The Goofus & Gallant Story Corpus for Practical Value Alignment

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 for training socially normative agents given this fact.

• The paper introduces a curated multi-modal dataset that enhances AI's ability to discern normative versus non-normative behaviors.
• It employs a dual-modality approach by leveraging both text and images, validated through manual review and GPT-4o annotations.
• Experimental tasks on normativity and principles classification demonstrate the dataset's potential for practical AI value alignment.

Insight into the Goofus and Gallant Story Corpus for Practical Value Alignment

The paper, "The Goofus and Gallant Story Corpus for Practical Value Alignment," introduces a meticulously curated multi-modal dataset aimed at addressing the challenges inherent in the training of AI systems for socially normative behaviors. The hypothesized utility of this dataset lies in its potential to provide superior training data for the creation of value-aligned autonomous agents. Value alignment in AI involves ensuring that autonomous systems operate in concordance with human societal norms, a nuanced task given the vast diversity and implicit nature of human values.

Overview of the Dataset

The core contribution of the paper is the development of the Goofus and Gallant (GnG) Story Corpus, which leverages the dichotomous characteristics of Goofus and Gallant from the eponymous children's comic strips. This corpus consists of stories depicting normative and non-normative behaviors, presented through text and images that are straightforwardly labeled according to the predefined moral actions of the characters Goofus (non-normative behavior) and Gallant (normative behavior). The dataset is bifurcated into the GnG Normative and GnG Principles datasets. The former categorizes actions as normative or not, while the latter associates these actions with specific societal principles sourced from a refined value taxonomy.

Methodology and Data Curation

To construct this dataset, the authors scrupulously curated content from Highlights magazine, selecting comic strips spanning from 1995 to 2017. The extraction involved a dual-modality approach using both text and corresponding images, which augments the potential for rich learning of contextual societal cues. Furthermore, the paper acknowledges and addresses the limitations seen in typical crowd-sourced datasets. Emphasizing quality over quantity, the research introduces a manual validation mechanism, involving human certification and the use of LLMs, specifically GPT-4o, to annotate a subset of the data.

Experimental Tasks

Two experimental tasks are presented to demonstrate the capabilities of the dataset:

1. Normativity Classification: This task endeavors to distinguish between normative and non-normative actions using both text and image inputs. The research finds that a dual-input approach leveraging both modalities notably enhances the performance of AI classifiers in discerning societal norms from the dataset.
2. Principles Classification: This task aims to predict the underlying social principles associated with given actions. The experiment here illustrates the capability of the GnG dataset to disclose latent values in seemingly straightforward actions.

Implications and Future Work

The dataset's potential implications are prominent both theoretically and practically. Theoretically, it lays the groundwork for future studies into value alignment by providing a focused corpus designed to minimize noise and maximize the clarity of moral messaging. Practically, it facilitates the development of AI that can learn and replicate human societal values more reliably than existing datasets allow. This represents a substantial benefit in improving the trustworthiness and safety of autonomous systems interacting with humans.

While the research stops short of extensive real-world deployment, its foundational nature suggests an exciting prospect for future developments in deep-learning-based systems. Further exploration might involve more sophisticated models integrating this dataset into broader AI training regimes, thereby enhancing the interpretability and cultural competency of such systems. Additionally, the incorporation of stories into AI training datasets points towards a promising direction where narrative-based data could unravel complexities in value imbued reasoning.

In summary, this paper provides a novel approach to creating socially normative AI, underscoring the importance of high-quality, targeted datasets. It presents promising grounds for future research aiming at reliable AI value alignment, emphasizing the role of clear, ethically charged data curation in shaping the future of machine ethics.