The potential for data-driven algorithmic systems to amplify existing social inequities, or create new ones, is receiving increasing popular and academic attention. A surge of recent work, across multiple researcher and practitioner communities, has focused on the development of design strategies and algorithmic methods to monitor and mitigate bias in such systems. Yet relatively little of this work has addressed the unique challenges raised in the design, development, maintenance, and real-world deployment of learning analytics systems.
This interactive, half-day workshop aims to provide a venue for researchers and practitioners to share research and real-world experiences related to fairness and equity in the design of learning analytics systems. In addition, the workshop aims to facilitate the development of a lasting community around Fair Learning Analytics, and to open the door to new researcher, practitioner, and researcher-practitioner collaborations around these topics.
This workshop encourages contributions from researchers, developers, designers, and educators who have research and/or real-world experiences to share related to fairness and equity in the design of learning analytics.
Submissions should follow the LAK Companion Proceedings template, and should be between 2- to 5-pages in length. If accepted, submissions will be published electronically through the LAK19 Companion Proceedings.
Submissions should be sent by email to FairLAK@gmail.com
Examples of topics well-fit to this workshop are provided below. However, this is far from an exhaustive list! If you are uncertain whether your topic may be a good fit for this workshop, please feel free to send a brief description to FairLAK@gmail.com
December 3 - Deadline for submissions
December 18 - Extended deadline for submissions!
January 4 - Notifications sent out
February 5 - Camera-ready deadline
March 5 - FairLAK Workshop