{"type":"ScholarlyArticle","title":"ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments","authors":[{"name":"Sören Schleibaum","slug":"soren-schleibaum","given":"Sören","family":"Schleibaum","affiliation":"Clausthal University of Technology"},{"name":"Lu Feng","slug":"lu-feng","given":"Lu","family":"Feng","affiliation":"University of Virginia"},{"name":"Sarit Kraus","slug":"sarit-kraus","given":"Sarit","family":"Kraus","affiliation":"Bar-Ilan University"},{"name":"Jörg P. Müller","slug":"jorg-p-muller","given":"Jörg P.","family":"Müller","affiliation":"Clausthal University of Technology"}],"abstract":"In the evolving landscape of human-centered AI, fostering a synergistic relationship between humans and AI agents in decision-making processes stands as a paramount challenge. This work considers a problem setup where an intelligent agent comprising a neural network-based prediction component and a deep reinforcement learning component provides advice to a human decision-maker in complex repeated decision-making environments. Whether the human decision-maker would follow the agent's advice depends on their beliefs and trust in the agent and on their understanding of the advice itself. To this end, we developed an approach named ADESSE to generate explanations about the adviser agent to improve human trust and decision-making. Computational experiments on a range of environments with varying model sizes demonstrate the applicability and scalability of ADESSE. Furthermore, an interactive game-based user study shows that participants were significantly more satisfied, achieved a higher reward in the game, and took less time to select an action when presented with explanations generated by ADESSE. These findings illuminate the critical role of tailored, human-centered explanations in AI-assisted decision-making.","doi":"10.24963/ijcai.2024/875","url":"https://ijcai.org/proceedings/2024/875","pdf":"https://ijcai.org/proceedings/2024/0875.pdf","markdown":"https://ijcai.org/proceedings/2024/875.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/875","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Human-Centred AI","track":"Human-Centred AI","number":875,"pages":{"first":7904,"last":7912},"keywords":["Humans and AI: HAI: Human-AI collaboration","Machine Learning: ML: Reinforcement learning","Machine Learning: ML: Explainable/Interpretable machine learning","AI Ethics, Trust, Fairness: ETF: Explainability and interpretability"],"videos":[],"references":[{"position":1,"raw":"[Adadi and Berrada, 2018] Amina Adadi and Mohammed Berrada. Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE access, 6:52138-52160, 2018.","title":"Amina Adadi and Mohammed Berrada. Peeking inside the black-box: a survey on explainable artificial intelligence (xai)","authors":"Berrada Adadi","year":"2018","venue":"IEEE access","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Alshehri et al., 2023] Abeer Alshehri, Tim Miller, and Mor Vered. Explainable goal recognition: a framework based on weight of evidence. In Proceedings of the Thirty- Third International Conference on Automated Planning and Scheduling, ICAPS '23. AAAI Press, 2023.","title":"Explainable goal recognition: a framework based on weight of evidence","authors":"Abeer Alshehri, Tim Miller, Mor Vered","year":"2023","venue":"Proceedings of the Thirty-Third International Conference on Automated Planning and Scheduling, ICAPS '23","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Amir and Amir, 2018] Dan Amir and Ofra Amir. High- lights: Summarizing agent behavior to people. In Proceed- ings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, pages 1168-1176, 2018.","title":"Highlights: Summarizing agent behavior to people","authors":"Amir Amir, Dan Amir, Ofra Amir","year":"2018","venue":"Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Anjomshoae et al., 2019] Sule Anjomshoae, Amro Najjar, Davide Calvaresi, and Kary Främling. Explainable agents and robots: Results from a systematic literature review. In 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, May 13-17, 2019, pages 1078-1088. International Foun- dation for Autonomous Agents and Multiagent Systems, 2019.","title":"Explainable agents and robots: Results from a systematic literature review","authors":"Sule Anjomshoae, Amro Najjar, Davide Calvaresi, Kary Främling","year":"2019","venue":"18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Boggess et al., 2022] Kayla Boggess, Sarit Kraus, and Lu Feng. Toward policy explanations for multi-agent rein- forcement learning. In International Joint Conference on Artificial Intelligence (IJCAI), 2022.","title":"Toward policy explanations for multi-agent reinforcement learning","authors":"Kayla Boggess, Sarit Kraus, Lu Feng","year":"2022","venue":"International Joint Conference on Artificial Intelligence (IJCAI)","doi":null,"url":null,"internal_url":"/proceedings/2022/16"},{"position":6,"raw":"[Boggess et al., 2023] Kayla Boggess, Sarit Kraus, and Lu Feng. Explainable multi-agent reinforcement learn- ing for temporal queries. In Proceedings of the Thirty- Second International Joint Conference on Artificial Intel- ligence (IJCAI), 2023.","title":"Explainable multi-agent reinforcement learning for temporal queries","authors":"Kayla Boggess, Sarit Kraus, Lu Feng","year":"2023","venue":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI)","doi":null,"url":null,"internal_url":"/proceedings/2023/7"},{"position":7,"raw":"[Chakraborti et al., 2020] Tathagata Chakraborti, Sarath Sreedharan, and Subbarao Kambhampati. The emerging landscape of explainable automated planning & decision making. In IJCAI, pages 4803-4811, 2020.","title":"The emerging landscape of explainable automated planning & decision making","authors":"Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati","year":"2020","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2020/669"},{"position":8,"raw":"[Emerson et al., 2023] Harry Emerson, Matthew Guy, and Ryan McConville. Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes. Jour- nal of Biomedical Informatics, 142:104376, 2023.","title":"Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes","authors":"Emerson","year":"2023","venue":"Journal of Biomedical Informatics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Farazi et al., 2021] Nahid Parvez Farazi, Bo Zou, Tanvir Ahamed, and Limon Barua. Deep reinforcement learn- ing in transportation research: A review. Transportation research interdisciplinary perspectives, 11:100425, 2021.","title":"Deep reinforcement learning in transportation research: A review","authors":"Nahid Parvez Farazi, Bo Zou, Tanvir Ahamed, Limon Barua","year":"2021","venue":"Transportation research interdisciplinary perspectives","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Finkelstein et al., 2022] Mira Finkelstein, Lucy Liu, Yoav Kolumbus, David C Parkes, Jeffrey Rosenschein, Sarah Keren, et al. Explainable reinforcement learning via model transforms. In Advances in Neural Information Processing Systems, 2022. Guidotti et al., 2018]","title":"Explainable reinforcement learning via model transforms","authors":"Mira Finkelstein, Lucy Liu, Yoav Kolumbus, David C Parkes, Jeffrey Rosenschein, Sarah Keren","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51(5):1-42, 2018.","title":"A survey of methods for explaining black box models","authors":"Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, Dino Pedreschi","year":"2018","venue":"ACM computing surveys","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Haksar and Schwager, 2018] Ravi N. Haksar and Mac Schwager. Distributed deep reinforcement learning for fighting forest fires with a network of aerial robots. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1067-1074. IEEE, 2018.","title":"Distributed deep reinforcement learning for fighting forest fires with a network of aerial robots","authors":"Ravi N Haksar, Mac Schwager","year":"2018","venue":"IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Heuillet et al., 2021] Alexandre Heuillet, Fabien Couthouis, and Natalia Díaz-Rodríguez. Explain- ability in deep reinforcement learning. Knowledge-Based Systems, 214:106685, 2021.","title":"Explainability in deep reinforcement learning","authors":"Alexandre Heuillet, Fabien Couthouis, Natalia Díaz-Rodríguez","year":"2021","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Hoffman et al., 2018] Robert R. Hoffman, Shane T. Mueller, Gary Klein, and Jordan Litman. Metrics for explainable ai: Challenges and prospects. arXiv preprint arXiv:1812.04608, 2018.","title":"Metrics for explainable ai: Challenges and prospects","authors":"Robert R Hoffman, Shane T Mueller, Gary Klein, Jordan Litman","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Huber et al., 2021] Tobias Huber, Katharina Weitz, Elisa- beth André, and Ofra Amir. Local and global explana- tions of agent behavior: Integrating strategy summaries with saliency maps. Artificial Intelligence, 301:103571, 2021.","title":"Local and global explanations of agent behavior: Integrating strategy summaries with saliency maps","authors":"Huber","year":"2021","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Julian and Kochenderfer, 2019] Kyle D. Julian and Mykel J. Kochenderfer. Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning. Journal of Guidance, Control, and Dynamics, 42(8):1768- 1778, 2019.","title":"Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning","authors":"Kyle D Julian, Mykel J Kochenderfer","year":"2019","venue":"Journal of Guidance, Control, and Dynamics","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kamga et al., 2015] Camille Kamga, M. Anil Yazici, and Abhishek Singhal. Analysis of taxi demand and supply in new york city: implications of recent taxi regulations. Transportation Planning and Technology, 38(6):601-625, 2015.","title":"Analysis of taxi demand and supply in new york city: implications of recent taxi regulations","authors":"Camille Kamga, M Anil Yazici, Abhishek Singhal","year":"2015","venue":"Transportation Planning and Technology","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kraus et al., 2020] Sarit Kraus, Amos Azaria, Jelena Fiosina, Maike Greve, Noam Hazon, Lutz Kolbe, Tim- Benjamin Lembcke, Jörg P. Müller, Sören Schleibaum, and Mark Vollrath. Ai for explaining decisions in multi- agent environments. In Proceedings of the AAAI Confer- ence on Artificial Intelligence, volume 34, pages 13534- 13538, 2020.","title":"Ai for explaining decisions in multiagent environments","authors":"Sarit Kraus, Amos Azaria, Jelena Fiosina, Maike Greve, Noam Hazon, Lutz Kolbe, Tim-Benjamin Lembcke, Jörg P Müller, Sören Schleibaum, Mark Vollrath","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lundberg and Lee, 2017] Scott M. Lundberg and Su-In Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30, 2017.","title":"A unified approach to interpreting model predictions","authors":"M Scott, Su-In Lundberg, Lee","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Meijer and Wessels, 2019] Albert Meijer and Martijn Wes- sels. Predictive policing: Review of benefits and draw- backs. International Journal of Public Administration, 42(12):1031-1039, 2019.","title":"Predictive policing: Review of benefits and drawbacks","authors":"Wessels Meijer, Albert Meijer, Martijn Wessels","year":"2019","venue":"International Journal of Public Administration","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Naiseh et al., 2020] Mohammad Naiseh, Nan Jiang, Jian- bing Ma, and Raian Ali. Explainable recommenda- tions in intelligent systems: delivery methods, modali- ties and risks. In Research Challenges in Information Science: 14th International Conference, RCIS 2020, Li- massol, Cyprus, September 23-25, 2020, Proceedings 14, pages 212-228. Springer, 2020.","title":"Explainable recommendations in intelligent systems: delivery methods, modalities and risks","authors":"Mohammad Naiseh, Nan Jiang, Jianbing Ma, Raian Ali","year":"2020","venue":"Research Challenges in Information Science: 14th International Conference, RCIS 2020","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Puiutta and Veith, 2020] Erika Puiutta and Eric Veith. Ex- plainable reinforcement learning: A survey. In Interna- tional cross-domain conference for machine learning and knowledge extraction, pages 77-95. Springer, 2020.","title":"International cross-domain conference for machine learning and knowledge extraction","authors":"Veith Puiutta, Erika Puiutta, Eric Veith","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ribeiro et al., 2016] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. \"why should i trust you?\" explain- ing the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowl- edge discovery and data mining, pages 1135-1144, New York, NY, USA, 2016. Association for Computing Ma- chinery.","title":"why should i trust you?\" explaining the predictions of any classifier","authors":"Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Saeed and Omlin, 2023] Waddah Saeed and Christian Om- lin. Explainable ai (xai): A systematic meta-survey of current challenges and future opportunities. Knowledge- Based Systems, 263:110273, 2023.","title":"Explainable ai (xai): A systematic meta-survey of current challenges and future opportunities","authors":"Waddah Saeed, Christian Omlin","year":"2023","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Schleibaum et al., 2024] Sören Schleibaum, Lu Feng, Sarit Kraus, and Jörg P. Müller. ADESSE: Advice Expla- nations in Complex Repeated Decision-Making Environ- ments. arXiv preprint arXiv:2405.20705, 2024.","title":"ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments","authors":"Sören Schleibaum, Lu Feng, Sarit Kraus, Jörg P Müller","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Shin, 2021] Donghee Shin. The effects of explainability and causability on perception, trust, and acceptance: Implica- tions for explainable ai. International Journal of Human- Computer Studies, 146:102551, 2021.","title":"Donghee Shin. The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable ai","authors":"Shin","year":"2021","venue":"International Journal of Human-Computer Studies","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Shvo and McIlraith, 2020] Maayan Shvo and Sheila A. McIlraith. Active goal recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 9957-9966, 2020.","title":"Active goal recognition","authors":"Mcilraith ; ] Maayan Shvo, Sheila A Shvo, Mcilraith","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Speith, 2022] Timo Speith. A review of taxonomies of ex- plainable artificial intelligence (xai) methods. In Proceed- ings of the 2022 ACM Conference on Fairness, Account- ability, and Transparency, pages 2239-2250, New York, NY, USA, 2022. Association for Computing Machinery.","title":"A review of taxonomies of explainable artificial intelligence (xai) methods","authors":"Timo Speith","year":"2022","venue":"Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Sreedharan et al., 2022] Sarath Sreedharan, Utkarsh Soni, Mudit Verma, Siddharth Srivastava, and Subbarao Kamb- hampati. Bridging the gap: Providing post-hoc sym- bolic explanations for sequential decision-making prob- lems with inscrutable representations. In International Conference on Learning Representations, 2022.","title":"Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations","authors":"Sarath Sreedharan, Utkarsh Soni, Mudit Verma, Siddharth Srivastava, Subbarao Kambhampati","year":"2022","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Topin and Veloso, 2019] Nicholay Topin and Manuela Veloso. Generation of policy-level explanations for reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 2514-2521. AAAI Press, 2019.","title":"Nicholay Topin and Manuela Veloso. Generation of policy-level explanations for reinforcement learning","authors":"Veloso Topin","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Torrey and Taylor, 2013] Lisa Torrey and Matthew Taylor. Teaching on a budget: Agents advising agents in rein- forcement learning. In Proceedings of the 2013 interna- tional conference on Autonomous agents and multi-agent systems, pages 1053-1060, Richland, SC, 2013. Interna- tional Foundation for Autonomous Agents and Multiagent Systems.","title":"Teaching on a budget: Agents advising agents in reinforcement learning","authors":"Torrey, Taylor ;, Lisa Torrey, Matthew Taylor","year":"2013","venue":"Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Tran et al., 2023] Thi Ngoc Trang Tran, Alexander Felfer- nig, Viet Man Le, Thi Minh Ngoc Chau, and Thu Giang Mai. User needs for explanations of recommendations: In-depth analyses of the role of item domain and personal characteristics. In Proceedings of the 31st ACM Confer- ence on User Modeling, Adaptation and Personalization, pages 54-65, Limassol Cyprus, 2023. ACM.","title":"User needs for explanations of recommendations: In-depth analyses of the role of item domain and personal characteristics","authors":"Thi Ngoc, Trang Tran, Alexander Felfernig, Man Viet, Thi Le, Ngoc Minh, Thu Giang Chau, Mai","year":"2023","venue":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Vodrahalli et al., 2022] Kailas Vodrahalli, Roxana Daneshjou, Tobias Gerstenberg, and James Zou. Do humans trust advice more if it comes from ai? an analysis of human-ai interactions. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, pages 763-777, Oxford United Kingdom, 2022. ACM.","title":"Do humans trust advice more if it comes from ai? an analysis of human-ai interactions","authors":"Kailas Vodrahalli, Roxana Daneshjou, Tobias Gerstenberg, James Zou","year":"2022","venue":"Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Vouros, 2022] George A. Vouros. Explainable deep rein- forcement learning: state of the art and challenges. ACM Computing Surveys, 55(5):1-39, 2022.","title":"Explainable deep reinforcement learning: state of the art and challenges","authors":"George A Vouros","year":"2022","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Vultureanu-Albis ¸i and Bȃdicȃ, 2022] Alexandra Vultureanu-Albis ¸i and Costin Bȃdicȃ. A survey on effects of adding explanations to recommender systems. Concurrency and Computation: Practice and Experience, 34(20):e6834, 2022.","title":"A survey on effects of adding explanations to recommender systems","authors":"Alexandra Vultureanu-Albis ¸i, Costin Bȃdicȃ","year":"2022","venue":"Concurrency and Computation: Practice and Experience","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2016] Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas. Duel- ing network architectures for deep reinforcement learn- ing. In International conference on machine learning, pages 1995-2003, New York, NY, USA, 2016. PMLR, JMLR.org.","title":"Dueling network architectures for deep reinforcement learning","authors":"Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, Nando Freitas","year":"2016","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wells and Bednarz, 2021] Lindsay Wells and Tomasz Bed- narz. Explainable ai and reinforcement learning-a sys- tematic review of current approaches and trends. Frontiers in artificial intelligence, 4:48, 2021.","title":"Lindsay Wells and Tomasz Bednarz. Explainable ai and reinforcement learning-a systematic review of current approaches and trends","authors":"Bednarz Wells","year":"2021","venue":"Frontiers in artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yan et al., 2023] An Yan, Zhankui He, Jiacheng Li, Tianyang Zhang, and Julian McAuley. Personalized show- cases: Generating multi-modal explanations for recom- mendations. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Infor- mation Retrieval, pages 2251-2255, Taipei Taiwan, 2023. ACM.","title":"Personalized showcases: Generating multi-modal explanations for recommendations","authors":"Yan","year":"2023","venue":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhang et al., 2020a] Yongfeng Zhang, Xu Chen, et al. Ex- plainable recommendation: A survey and new perspec- tives. Foundations and Trends® in Information Retrieval, 14(1):1-101, 2020.","title":"Explainable recommendation: A survey and new perspectives","authors":"Zhang","year":"2020","venue":"Foundations and Trends® in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2020b] Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy. Effect of confidence and explana- tion on accuracy and trust calibration in ai-assisted de- cision making. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 295- 305, Barcelona Spain, 2020. ACM.","title":"Effect of confidence and explanation on accuracy and trust calibration in ai-assisted decision making","authors":"Zhang","year":"2020","venue":"Proceedings of the 2020 conference on fairness, accountability, and transparency","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}