Abstract
With social computing systems and algorithms having been shown to give rise to unintended consequences, one of the suspected success criteria is their ability to integrate and utilize people's inherent cognitive biases. Biases can be present in users, systems and their contents. With HCI being at the forefront of designing and developing user-facing computing systems, we bear special responsibility for increasing awareness of potential issues and working on solutions to mitigate problems arising from both intentional and unintentional effects of cognitive biases. This workshop brings together designers, developers, and thinkers across disciplines to redefine computing systems by focusing on inherent biases in people and systems and work towards a research agenda to mitigate their effects. By focusing on cognitive biases from content or system as well as from a human perspective, this workshop will sketch out blueprints for systems that contribute to advancing technology and media literacy, building critical thinking skills, and depolarization by design.
Original language | English |
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Title of host publication | Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems Sponsored by: |
Editors | Joanna McGrenere, Andy Cockburn |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Number of pages | 6 |
ISBN (Electronic) | 9781450368193 |
DOIs | |
Publication status | Published - Apr 2020 |
Externally published | Yes |
Event | International Conference on Human Factors in Computing Systems 2020 - Honolulu , United States of America Duration: 25 Apr 2020 → 30 Apr 2020 Conference number: 38th https://chi2020.acm.org (Website) https://dl.acm.org/doi/proceedings/10.1145/3313831 (Proceedings) |
Conference
Conference | International Conference on Human Factors in Computing Systems 2020 |
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Abbreviated title | CHI 2020 |
Country/Territory | United States of America |
City | Honolulu |
Period | 25/04/20 → 30/04/20 |
Internet address |
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Keywords
- Cognition-aware systems
- Cognitive biases
- System bias