31 citations · 67 across the 9 of their papers we have counts for
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cs.IR2022★ 22 cited
Solutions to preference manipulation in recommender systems require knowledge of meta-preferences
Hal Ashton, Matija Franklin
Iterative machine learning algorithms used to power recommender systems often change people's preferences by trying to learn them. Further a recommender can better predict what a u…
cs.RO2022
Preference Change in Persuasive Robotics
Matija Franklin, Hal Ashton
Human-robot interaction exerts influence towards the human, which often changes behavior. This article explores an externality of this changed behavior - preference change. It expa…
cs.AI2022★ 9 cited
Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI
Matija Franklin, Hal Ashton, Rebecca Gorman +1
As artificial intelligence becomes more powerful and a ubiquitous presence in daily life, it is imperative to understand and manage the impact of AI systems on our lives and decisi…