Social Chemistry 101: Learning to Reason about Social and Moral Norms (2020)

Abstract: 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 neighbor” 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.

## introduction understanding and reasoning about social situations relies on unspoken commonsense rules about social norms, i.e., acceptable social behavior @xcite . for example, when faced with situations like "wanting to call the cops on my neighbors," (figure 1 ), we perform a rich variety of reasoning about about legality, cultural pressure, and even morality of the situation (here, "reporting a crime" and "being friends with your neighbor" are conflicting norms). failure to account multiple characters different perspectives rich structure it's not right to tell another person who to spend time with. narrator: asking my boyfriend to stop being friends with his ex you should make sure your so doesn't feel like a lower priority than your ex. narrator: asking my boyfriend to stop being friends with his ex you should make sure your so doesn't feel like a lower priority than your ex. narrator: asking my boyfriend to stop being friends with his ex legality it's not right to tell another person who to spend time with. bad it's okay to ask your significant other to stop doing something you're uncomfortable with. expected/ok good legal loyalty/betrayal care/harm strong cultural pressure common anticipated agreement social judgment moral foundations performing action probably not (b) (c) (a) figure 2 : three different slices of a complete annotation for a single situation, meant to illustrate our approach. each rot (text in colored boxes, e.g., "it's not right to tell...") is written for a particular real life situation (text in pale grey boxes, e.g., "asking my boyfriend to stop being ...") and a specific person in that situation ("narrator" vs "my boyfriend"). (a) a situation often includes multiple people with distinct perspectives, evoking different (and possibly conflicting) rots. (b) even a single person may have multiple, conflicting rots-key ingredients for moral dilemmas. (c) each rot is further broken down with categorical and free text annotations (shown in tiny colored buttons. e.g., "strong" for cultural pressure). the full definition of the low-level rot attributes are in figure 4 . for social norms could significantly hinder ai systems' ability to interact with humans @xcite . in this paper, we introduce social chemistry as a new formalism to study people's social and moral norms over everyday real life situations. our approach based on crowdsourced descriptions of norms is inspired in part by studies in descriptive or applied ethics @xcite , which takes a bottom-up approach by asking people's judgements on various ethical situations. this is in contrast to the top-down approach taken by normative or prescriptive ethics to prescribe the key elements of ethical judgements. the underlying motivation of our study is that we, the nlp field, might have a real chance to contribute to the studies of computational social norms and descriptive ethics through large-scale crowdsourced annotation efforts combined with state-of-the-art neural language models. to that end, we organize descriptive norms via free-text rules-of-thumb (rots) as the basic conceptual units. rule-of-thumb (rot) -a descriptive cultural norm structured as the judgment of an action. for example, "it's rude to run the blender at 5am." each rot is further broken down with 12 theoretically-motivated dimensions of people's judgments such as social judgments of good and bad, theoretical categories of moral foundations, relevant. for example, if the neighbors are african american, it might be worse to call the cops due to racial profiling (eberhardt, 2020). we investigate how state-of-the-art neural language models can learn and generalize out of social-chem-101 to accurately reason about social norms with respect to a previously unseen situation. we term this modeling framework neu-ral norm transformer, and find it is able to generate relevant (and potentially novel) rulesof-thumb conditioned on all attribute dimensions. even so, this breadth of this task proves challenging to current neural models, with humans rating model's adherence to different attributes from 0.28 to 0.91 micro-f1. in addition, we showcase a potential practical use case of computational social norms by analyzing political news headlines through the lens of our framework. we find that our empirical results align with the moral foundation theory of @xcite ; @xcite on how the moral norms of different communities vary depending on their political leanings and news reliability. our empirical studies demonstrate that computational modeling of social norms is a feasible and promising research direction that warrants further investigation. social-chem-101 provides a new resource to teach ai models to learn people's norms, as well as to support novel interdisciplinary research across nlp, computational norms, and descriptive ethics. ## approach the study of social norms have roots in descriptive ethics and moral psychology. they tell us that social norms are culturally-sensitive standards of appropriate conduct. alongside explicit laws and regulations that govern our society, social norms perform the role of providing guidelines on socially appropriate behaviors @xcite @xcite and are responsible for setting implicit expectations of what is socially right or wrong @xcite @xcite . they influence a wide-range of social functions such as preserving biological needs to survival (e.g., refraining from harming or killing), maintaining social civility and order (e.g., maintaining politeness, recognizing personal space), and providing identity and belonging to a community (e.g., respecting the elderly). in turn, these social norms influence how we judge, communicate, and interact with each other. rots our aim is then to forefront these implicit expectations about social norms via rots. we formalize the definition of rots as situationallyrelevant evaluative judgments of social norm, and posit that for any given situation, one or more rots will be evoked in the minds of the interpreter. consider the following situation and its rot. ## social-chem-101 dataset we obtained 104k source situations from 4 text domains ( §3.1), for which we elicited 292k rots from crowd workers ( §3.2). we then define a structured annotation task where workers isolate the central action described by the rot and provide a series of judgments about the rot and the action ( §3.3). in total, we collect 365k structured annotations, performing multiple annotations per rot for a subset of the rots to study the variance in annotations. figure 3 illustrates our dataset statistics. ## model we investigate neural models based on pre-trained language models for learning various sub-tasks derived from social-chem-101. ## morality & political bias to demonstrate a use case of our proposed formalism, we analyze the social norms and expectations evoked in news headlines from news sources of 9 bert and bart performed worse across attributes. left (-) or right (+) reliability agreement -0.015 * * -0.008 * rot cat. morality / ethics -0.069 * * * -0.022 * * * social norms 0.019 * * * -0.006 * it is what it is 0.039 * * * -0.007 * * advice 0.031 * * * 0.033 * * * moral f. care / harm -0.033 * * * -0.016 * * * authority / subversion n.s. n.s. fairness / cheating -0.050 * * * n.s. loyalty / betrayal 0.026 * * * -0.007 * * sanctity / degradation 0.014 * * -0.017 * * * takeaway: we see evidence that a model trained on the social-chem-101 dataset can naturally uncover moral and topical leanings in news sources, mirroring results found in previous news studies. ## related work our formalism heavily draws from works in descriptive ethics and social psychology, but is also inspired by studies in social implicatures and cooperative principles in pragmatics @xcite and the theories of situationally-rooted evocation of frames @xcite . our work adds to the growing literature concerned with distilling reactions to situations @xcite as well as so-cial and moral dynamics in language @xcite . commonly used for coarse-grained analyses of morality in text @xcite @xcite , @xcite introduce the moral foundations lexicon, a dictionary of morality-evoking words (later extended by @xcite . a recent line of work focused on representing social implications of everyday situations in freeform text in a knowledge graph @xcite . relatedly, @xcite introduce social bias frames, a hybrid free-text and categorical formalism to reason about biased implications in language. in contrast, our work formalizes a new type of reasoning around expectations of social norms evoked by situations. finally, concurrent works have developed rich and exciting resources studying similar phenomena. tay et al. (2020) study would you rather? questions, and acharya et al. (2020) investigate ritual understanding across cultures. hendrycks et al. (2020) study ethical questions, attempting to assign a real-valued utility to scenarios across a range of ethical cateogires. and lourie et al. (2020) define the challenge of predicting the r/aita task using the full posts. in contrast to these studies, our work addresses norms by distilling cultural knowledge to a new conceptual level of rules-of-thumb and corresponding structural annotations. ## conclusion we present social-chem-101, an attempt at providing a formalism and resource around the study of grounded social, moral, and ethical norms. our experiments demonstrate preliminary success in generative modeling of structured rots, and corroborate findings of moral leaning in an extrinsic task. comprehensive modeling of social norms presents a promising challenge for nlp work in the future.