Source-linked AI summary
Social Chemistry 101: Learning to Reason about Social and Moral Norms
Maxwell Forbes, Jena D. Hwang, Vered Shwartz, Maarten Sap, Yejin Choi
TL;DR
Social norms are difficult for AI systems to reason about because they are unspoken, culturally sensitive judgments underlying everyday actions. The paper formalizes these norms as annotated rules-of-thumb in SOCIAL-CHEM-101 and trains NEURAL NORM TRANSFORMER to generalize them to unseen situations, with results showing both generated relevant rules and variable attribute adherence.
Problem
AI systems need to account for unspoken social and moral norms when interpreting everyday actions and interacting with people.
Method
The paper introduces Social Chemistry, organizing descriptive norms as rules-of-thumb and collecting structured judgments across 12 norm-attribute dimensions.
Results
NEURAL NORM TRANSFORMER generates relevant and potentially novel attribute-aware rules-of-thumb for unseen situations, with human-rated attribute adherence ranging from 0.28 to 0.91 micro-F1.
Takeaways & Limitations
SOCIAL-CHEM-101 provides a resource for computational modeling of social norms and supports interdisciplinary research across NLP, computational norms, and descriptive ethics.
Takeaways & Limitations
The study focuses on English-speaking cultures represented within North America, leaving extension to other countries and non-English-speaking cultures for future research.
Abstract
from arXiv · showhide
Social norms -- the unspoken commonsense rules about acceptable social behavior -- are crucial in understanding the underlying causes and intents of people's actions in narratives. For example, underlying an action such as "wanting to call cops on my neighbors" are social norms that inform our conduct, such as "It is expected that you report crimes." We present Social Chemistry, a new conceptual formalism to study people's everyday social norms and moral judgments over a rich spectrum of real life situations described in natural language. We introduce Social-Chem-101, a large-scale corpus that catalogs 292k rules-of-thumb such as "it is rude to run a blender at 5am" as the basic conceptual units. Each rule-of-thumb is further broken down with 12 different dimensions of people's judgments, including social judgments of good and bad, moral foundations, expected cultural pressure, and assumed legality, which together amount to over 4.5 million annotations of categorical labels and free-text descriptions. Comprehensive empirical results based on state-of-the-art neural models demonstrate that computational modeling of social norms is a promising research direction. Our model framework, Neural Norm Transformer, learns and generalizes Social-Chem-101 to successfully reason about previously unseen situations, generating relevant (and potentially novel) attribute-aware social rules-of-thumb.
1 Introduction
The paper introduces Social Chemistry as a bottom-up formalism and corpus for representing social and moral norms in everyday situations. It also presents a neural framework that generates attribute-aware rules-of-thumb for unseen situations, though adherence varies across attributes.
- Motivation and formalism: Social Chemistry represents unspoken social and moral norms as descriptive rules-of-thumb grounded in everyday situations.The approach studies crowdsourced judgments rather than prescribing universal ethical rules.
- Dataset: 292k rules-of-thumb cover 104k real-life situations, with 365k structural annotation sets and over 4.5M categorical and free-text annotations.Each rule-of-thumb is decomposed into 12 dimensions of norm attributes.
- Modeling: NEURAL NORM TRANSFORMER learns from SOCIAL-CHEM-101 to generate relevant and potentially novel rules-of-thumb for previously unseen situations.Generation is conditioned on the attribute dimensions.
- Results: 0.28 to 0.91 micro-F1 captures human ratings of model adherence to different norm attributes.The reported range indicates that the breadth of the task remains challenging for current neural models.
- Applications: Political-news analysis aligns with Moral Foundation Theory on variation in communities’ moral norms by political leaning and news reliability.The authors present this analysis as a potential practical use case for computational social norms.
- Implications: The empirical studies characterize computational modeling of social norms as a feasible and promising direction for interdisciplinary research.The proposed resource is positioned for work across NLP, computational norms, and descriptive ethics.
2 Approach
The approach models social norms as situationally relevant evaluative rules-of-thumb that can express multiple perspectives and conflicts. SOCIAL-CHEM-101 annotates these rules across culturally sensitive judgments and social situations.
- Rules-of-Thumb: A rule-of-thumb is a descriptive cultural norm structured as a judgment of an action, such as calling an action rude.The formalism treats rules-of-thumb as evaluative judgments evoked by situations.
- Rules-of-Thumb: Complex situations can evoke multiple rules-of-thumb covering different actions, characters, perspectives, and conflicting judgments.The example of punching a friend includes norms about injury, theft, betrayal, and revenge.
- Annotation structure: Each situation annotation links rules-of-thumb to particular people and records categorical and free-text judgments about the relevant norms.Different people may evoke distinct or conflicting rules, and one person may hold conflicting rules that form moral dilemmas.
- Cultural scope: The study focuses on socionormative judgments from English-speaking cultures represented within North America.Extending the formalism to other countries and non-English-speaking cultures is identified as future research.
3 SOCIAL-CHEM-101 Dataset
SOCIAL-CHEM-101 collects real-life situations and crowdsourced rules-of-thumb, then structures each rule with judgments that ground it to characters, actions, and social expectations. The dataset enables analysis across moral judgment, agreement, cultural pressure, and legality, while exposing annotation-quality and interpretive limits.
- Data collection: 104k situations from four domains yielded 292k rules-of-thumb, including 260k unique RoTs.Workers generated one to five RoTs per situation, with RoTs averaging 10 words.
- RoT collection: Workers were instructed to write relevant, self-contained acceptability judgments that balance generalizability against excessive specificity.The guidelines also asked for distinct ideas and discouraged trivial inversions or rephrasings.
- Structured annotation: Each RoT receives a breakdown isolating its action and assigning categorical attributes to both the RoT and action.The attributes support analyses beyond text-only RoTs and are organized around situation grounding and theoretically motivated social categories.
- Structured annotation: Grounding attributes link RoTs and actions to relevant characters and indicate whether the candidate character explicitly performs the action.The RoT Targeting attribute identifies who should heed the rule, while action annotations select the most relevant character.
- Social attributes: Social attributes capture anticipated agreement, moral foundations, legality, cultural pressure, and subjective social judgment.RoT-level attributes concern agreement and moral foundations, while action-level attributes include legality and cultural pressure.
- Dataset analysis: Figure 5 separates morally charged discretionary actions from morally neutral actions that still attract cultural pressure.The plots also place illegal activities among actions judged bad and subject to strong negative pressure, but they are illustrative rather than quantitative claims.
4 Model
The Neural Norm Transformer models RoT, action, and attribute distributions conditioned on situations, using language-modeling objectives across multiple field configurations. The framework also studies more difficult situation-only action generation while retaining the assumption that situations are provided.
- Model formulation: The model represents RoTs, actions, and their attribute breakdowns as conditional distributions given a situation.Attributes are partitioned into RoT-relevant and action-relevant sets.
- Model formulation: Action transcription is strongly conditioned on RoTs because actions are closely related to them.
- Model formulation: The study instead evaluates action prediction from the situation alone, making the action distribution more difficult.
- Training objectives: Models tokenize situations, outputs, actions, and attribute values into canonical-order sequences and maximize a standard language-modeling objective.
- Training objectives: Each model is trained simultaneously on multiple objectives that vary which fields are conditioned on and generated.The implementations include GPT, GPT-2, BART, and T5 architectures under the Neural Norm Transformer framework.
5 Experiments and Results
Experiments compare model-choice generation, which lets systems select attributes, with conditional generation under human-provided attribute constraints. Human and automatic evaluations show a trade-off between relevance and attribute adherence, with complete conditional generation remaining difficult.
- 5.1 Tasks: The model-choice setting lets systems select likely attributes before generating a RoT or action, whereas the conditional setting requires following supplied attributes.The conditional setup is harder because models cannot choose the attributes they find most likely.
- 5.1 Tasks: An 80/10/10% situation-level train/dev/test split ensures previously unobserved development and test situations.
- 5.1 Tasks: The evaluation uses Random RoT and BERT-Score retrieval baselines alongside model ablations for smaller and non-pretrained GPT-2 variants.
- 5.2 Results: T5 generates the most situation-relevant outputs, while GPT-2 adheres more consistently to attributes, especially in the controlled setup.GPT-2 achieves the top scores on 5/9 attributes in that setup.
- 5.2 Results: No model scores highly across all attribute columns, indicating that fully constrained conditional generation remains challenging.
- 5.2 Results: Nearly 20 points for RoTs and over 10 points for actions separate GPT-2 from T5 in attribute F1, while T5 achieves the highest BLEU score.Automatic metrics are reported as consistent with the human evaluation.
6 Morality & Political Bias
The framework is applied to political news headlines to examine whether generated social-norm attributes vary with source ideology and reliability. The observed correlations align with prior findings from moral-foundation and news-reliability research.
- Use case: The study generates RoTs and attributes for 50,000 political news headlines annotated for source leaning and factual reliability.
- Political leaning: Liberal headlines evoke more fairness and care, whereas right-leaning headlines evoke more sanctity and loyalty.The correlations are reported as strongly corroborating Graham et al. (2009).
- Source reliability: More reliable news sources tend to evoke more advice and less morality, consistent with prior findings.
7 Related Work
Related work spans descriptive ethics, social psychology, pragmatics, frame theory, social implications, bias frames, and resources for moral or cultural reasoning. Social Chemistry is distinguished by formalizing expectations of social norms evoked by situations.
- Foundations: The formalism draws on descriptive ethics, social psychology, social implicatures, cooperative principles, and situationally evoked frames.
- NLP resources: Earlier NLP resources study reactions to situations, social and moral dynamics, morality in text, and free-form social implications.
- Distinction: Unlike related resources for bias, ritual understanding, ethical utility, or AITA prediction, this work formalizes expectations of social norms evoked by situations.
8 Conclusion
The paper presents SOCIAL-CHEM-101 as a formalism and resource for grounded social, moral, and ethical norms. Experiments show preliminary success in structured rule-of-thumb generation and corroborate moral-leaning findings in an extrinsic task.
- SOCIAL-CHEM-101 provides a formalism and resource for studying grounded social, moral, and ethical norms.
- Experiments demonstrate preliminary success in generative modeling of structured rules-of-thumb.
- An extrinsic task corroborates findings of moral leaning.
- Comprehensive computational modeling of social norms remains a promising future challenge for NLP.
A Additional Dataset Details
The appendix details the four source domains, annotation procedures, and guidelines used to construct and organize situations, characters, and rules-of-thumb. It emphasizes semantic RoT-writing criteria and practical dataset-labeling choices.
- Domains: The dataset draws situations from r/amitheasshole, r/confessions, ROCStories, and Dear Abby, with examples organized across four domains.The study attempts to balance domain sizes but is limited by the complete Dear Abby archive.
- Character Identification: Character identification seeks one most descriptive reference for each non-narrator person to support consistent RoT and action grounding.Human annotation handles ambiguous edge cases, and the guidelines favor recall by retaining the largest set of worker-identified characters.
- RoT Writing: RoT-writing guidelines balance generalization against specificity while requiring distinct, nontrivial descriptions of social, ethical, moral, and cultural norms.Unlike largely syntactic character guidelines, RoT-writing guidelines are semantic and therefore more challenging to define and check.
A.6 Annotator Demographics
The annotation workforce comprised 137 qualified workers with varied demographic backgrounds. The reported sample included differences in gender, race, age, relationship status, class, education, and parenthood.
- 137 workers participated after an extensive qualification process.
- 55% of workers were women and 45% were men, while 89% identified as white and 7% as Black.
- The largest age groups were 30–39 at 39%, 21–29 at 27%, and 40–49 at 19%.
- 53% of workers were single and 35% were married, while 47% identified as middle class and 41% as working class.
- 44% had bachelor’s degrees and 36% had some college or an associate degree; 63% had no children.
A.7 Demographics and Annotations
The study examines demographic variation in rule-of-thumb and action annotations using heavily repeated annotations and regression analyses. Agreement, social judgment, and cultural-pressure patterns show demographic associations, generally weaker for RoTs than actions.
- Analysis: 400 rules-of-thumb were annotated by 50 workers each, and generalized linear models analyzed demographic associations while controlling for relevant annotation factors.The analysis also considered political leaning inferred from state-level voting patterns and used Bonferroni correction for multiple comparisons.
- Agreement: Action judgments showed strongest agreement associations with being a woman and living in an urban setting, alongside associations with higher education, household size, and political leaning.
- Agreement: RoT agreement showed similar but weaker demographic associations, with small correlations involving income and social class.
- Cultural Pressure: Political leaning of the worker’s state was the only variable correlated with feeling culturally pressured, and the effect was small.
- Social Judgment: Higher social judgments for actions were associated with workers being women, highly educated, or younger, with weaker effects than action agreement.
B Experimental details
The models use the Transformers package and are trained under a fixed, single-epoch configuration. Inputs and outputs receive type and attribute-value special tokens with initialized embeddings.
- The models are implemented with the Transformers package and trained for one epoch using batch size 64 and random seed 42.
- Each input and output sequence is prefixed with a token indicating its type, such as [attrs], [rot], or [action].
- Special tokens represent attribute values, including <morality-ethics>, <bad>, <all>, and <against>.
- Special-token embeddings are initialized from corresponding word embeddings, averaging embeddings for multiword expressions.For example, <morality-ethics> uses the average of the morality and ethics embeddings.