail, B., Aleksei, M., & Ekaterina, S. (2018). On the way to legal framework for AI in public sector [Paper presentation]. Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance (pp. 682–684), Galway, Ireland. https://doi.org/10.1145/3209415.3209448
   Google Scholar
• Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007
   Web of Science ®Google Scholar
• Milton, S., Keen, C., & Kurnia, S. (2010). Understanding the benefits of ontology use for Australian industry: A conceptual study. ACIS 2010 Proceedings, Brisbane, Australia. https://aisel.aisnet.org/acis2010/64
   Google Scholar
• Mohseni, S., Zarei, N., & Ragan, E. D. (2018). A multidisciplinary survey and framework for design and evaluation of explainable AI systems. https://doi.org/10.48550/arXiv.1811.11839
   Google Scholar
• Montero, C. S., Alexander, J., Marshall, M. T., & Subramanian, S. (2010). Would you do that? Understanding social acceptance of gestural interfaces [Paper presentation]. Proceedings of the 12th International Conference on Human Computer Interaction with Mobile Devices and Services (pp. 275–278), Lisbon, Portugal. https://doi.org/10.1145/1851600.1851647
   Google Scholar
• Morland, D. V. (1983). Human factors guidelines for terminal interface design. Communications of the ACM, 26(7), 484–494. https://doi.org/10.1145/358150.358156
   Web of Science ®Google Scholar
• Murmann, P. (2018). Usable transparency for enhancing privacy in mobile health apps [Paper presentation]. Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (pp. 440–442), Barcelona, Spain. https://doi.org/10.1145/3236112.3236184
   Google Scholar
• Nowacka, D., & Kirk, D. (2014). Tangible autonomous interfaces (TAIs): Exploring autonomous behaviours in TUIs [Paper presentation]. Proceedings of the 8th International Conference on Tangible, Embedded and Embodied Interaction (pp. 1–8), Munich, Germany. https://doi.org/10.1145/2540930.2540942
   Google Scholar
• Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvintsev, A. (2018). The Building Blocks of Interpretability. Distill, 3(3). https://distill.pub/2018/building-blocks/ https://doi.org/10.23915/distill.00010
   Google Scholar
• Osterwalder, A. (2004). The business model ontology a proposition in a design science approach. Undefined. /paper/The-business-model-ontology-a-proposition-in-a-Osterwalder/87bbedf0efbf010515ed54086bdf31c7cb33e4a3
   Google Scholar
• Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers (1st ed.). John Wiley and Sons.
   Google Scholar
• Parra, D., Brusilovsky, P., & Trattner, C. (2014). See what you want to see: Visual user-driven approach for hybrid recommendation [Paper presentation]. Proceedings of the 19th International Conference on Intelligent User Interfaces (pp. 235–240), Haifa, Israel. https://doi.org/10.1145/2557500.2557542
   Google Scholar
• Pöppel, E. (1994). Temporal mechanisms in perception. International Review of Neurobiology, 37, 185–202. https://doi.org/10.1016/s0074-7742(08)60246-9
   PubMed Web of Science ®Google Scholar
• Porcheron, M., Fischer, J. E., Reeves, S., & Sharples, S. (2018). Voice interfaces in everyday life [Paper presentation]. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–12), Montreal, QC, Canada. https://doi.org/10.1145/3173574.3174214
   Google Scholar
• Prakken, H. (2017). On making autonomous vehicles respect traffic law: A case study for Dutch law [Paper presentation]. Proceedings of the 16th International Conference on Artificial Intelligence and Law (pp. 241–244), London, UK. https://doi.org/10.1145/3086512.3086542
   Google Scholar
• Pu, P., & Chen, L. (2006). Trust building with explanation interfaces [Paper presentation]. Proceedings of the 11th International Conference on Intelligent User Interfaces (pp. 93–100), Sydney, Australia. https://doi.org/10.1145/1111449.1111475
   Google Scholar
• Raweni, A. M., & Majstorović, V. D. (2017). ISO CERTIFICATIONS DIFFUSION IN EUROPEAN COUNTRIES 2007–2014 AND FORECASTINNG FOR 2022-STARE OF THE ART. International Journal “Advanced Quality”, 44(1), 53–58. https://doi.org/10.25137/IJAQ.n1.v44.y2016.p53-58
   Google Scholar
• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Nothing else matters: Model-agnostic explanations by identifying prediction invariance. ArXiv, arXiv:1611.05817 [stat.ML].
   Google Scholar
• Ribeiro, M., Singh, S., & Guestrin, C. (2016). “ Why should i trust you?”: Explaining the predictions of any classifier [Paper presentation]. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations (pp. 97–101), San Diego, California. https://doi.org/10.18653/v1/N16-3020
   Google Scholar
• Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice: A guide to assessing scientific models. Halsted Press.
   Google Scholar
• Schaefer, K. E., Chen, J. Y. C., Szalma, J. L., & Hancock, P. A. (2016). A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human Factors, 58(3), 377–400. https://doi.org/10.1177/0018720816634228
   PubMed Web of Science ®Google Scholar
• Schaffer, J., O’Donovan, J., Michaelis, J., Raglin, A., & Höllerer, T. (2019). I can do better than your AI: Expertise and explanations [Paper presentation]. Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 240–251), Marina del Rey, California. https://doi.org/10.1145/3301275.3302308
   Google Scholar
• Schlesinger, A., O’Hara, K. P., & Taylor, A. S. (2018). Let’s talk about race: Identity, chatbots, and AI [Paper presentation]. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–14), Montreal, QC, Canada. https://doi.org/10.1145/3173574.3173889
   Google Scholar
• Selkowitz, A. R., Lakhmani, S. G., Larios, C. N., & Chen, J. Y. C. (2016). Agent transparency and the autonomous squad member. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60(1), 1319–1323. https://doi.org/10.1177/1541931213601305
   Google Scholar
• Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization [Paper presentation]. 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 618–626), Venice, Italy. https://doi.org/10.1109/ICCV.2017.74
   Google Scholar
• Sendak, M., Elish, M. C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., Bedoya, A., Balu, S., & O’Brien, C. (2020). ‘ The human body is a black box’: Supporting clinical decision-making with deep learning [Paper presentation]. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 99–109), Barcelona, Spain. https://doi.org/10.1145/3351095.3372827
   Google Scholar
• Shishehchi, S., Banihashem, S. Y., & Zin, N. A. M. (2010). A proposed semantic recommendation system for e-learning: A rule and ontology based e-learning recommendation system. Proceedings of the 2010 International Symposium on Information Technology (pp. 1–5). https://doi.org/10.1109/ITSIM.2010.5561329
   Google Scholar
• Shneiderman, B. (2020). Bridging the gap between ethics and practice: Guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems, 10(4), 1–31. https://doi.org/10.1145/3419764
   Web of Science ®Google Scholar
• Simonyan, K., Vedaldi, A., & Zisserman, A. (2014). Deep inside convolutional networks: visualising image classification models and saliency maps. ICLR.
   Google Scholar
• Springer, A., & Whittaker, S. (2019). Progressive disclosure: Empirically motivated approaches to designing effective transparency [Paper presentation]. Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 107–120), Marina del Rey, California. https://doi.org/10.1145/3301275.3302322
   Google Scholar
• Springer, A., & Whittaker, S. (2020). Progressive disclosure: When, why, and how do users want algorithmic transparency information? ACM Transactions on Interactive Intelligent Systems, 10(4), 1–32. https://doi.org/10.1145/3374218
   Web of Science ®Google Scholar
• Tam, N. T., Huy, N. T., Thoa, L. T. B., Long, N. P., Trang, N. T. H., Hirayama, K., & Karbwang, J. (2015). Participants’ understanding of informed consent in clinical trials over three decades: Systematic review and meta-analysis. Bulletin of the World Health Organization, 93(3), 186–198H. https://doi.org/10.2471/BLT.14.141390
   PubMed Web of Science ®Google Scholar
• Tan, S., Caruana, R., Hooker, G., Koch, P., & Gordo, A. (2018). Learning global additive explanations for neural nets using model distillation.
   Google Scholar
• Tang, A., Finke, M., Blackstock, M., Leung, R., Deutscher, M., & Lea, R. (2008). Designing for bystanders: Reflections on building a public digital forum [Paper presentation]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 879–882), Florence, Italy. https://doi.org/10.1145/1357054.1357193
   Google Scholar
• Thelisson, E., Sharma, K., Salam, H., & Dignum, V. (2018). The general data protection regulation: An opportunity for the HCI community? Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–8), Montreal, QC, Canada. https://doi.org/10.1145/3170427.3170632
   Google Scholar
• Theodorou, A., Wortham, R. H., & Bryson, J. J. (2017). Designing and implementing transparency for real time inspection of autonomous robots. Connection Science, 29(3), 230–241. https://doi.org/10.1080/09540091.2017.1310182
   Web of Science ®Google Scholar
• Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
   PubMed Web of Science ®Google Scholar
• Tullio, J., Dey, A. K., Chalecki, J., & Fogarty, J. (2007). How it works: A field study of non-technical users interacting with an intelligent system [Paper presentation]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 31–40), San Jose, California, USA. https://doi.org/10.1145/1240624.1240630
   Google Scholar
• Turek, M. (2016). Explainable artificial intelligence. https://www.darpa.mil/program/explainable-artificial-intelligence
   Google Scholar
• Uschold, M., & Gruninger, M. (1996). Ontologies: Principles, methods and applications. The Knowledge Engineering Review, 11(2), 93–136. https://doi.org/10.1017/S0269888900007797
   Web of Science ®Google Scholar
• van Capelleveen, G., Amrit, C., Yazan, D. M., & Zijm, H. (2019). The recommender canvas: A model for developing and documenting recommender system design. Expert Systems with Applications, 129, 97–117. https://doi.org/10.1016/j.eswa.2019.04.001
   Web of Science ®Google Scholar
• Van Mechelen, M., Baykal, G. E., Dindler, C., Eriksson, E., & Iversen, O. S. (2020). 18 Years of ethics in child-computer interaction research: A systematic literature review [Paper presentation]. Proceedings of the Interaction Design and Children Conference (pp. 161–183), London, UK. https://doi.org/10.1145/3392063.3394407
   Google Scholar
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 6000–6010). arXiv:1706.03762
   Google Scholar
• Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1–14). Association for Computing Machinery. https://doi.org/10.1145/3173574.3174014
   Google Scholar
• Verbert, K., Parra, D., Brusilovsky, P., & Duval, E. (2013). Visualizing recommendations to support exploration, transparency and controllability [Paper presentation]. Proceedings of the 2013 International Conference on Intelligent User Interfaces (pp. 351–362), Santa Monica, California, USA. https://doi.org/10.1145/2449396.2449442
   Google Scholar
• Vermeulen, J., Vanderhulst, G., Luyten, K., & Coninx, K. (2010). PervasiveCrystal: Asking and answering why and why not questions about pervasive computing applications [Paper presentation]. 2010 Sixth International Conference on Intelligent Environments (pp. 271–276), Kuala Lumpur, Malaysia. https://doi.org/10.1109/IE.2010.56
   Google Scholar
• Vines, J., McNaney, R., Clarke, R., Lindsay, S., McCarthy, J., Howard, S., Romero, M., & Wallace, J. (2013). Designing for- and with- vulnerable people [Paper presentation]. CHI ’13 Extended Abstracts on Human Factors in Computing Systems (pp. 3231–3234), Paris, France. https://doi.org/10.1145/2468356.2479654
   Google Scholar
• Vlachos, M., & Svonava, D. (2012). Graph embeddings for movie visualization and recommendation. /paper/Graph-Embeddings-for-Movie-Visualization-and-Vlachos-Svonava/1d8273a4276682067ac6de0bffb4609721e63bed
   Google Scholar
• Wachter, S., Mittelstadt, B., & Russell, C. (2018). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. ArXiv:1711.00399 [Cs]. http://arxiv.org/abs/1711.00399
   Google Scholar
• Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019). Designing Theory-driven user-centric explainable AI [Paper presentation]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–15), Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300831
   Google Scholar
• Wang, L., Jamieson, G. A., & Hollands, J. G. (2009). Trust and reliance on an automated combat identification system. Human Factors, 51(3), 281–291. https://doi.org/10.1177/0018720809338842
   PubMed Web of Science ®Google Scholar
• Wang, N., Pynadath, D. V., & Hill, S. G. (2016). The impact of POMDP-generated explanations on trust and performance in human-robot teams [Paper presentation]. Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (pp. 997–1005), Singapore, Singapore.
   Google Scholar
• Wang, W., & Benbasat, I. (2007). Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems, 23(4), 217–246. https://doi.org/10.2753/MIS0742-1222230410
   Web of Science ®Google Scholar
• Wang, X., Liu, S., Liu, J., Chen, J., Zhu, J., & Guo, B. (2016). TopicPanorama: A full picture of relevant topics. IEEE Transactions on Visualization and Computer Graphics, 22(12), 2508–2521. https://doi.org/10.1109/TVCG.2016.2515592
   PubMed Web of Science ®Google Scholar
• Weitz, K., Schiller, D., Schlagowski, R., Huber, T., & André, E. (2021). “Let me explain!”: Exploring the potential of virtual agents in explainable AI interaction design. Journal on Multimodal User Interfaces, 15, 87–98. https://doi.org/10.1007/s12193-020-00332-0
   Web of Science ®Google Scholar
• Weller, A. (2019). Transparency: Motivations and challenges. ArXiv:1708.01870 [Cs]. http://arxiv.org/abs/1708.01870
   Google Scholar
• West, S. M. (2019). Data capitalism: Redefining the logics of surveillance and privacy. Business & Society, 58(1), 20–41. https://doi.org/10.1177/0007650317718185
   Web of Science ®Google Scholar
• Wood, S. N. (2006). Generalized additive models: An introduction with R (1st ed.). Chapman and Hall/CRC.
   Google Scholar
• Woodruff, A., Fox, S. E., Rousso-Schindler, S., & Warshaw, J. (2018). A qualitative exploration of perceptions of algorithmic fairness [Paper presentation]. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–14), Montreal, QC, Canada. https://doi.org/10.1145/3173574.3174230
   Google Scholar
• Wouters, N., Kelly, R., Velloso, E., Wolf, K., Ferdous, H. S., Newn, J., Joukhadar, Z., & Vetere, F. (2019). Biometric mirror: Exploring ethical opinions towards facial analysis and automated decision-making [Paper presentation]. Proceedings of the 2019 on Designing Interactive Systems Conference (pp. 447–461), San Diego, CA, USA. https://doi.org/10.1145/3322276.3322304
   Google Scholar
• Wright, J. L., Chen, J. Y. C., & Lakhmani, S. G. (2020). Agent transparency and reliability in human–robot interaction: The influence on user confidence and perceived reliability. IEEE Transactions on Human-Machine Systems, 50(3), 254–263. https://doi.org/10.1109/THMS.2019.2925717
   Web of Science ®Google Scholar
• Wright, J. L., Chen, J. Y. C., Barnes, M. J., & Boyce, M. W. (2015). The effects of information level on human-agent interaction for route planning. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 811–815. https://doi.org/10.1177/1541931215591247
   Google Scholar
• Wu, B., Xu, C., Dai, X., Wan, A., Zhang, P., Yan, Z., Tomizuka, M., Gonzalez, J., Keutzer, K., & Vajda, P. (2020). Visual transformers: Token-based image representation and processing for computer vision. ArXiv:2006.03677 [Cs, Eess]. http://arxiv.org/abs/2006.03677
   Google Scholar
• Wu, H. C., Luk, R. W. P., Wong, K. F., & Kwok, K. L. (2008). Interpreting TF-IDF term weights as making relevance decisions. ACM Transactions on Information Systems, 26(3), 1–37. https://doi.org/10.1145/1361684.1361686
   Web of Science ®Google Scholar
• Yang, C., Rangarajan, A., & Ranka, S. (2018). Global model interpretation via recursive partitioning [Paper presentation]. 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 1563–1570), Exeter, UK. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00256
   Google Scholar
• Yang, Q., Banovic, N., & Zimmerman, J. (2018). Mapping machine learning advances from hci research to reveal starting places for design innovation [Paper presentation]. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI, 18 (pp. 1–11), Montreal, QC, Canada. https://doi.org/10.1145/3173574.3173704
   Google Scholar
• Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020). Re-examining whether, why, and how human-ai interaction is uniquely difficult to design [Paper presentation]. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–13), Honolulu, HI, USA. https://doi.org/10.1145/3313831.3376301
   Google Scholar
• Yao, Y., Basdeo, J. R., Mcdonough, O. R., & Wang, Y. (2019). Privacy perceptions and designs of bystanders in smart homes. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–24. https://doi.org/10.1145/3359161
   PubMedGoogle Scholar
• Yu, Z., Nakamura, Y., Jang, S., Kajita, S., & Mase, K. (2007). Ontology-based semantic recommendation for context-aware E-learning. In J. Indulska, J. Ma, L. T. Yang, T. Ungerer, & J. Cao (Eds.), Ubiquitous intelligence and computing (pp. 898–907). Springer. https://doi.org/10.1007/978-3-540-73549-6_88
   Google Scholar
• Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer vision – ECCV 2014 (pp. 818–833). Springer International Publishing. https://doi.org/10.1007/978-3-319-10590-1_53
   Google Scholar
• Zhang, Q., & Zhu, S. (2018). Visual interpretability for deep learning: A survey. Frontiers of Information Technology & Electronic Engineering, 19(1), 27–39. https://doi.org/10.1631/FITEE.1700808
   Web of Science ®Google Scholar
• Zhang, Y., Bellamy, R., & Varshney, K. (2020). Joint optimization of AI fairness and utility: A human-centered approach [Paper presentation]. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 400–406), New York, NY, USA. https://doi.org/10.1145/3375627.3375862
   Google Scholar
• Zhao, E., & Sukkerd, R. (2019). Interactive explanation for planning-based systems: WIP abstract [Paper presentation]. Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems (pp. 322–323), Montreal, Quebec, Canada. https://doi.org/10.1145/3302509.3313322
   Google Scholar
• Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. (2015). Object detectors emerge in deep scene CNNs. ICLR.
   Google Scholar
• Zhou, J., & Chen, F. (2015). Making machine learning useable. International Journal of Intelligent Systems Technologies and Applications, 14(2), 91–109. https://doi.org/10.1504/IJISTA.2015.074069
   Google Scholar