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What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

Ferreira, Juliana J., Monteiro, Mateus S.·2020·Lecture Notes in Computer Science

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  1. Barria-Pineda, J., Brusilovsky, P.: Making educational recommendations transparent through a fine-grained open learner model. In: IUI Workshops (2019) · DOI
  2. Belle, V.: Logic meets probability: towards explainable AI systems for uncertain worlds. In: IJCAI, pp. 5116–5120 (2017) · DOI
  3. Benjamin, J.J., Müller-Birn, C.: Materializing interpretability: exploring meaning in algorithmic systems. In: Companion Publication of the 2019 on Designing Interactive Systems Conference 2019 Companion, pp. 123–127. ACM (2019) · DOI
  4. Bhatia, A., Garg, V., Haves, P., Pudi, V.: Explainable clustering using hyper-rectangles for building energy simulation data. In: IOP Conference Series: Earth and Environmental Science, vol. 238, p. 012068. IOP Publishing (2019) · DOI
  5. Browne, J.T.: Wizard of OZ prototyping for machine learning experiences. In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, p. LBW2621. ACM (2019) · DOI
  6. Cabitza, F., Campagner, A., Ciucci, D.: New frontiers in explainable AI: understanding the GI to interpret the GO. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2019. LNCS, vol. 11713, pp. 27–47. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29726-8_3 · DOI
  7. Chander, A., Srinivasan, R., Chelian, S., Wang, J., Uchino, K.: Working with beliefs: AI transparency in the enterprise. In: IUI Workshops (2018)
  8. Charleer, S., Gutiérrez, F., Verbert, K.: Supporting job mediator and job seeker through an actionable dashboard. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 121–131 (2019) · DOI
  9. Chen, L., Wang, F.: Explaining recommendations based on feature sentiments in product reviews. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces, pp. 17–28. ACM (2017) · DOI
  10. Chromik, M., Eiband, M., Völkel, S.T., Buschek, D.: Dark patterns of explainability, transparency, and user control for intelligent systems. In: IUI Workshops (2019)
  11. Clewley, N., Dodd, L., Smy, V., Witheridge, A., Louvieris, P.: Eliciting expert knowledge to inform training design. In: Proceedings of the 31st European Conference on Cognitive Ergonomics, pp. 138–143 (2019) · DOI
  12. Datta, A., Sen, S., Zick, Y.: Algorithmic transparency via quantitative input influence: theory and experiments with learning systems. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 598–617. IEEE (2016) · DOI · Paper page
  13. Di Castro, F., Bertini, E.: Surrogate decision tree visualization interpreting and visualizing black-box classification models with surrogate decision tree. In: CEUR Workshop Proceedings, vol. 2327 (2019)
  14. Dimitrova, R., Majumdar, R., Prabhu, V.S.: Causality analysis for concurrent reactive systems. arXiv preprint arXiv:1901.00589 (2019) · DOI
  15. Ding, L.: Human knowledge in constructing AI systems-neural logic networks approach towards an explainable AI. Procedia Comput. Sci. 126, 1561–1570 (2018) · DOI
  16. Dodge, J., Penney, S., Anderson, A., Burnett, M.M.: What should be in an XAI explanation? what IFT reveals. In: IUI Workshops (2018)
  17. Dominguez, V., Messina, P., Donoso-Guzmán, I., Parra, D.: The effect of explanations and algorithmic accuracy on visual recommender systems of artistic images. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 408–416. ACM (2019) · DOI
  18. Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable AI and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274. ACM (2019) · DOI · Paper page
  19. Eiband, M., Schneider, H., Buschek, D.: Normative vs. pragmatic: two perspectives on the design of explanations in intelligent systems. In: IUI Workshops (2018)
  20. Eisenstadt, V., Espinoza-Stapelfeld, C., Mikyas, A., Althoff, K.-D.: Explainable distributed case-based support systems: patterns for enhancement and validation of design recommendations. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 78–94. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_6 · DOI
  21. Eisenstadt, V., Langenhan, C., Althoff, K.-D.: FLEA-CBR – a flexible alternative to the classic 4R cycle of case-based reasoning. In: Bach, K., Marling, C. (eds.) ICCBR 2019. LNCS (LNAI), vol. 11680, pp. 49–63. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29249-2_4 · DOI
  22. Escalante, H.J., et al.: Design of an explainable machine learning challenge for video interviews. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3688–3695. IEEE (2017) · DOI
  23. Finkbeiner, B., Kleinberg, S.: Proceedings 3rd workshop on formal reasoning about causation, responsibility, and explanations in science and technology. arXiv preprint arXiv:1901.00073 (2019) · DOI
  24. Garcia, R., Telea, A.C., da Silva, B.C., Tørresen, J., Comba, J.L.D.: A task-and-technique centered survey on visual analytics for deep learning model engineering. Comput. Graph. 77, 30–49 (2018) · DOI
  25. Gervasio, M.T., Myers, K.L., Yeh, E., Adkins, B.: Explanation to avert surprise. In: IUI Workshops, vol. 2068 (2018)
  26. Gorzałczany, M.B., Rudziński, F.: Interpretable and accurate medical data classification-a multi-objective genetic-fuzzy optimization approach. Expert Syst. Appl. 71, 26–39 (2017) · DOI
  27. Grigsby, S.S.: Artificial intelligence for advanced human-machine symbiosis. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) AC 2018. LNCS (LNAI), vol. 10915, pp. 255–266. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91470-1_22 · DOI
  28. Guo, K., Pratt, D., MacDonald III, A., Schrater, P.: Labeling images by interpretation from natural viewing. In: IUI Workshops (2018)
  29. Guzdial, M., Reno, J., Chen, J., Smith, G., Riedl, M.: Explainable PCGML via game design patterns. arXiv preprint arXiv:1809.09419 (2018) · arXiv
  30. Hamidi-Haines, M., Qi, Z., Fern, A., Li, F., Tadepalli, P.: Interactive naming for explaining deep neural networks: a formative study. arXiv preprint arXiv:1812.07150 (2018) · arXiv
  31. Hepenstal, S., Kodagoda, N., Zhang, L., Paudyal, P., Wong, B.W.: Algorithmic transparency of conversational agents. In: IUI Workshops (2019)
  32. Hohman, F., Head, A., Caruana, R., DeLine, R., Drucker, S.M.: Gamut: a design probe to understand how data scientists understand machine learning models. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, p. 579. ACM (2019) · DOI
  33. Hohman, F.M., Kahng, M., Pienta, R., Chau, D.H.: Visual analytics in deep learning: an interrogative survey for the next frontiers. IEEE Trans. Vis. Comput. Graph. 25(8), 2674–2693 (2018) · DOI · Paper page
  34. Ishii, K.: Comparative legal study on privacy and personal data protection for robots equipped with artificial intelligence: looking at functional and technological aspects. AI Soc. 34, 1–25 (2017)
  35. Jain, A., Keller, J., Popescu, M.: Explainable AI for dataset comparison. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7. IEEE (2019) · DOI
  36. Jentzsch, S.F., Höhn, S., Hochgeschwender, N.: Conversational interfaces for explainable AI: a human-centred approach. In: Calvaresi, D., Najjar, A., Schumacher, M., Främling, K. (eds.) EXTRAAMAS 2019. LNCS (LNAI), vol. 11763, pp. 77–92. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30391-4_5 · DOI
  37. Kampik, T., Nieves, J.C., Lindgren, H.: Explaining sympathetic actions of rational agents. In: Calvaresi, D., Najjar, A., Schumacher, M., Främling, K. (eds.) EXTRAAMAS 2019. LNCS (LNAI), vol. 11763, pp. 59–76. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30391-4_4 · DOI
  38. Krebs, L.M., et al.: Tell me what you know: GDPR implications on designing transparency and accountability for news recommender systems. In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, p. LBW2610. ACM (2019) · DOI
  39. Krishnan, J., Coronado, P., Reed, T.: SEVA: a systems engineer’s virtual assistant. In: AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering (2019)
  40. Kwon, B.C., et al.: RetainVis: visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Trans. Vis. Comput. Graph. 25(1), 299–309 (2018) · DOI · Paper page
  41. Lee, O.J., Jung, J.J.: Explainable movie recommendation systems by using story-based similarity. In: IUI Workshops (2018)
  42. Lim, B.Y., Wang, D., Loh, T.P., Ngiam, K.Y.: Interpreting intelligibility under uncertain data imputation. In: IUI Workshops (2018)
  43. Lim, B.Y., Yang, Q., Abdul, A.M., Wang, D.: Why these explanations? selecting intelligibility types for explanation goals. In: IUI Workshops (2019)
  44. Loi, D., Wolf, C.T., Blomberg, J.L., Arar, R., Brereton, M.: Co-designing AI futures: Integrating AI ethics, social computing, and design. In: A Companion Publication of the 2019 on Designing Interactive Systems Conference 2019 Companion, pp. 381–384. ACM (2019) · DOI · Paper page
  45. Magdalena, L.: Semantic interpretability in hierarchical fuzzy systems: creating semantically decouplable hierarchies. Inf. Sci. 496, 109–123 (2019) · DOI
  46. Meacham, S., Isaac, G., Nauck, D., Virginas, B.: Towards explainable AI: design and development for explanation of machine learning predictions for a patient readmittance medical application. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) CompCom 2019. AISC, vol. 997, pp. 939–955. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22871-2_67 · DOI
  47. Millecamp, M., Htun, N.N., Conati, C., Verbert, K.: To explain or not to explain: the effects of personal characteristics when explaining music recommendations. In: IUI, pp. 397–407 (2019) · DOI · Paper page
  48. Ming, Y., Qu, H., Bertini, E.: RuleMatrix: visualizing and understanding classifiers with rules. IEEE Trans. Vis. Comput. Graph. 25(1), 342–352 (2018) · DOI · Paper page
  49. Montavon, G., Samek, W., Müller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Proc. 73, 1–15 (2018) · DOI · Paper page
  50. Montenegro, J.L.Z., da Costa, C.A., Righi, R.D.R.: Survey of conversational agents in health. Expert Syst. Appl. 129, 56–67 (2019). https://doi.org/10.1016/j.eswa.2019.03.054. http://www.sciencedirect.com/science/article/pii/S0957417419302283 · DOI · Paper page
  51. Nassar, M., Salah, K., ur Rehman, M.H., Svetinovic, D.: Blockchain for explainable and trustworthy artificial intelligence. Wiley Interdisc. Rev.: Data Min. Knowl. Discovery 10(1), e1340 (2020) · DOI
  52. Neerincx, M.A., van der Waa, J., Kaptein, F., van Diggelen, J.: Using perceptual and cognitive explanations for enhanced human-agent team performance. In: Harris, D. (ed.) EPCE 2018. LNCS (LNAI), vol. 10906, pp. 204–214. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91122-9_18 · DOI
  53. Nguyen, A.T., et al.: Believe it or not: designing a human-AI partnership for mixed-initiative fact-checking. In: The 31st Annual ACM Symposium on User Interface Software and Technology, pp. 189–199. ACM (2018) · DOI
  54. Nguyen, A.T., Lease, M., Wallace, B.C.: Explainable modeling of annotations in crowdsourcing. In: IUI, pp. 575–579 (2019) · DOI
  55. Nguyen, A.T., Lease, M., Wallace, B.C.: Mash: software tools for developing interactive and transparent machine learning systems. In: IUI Workshops (2019)
  56. Nunes, I., Jannach, D.: A systematic review and taxonomy of explanations in decision support and recommender systems. User Model. User-Adap. Inter. 27(3–5), 393–444 (2017) · DOI
  57. Olszewska, J.I.: Designing transparent and autonomous intelligent vision systems. In: Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART), pp. 850–856 (2019) · DOI
  58. van Oosterhout, A.: Understanding the benefits and drawbacks of shape change in contrast or addition to other modalities. In: Companion Publication of the 2019 on Designing Interactive Systems Conference 2019 Companion, pp. 113–116. ACM (2019) · DOI
  59. Paudyal, P., Lee, J., Kamzin, A., Soudki, M., Banerjee, A., Gupta, S.K.: Learn2sign: explainable AI for sign language learning. In: IUI Workshops (2019)
  60. Ribera, M., Lapedriza, À.: Can we do better explanations? A proposal of user-centered explainable AI. In: IUI Workshops (2019)
  61. Rotsidis, A., Theodorou, A., Wortham, R.H.: Robots that make sense: transparent intelligence through augmented reality. In: IUI Workshops (2019)
  62. Santos, T.I., Abel, A.: Using feature visualisation for explaining deep learning models in visual speech. In: 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), pp. 231–235, March 2019. https://doi.org/10.1109/ICBDA.2019.8713256 · DOI
  63. Schmidmaier, M., Han, Z., Weber, T., Liu, Y., Hußmann, H.: Real-time personalization in adaptive ides (2019) · DOI
  64. Schuessler, M., Weiß, P.: Minimalistic explanations: capturing the essence of decisions. arXiv preprint arXiv:1905.02994 (2019) · DOI
  65. Sellam, T., Lin, K., Huang, I., Yang, M., Vondrick, C., Wu, E.: DeepBase: deep inspection of neural networks. In: Proceedings of the 2019 International Conference on Management of Data, pp. 1117–1134 (2019) · DOI
  66. Singh, M., Martins, L.M., Joanis, P., Mago, V.K.: Building a cardiovascular disease predictive model using structural equation model & fuzzy cognitive map. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1377–1382. IEEE (2016) · DOI
  67. Sliwinski, J., Strobel, M., Zick, Y.: An axiomatic approach to linear explanations in data classification. In: IUI Workshops (2018)
  68. Smith, A., Nolan, J.: The problem of explanations without user feedback. In: IUI Workshops (2018)
  69. Smith-Renner, A., Rua, R., Colony, M.: Towards an explainable threat detection tool. In: IUI Workshops (2019)
  70. Sokol, K., Flach, P.A.: Conversational explanations of machine learning predictions through class-contrastive counterfactual statements. In: IJCAI, pp. 5785–5786 (2018) · DOI
  71. Stumpf, S.: Horses for courses: making the case for persuasive engagement in smart systems. In: Joint Proceedings of the ACM IUI 2019 Workshops, vol. 2327. CEUR (2019)
  72. Stumpf, S., Skrebe, S., Aymer, G., Hobson, J.: Explaining smart heating systems to discourage fiddling with optimized behavior. In: CEUR Workshop Proceedings, vol. 2068 (2018)
  73. Sundararajan, M., Xu, J., Taly, A., Sayres, R., Najmi, A.: Exploring principled visualizations for deep network attributions. In: IUI Workshops (2019)
  74. Theodorou, A., Wortham, R.H., Bryson, J.J.: Designing and implementing transparency for real time inspection of autonomous robots. Connect. Sci. 29(3), 230–241 (2017) · DOI
  75. Tsai, C.H., Brusilovsky, P.: Explaining social recommendations to casual users: design principles and opportunities. In: Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion, p. 59. ACM (2018) · DOI
  76. Tsai, C.H., Brusilovsky, P.: Designing explanation interfaces for transparency and beyond. In: IUI Workshops (2019)
  77. Vijay, A., Umadevi, K.: Secured AI guided architecture for D2D systems of massive MIMO deployed in 5G networks. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 468–472. IEEE (2019) · DOI
  78. Wang, D., Yang, Q., Abdul, A., Lim, B.Y.: Designing theory-driven user-centric explainable AI. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, p. 601. ACM (2019) · DOI · Paper page
  79. Wang, Q., et al.: ATMSeer: increasing transparency and controllability in automated machine learning. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, p. 681. ACM (2019) · DOI
  80. Wang, X., Chen, Y., Yang, J., Wu, L., Wu, Z., Xie, X.: A reinforcement learning framework for explainable recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 587–596. IEEE (2018) · DOI · Paper page
  81. Wolf, C.T., Blomberg, J.: Explainability in context: lessons from an intelligent system in the it services domain. In: IUI Workshops (2019) · DOI
  82. Xie, Y., Gao, G., Chen, X.: Outlining the design space of explainable intelligent systems for medical diagnosis. arXiv preprint arXiv:1902.06019 (2019) · arXiv
  83. Yang, Q., Banovic, N., Zimmerman, J.: Mapping machine learning advances from HCI research to reveal starting places for design innovation. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p. 130. ACM (2018) · DOI
  84. Yeganejou, M., Dick, S.: Improved deep fuzzy clustering for accurate and interpretable classifiers. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7. IEEE (2019) · DOI
  85. Zhao, R., Benbasat, I., Cavusoglu, H.: Transparency in advice-giving systems: a framework and a research model for transparency provision. In: IUI Workshops (2019)
  86. Zheng, X.l., Zhu, M.Y., Li, Q.B., Chen, C.C., Tan, Y.C.: FinBrain: when finance meets AI 2.0. Front. Inf. Technol. Electron. Eng. 20(7), 914–924 (2019) · DOI
  87. Zhou, J., et al.: Effects of influence on user trust in predictive decision making. In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–6 (2019) · DOI
  88. Zhu, J., Liapis, A., Risi, S., Bidarra, R., Youngblood, G.M.: Explainable AI for designers: a human-centered perspective on mixed-initiative co-creation. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2018) · DOI · Paper page

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Metadata

Authors
Ferreira, J. J., & Monteiro, M. S.
Venue
Lecture Notes in Computer Science
Published
2020
Pages
56-73
ISSN
  • 0302-9743
  • 1611-3349
Cited by
68
References
8
Language
en

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