TY - GEN
T1 - ExSS-ATEC
T2 - 25th International Conference on Intelligent User Interfaces, IUI 2020
AU - Smith-Renner, Alison
AU - Kuflik, Tsvi
AU - Sarkar, Advait
AU - Kleanthous, Styliani
AU - Stumpf, Simone
AU - Dugan, Casey
AU - Lim, Brian
AU - Otterbacher, Jahna
AU - Shulner, Avital
N1 - Publisher Copyright:
© 2020 International Conference on Intelligent User Interfaces, Proceedings IUI. All rights reserved.
PY - 2020/3/17
Y1 - 2020/3/17
N2 - Smart systems that apply complex reasoning to make decisions and plan behavior, such as decision support systems and personalized recommendations, are difficult for users to understand. Algorithms allow the exploitation of rich and varied data sources, in order to support human decision-making and/or taking direct actions; however, there are increasing concerns surrounding their transparency and accountability, as these processes are typically opaque to the user. Transparency and accountability have attracted increasing interest to provide more effective system training, better reliability and improved usability. This workshop will provide a venue for exploring issues that arise in designing, developing and evaluating intelligent user interfaces that provide system transparency or explanations of their behavior. In addition, our goal is to focus on approaches to mitigate algorithmic biases that can be applied by researchers, even without access to a given system's inter-workings, such as awareness, data provenance, and validation.
AB - Smart systems that apply complex reasoning to make decisions and plan behavior, such as decision support systems and personalized recommendations, are difficult for users to understand. Algorithms allow the exploitation of rich and varied data sources, in order to support human decision-making and/or taking direct actions; however, there are increasing concerns surrounding their transparency and accountability, as these processes are typically opaque to the user. Transparency and accountability have attracted increasing interest to provide more effective system training, better reliability and improved usability. This workshop will provide a venue for exploring issues that arise in designing, developing and evaluating intelligent user interfaces that provide system transparency or explanations of their behavior. In addition, our goal is to focus on approaches to mitigate algorithmic biases that can be applied by researchers, even without access to a given system's inter-workings, such as awareness, data provenance, and validation.
KW - Accountability
KW - Explanations
KW - Fairness
KW - Intelligent systems
KW - Intelligibility
KW - Machine learning
KW - Transparency
KW - Visualizations
UR - http://www.scopus.com/inward/record.url?scp=85082176541&partnerID=8YFLogxK
U2 - 10.1145/3379336.3379361
DO - 10.1145/3379336.3379361
M3 - Conference contribution
AN - SCOPUS:85082176541
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 7
EP - 8
BT - Proceedings of the 25th International Conference on Intelligent User Interfaces Companion. IUI 2020
PB - Association for Computing Machinery
Y2 - 17 March 2020 through 20 March 2020
ER -