Social media is polluting society. Content moderation alone won’t fix the problem
10 Oct 2022
In Social media is polluting society. Content moderation alone won’t fix the problem published in the MIT Technology Review, CHAI’s Thomas Krendl Gilbert argues that if content moderation on social media were implemented perfectly, it would still miss a whole host of issues that are often portrayed as moderation problems but really are not. He explains that in order to address those non-speech issues, we need a new strategy: treat social media companies as potential polluters of the social fabric, and directly measure and mitigate the effects their choices have on human populations. That means establishing a policy framework—perhaps through something akin to an Environmental Protection Agency or Food and Drug Administration for social media—that can be used to identify and evaluate the societal harms generated by these platforms. If those harms persist, that group could be endowed with the ability to enforce those policies. But to transcend the limitations of content moderation, such regulation would have to be motivated by clear evidence and be able to have a demonstrable impact on the problems it purports to solve.
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems
03 Oct 2022
In Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems, CHAI’s Siddharth Srivastava argues that reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons. This paper presents a new approach that utilizes relational abstractions in conjunction with deep learning to learn a generalizable Q-function for such problems. The learned Q-function can be efficiently transferred to related problems that have different object names and object quantities, and thus, entirely different state spaces. We show that the learned, generalized Q- function can be utilized for zero-shot transfer to re- lated problems without an explicit, hand-coded curriculum. Empirical evaluations on a range of problems show that our method facilitates efficient zero-shot transfer of learned knowledge to much larger problem instances containing many objects.