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 provides 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, we 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.
|Title of host publication||26th International Conference on Intelligent User Interfaces, IUI 2021 Companion|
|Publisher||Association for Computing Machinery|
|Number of pages||2|
|State||Published - 14 Apr 2021|
|Event||26th International Conference on Intelligent User Interfaces: Where HCI Meets AI, IUI 2021 - Virtual, Online, United States|
Duration: 14 Apr 2021 → 17 Apr 2021
|Name||International Conference on Intelligent User Interfaces, Proceedings IUI|
|Conference||26th International Conference on Intelligent User Interfaces: Where HCI Meets AI, IUI 2021|
|Period||14/04/21 → 17/04/21|
Bibliographical notePublisher Copyright:
© 2021 Owner/Author.
- Intelligent systems
- Machine learning
ASJC Scopus subject areas
- Human-Computer Interaction