@workshop{Alameda-Pineda2020,
title = {FATE/MM 20: 2nd International Workshop on Fairness, Accountability, Transparency and Ethics in MultiMedia},
author = {Xavier Alameda-Pineda, Miriam Redi, Jahna Otterbacher, Nicu Sebe, Shih-Fu Chang},
url = {https://dl.acm.org/doi/abs/10.1145/3394171.3421896},
doi = {10.1145/3394171.3421896},
isbn = {9781450379885},
year = {2020},
date = {2020-10-12},
booktitle = {Proceedings of the 28th ACM International Conference on Multimedia},
abstract = {The series of FAT/FAccT events aim at bringing together researchers and practitioners interested in fairness, accountability, transparency and ethics of computational methods. The FATE/MM workshop focuses on addressing these issues in the Multimedia field. Multimedia computing technologies operate today at an unprecedented scale, with a growing community of scientists interested in multimedia models, tools and applications. Such continued growth has great implications not only for the scientific community, but also for the society as a whole. Typical risks of large-scale computational models include model bias and algorithmic discrimination. These risks become particularly prominent in the multimedia field, which historically has been focusing on user-centered technologies. To ensure a healthy and constructive development of the best multimedia technologies, this workshop offers a space to discuss how to develop ethical, fair, unbiased, representative, and transparent multimedia models, bringing together researchers from different areas to present computational solutions to these issues.},
keywords = {Accountability, Algorithmic Fairness, Algorithmic Transparency, Ethics},
pubstate = {published},
tppubtype = {workshop}
}
The series of FAT/FAccT events aim at bringing together researchers and practitioners interested in fairness, accountability, transparency and ethics of computational methods. The FATE/MM workshop focuses on addressing these issues in the Multimedia field. Multimedia computing technologies operate today at an unprecedented scale, with a growing community of scientists interested in multimedia models, tools and applications. Such continued growth has great implications not only for the scientific community, but also for the society as a whole. Typical risks of large-scale computational models include model bias and algorithmic discrimination. These risks become particularly prominent in the multimedia field, which historically has been focusing on user-centered technologies. To ensure a healthy and constructive development of the best multimedia technologies, this workshop offers a space to discuss how to develop ethical, fair, unbiased, representative, and transparent multimedia models, bringing together researchers from different areas to present computational solutions to these issues.