The Goofus & Gallant Story Corpus for Practical Value Alignment
Md Sultan Al Nahian, Tasmia Tasrin, Spencer Frazier, Mark Riedl and, Brent Harrison

TL;DR
This paper introduces a multi-modal dataset combining images and descriptions to teach AI systems social norms and principles, aiming to improve value alignment and prevent harm.
Contribution
It presents a novel dataset with real-life scenarios in natural language and images, designed for training socially normative AI agents.
Findings
Dataset includes curated images and descriptions of normative behavior.
Designed for training AI to understand social principles.
Aims to improve AI value alignment and safety.
Abstract
Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. Thus, to prevent intentional or unintentional harm, AI systems are expected to take actions that align with these principles. Training systems to exhibit this type of behavior is difficult and often requires a specialized dataset. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations described through natural language and artistic images. This training set contains curated sets of images that are designed to teach young children about social principles. We argue that this is an ideal dataset to use…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsALIGN · Sparse Evolutionary Training
