Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Sonboli, Nasim"'
General Data Protection Regulations (GDPR) aim to safeguard individuals' personal information from harm. While full compliance is mandatory in the European Union and the California Privacy Rights Act (CPRA), it is not in other places. GDPR requires s
Externí odkaz:
http://arxiv.org/abs/2410.07182
Autor:
Venkatasubramanian, Suresh, Gebru, Timnit, Topcu, Ufuk, Griffin, Haley, Rosenbloom, Leah Namisa, Sonboli, Nasim
Based on our workshop activities, we outlined three ways in which research can support community needs: (1) Mapping the ecosystem of both the players and ecosystem and harm landscapes, (2) Counter-Programming, which entails using the same surveillanc
Externí odkaz:
http://arxiv.org/abs/2406.07556
Recommender systems utilize users' historical data to learn and predict their future interests, providing them with suggestions tailored to their tastes. Calibration ensures that the distribution of recommended item categories is consistent with the
Externí odkaz:
http://arxiv.org/abs/2208.10192
Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorit
Externí odkaz:
http://arxiv.org/abs/2103.08786
As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit. There has been considerable research on rec
Externí odkaz:
http://arxiv.org/abs/2009.02590
As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primari
Externí odkaz:
http://arxiv.org/abs/2005.12974
Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents unique challenges for fairness due to trade offs between optimizing ac
Externí odkaz:
http://arxiv.org/abs/2003.06461
Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias dis
Externí odkaz:
http://arxiv.org/abs/1909.06362
Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system. Identifying influential users and studying t
Externí odkaz:
http://arxiv.org/abs/1905.08031
When evaluating recommender systems for their fairness, it may be necessary to make use of demographic attributes, which are personally sensitive and usually excluded from publicly-available data sets. In addition, these attributes are fixed and ther
Externí odkaz:
http://arxiv.org/abs/1809.04199