Laura Schelenz, Avi Segal, and Kobi Gal will present their research on “Best practices for Transparency in Machine Generated Personalization” at the ACM Conference on User Modeling, Adaptation and Personalization in July 2020. Their interdisciplinary research at the intersection of ethics, data science, and machine learning provides guidance to system designers on how to increase transparency in their systems. Increased transparency helps users navigate online services and engage in a more meaningful and ethical way with the technology at hand. To date, little guidance exists for system designers to implement transparency in their systems. A checklist and recommendations for best practices help designers who are interested in embedding ethics in their work.
A 2-minute video advertising the WeNet poster session for ACM UMAP can be found here: https://www.youtube.com/watch?v=lihpVrTh2xk
ACM UMAP 2020 – User Modeling, Adaptation and Personalization – is the premier international conference for researchers and practitioners working on systems that adapt to individual users or to groups of users, and that collect, represent, and model user information. The conference program is available at: https://um.org/umap2020/attending/program/