A nthro S core: A Computational Linguistic Measure of Anthropomorphism (2024)

Abstract: Anthropomorphism, or the attribution of human-like characteristics to non-human entities, has shaped conversations about the impacts and possibilities of technology. We present AnthroScore, an automatic metric of implicit anthropomorphism in language. We use a masked language model to quantify how non-human entities are implicitly framed as human by the surrounding context. We show that AnthroScore corresponds with human judgments of anthropomorphism and dimensions of anthropomorphism described in social science literature. Motivated by concerns of misleading anthropomorphism in computer science discourse, we use AnthroScore to analyze 15 years of research papers and downstream news articles. In research papers, we find that anthropomorphism has steadily increased over time, and that papers related to language models have the most anthropomorphism. Within ACL papers, temporal increases in anthropomorphism are correlated with key neural advancements. Building upon concerns of scientific misinformation in mass media, we identify higher levels of anthropomorphism in news headlines compared to the research papers they cite. Since AnthroScore is lexicon-free, it can be directly applied to a wide range of text sources.

## introduction anthropomorphism, or assigning human-like characteristics to non-human entities, is commonplace in people's interactions with technology @xcite . however, anthropomorphizing language can suggest undue accountability and agency in technologies like artificial intelligence (ai) and language models (lms). projecting human qualities onto these tools facilitates misinformation about their true capabilities, over-reliance on technology, and corporate avoidance of responsibility @xcite @xcite @xcite . such metaphors are especially consequential in public discourse ## background: anthropomorphism we ground our work in the social science literature on anthropomorphism. previous scholars define anthropomorphism as "the attribution of distinctively human-like feelings, mental states, and behavioral characteristics" to non-human entities @xcite @xcite . these characteristics entail definition 1. "the ability to ( 1 ) experience emotion and feel pain (affective mental states), (2) act and produce an effect on their environment (behavioral potential), and (3) think and hold beliefs (cognitive mental states)" @xcite . scientific and technological concepts, especially human-centered ones, are particularly susceptible to anthropomorphic metaphors and interpretations @xcite . according to the media equation theory from social psychology, people tend to assign human characteristics to computers, interacting with them as if they were social actors @xcite . this phenomenon leads people to behave and refer to computers in ways that are typical of human-human interactionssuch as attributing personality-even when they are aware that they are interacting with a non-human entity @xcite . ## discussion in this section, we first explore the underlying causes of anthropomorphism in text, including verb choice and norms of different academic fields. (we discuss other linguistic features of anthropomorphism in appendix e.) based on these observations, we then provide recommendations for individual authors and the broader community to avoid misleading anthropomorphism. ## other applications of anthroscore while we focus on anthropomorphism in research papers and downstream news articles, an-throscore can be applied to many other settings, including other research areas, analyzing full papers and comparing across disciplines; perceptions of corporations and brands, which has political and legal implications @xcite @xcite ; conspiracy theories, which @xcite link to anthropomorphism; and relationships with pets and objects @xcite . anthroscore is a first step toward analyzing anthropomorphism across different cultures, languages, and times. leveraged in large-scale quantitative contexts, an-throscore and its extensions facilitate deeper insights into human behavior. moreover, anthropomorphism is closely related to discussions of agency, human exceptionalism, and subjectivity @xcite @xcite . there is a rich literature on the implications of anthropomorphism in relation to biology and the natural world @xcite @xcite . also, feminist studies of science and technology have long leveraged anthropomorphism in their challenging of the dominant values and traditional boundaries between subject and object in science @xcite @xcite . anthroscore enables engagement with these topics using a quantitative lens. ## limitations our analysis is limited to english data, where thirdperson singular pronouns mark animacy. however, many other languages have various grammati-cal markers of animacy @xcite , to which our method can be extended to study how various cultural factors, societal values, and religious beliefs affect the tendency to anthropomorphize non-human entities, as well as the meaning and perception of anthropomorphism in different contexts @xcite @xcite . outputs from pre-trained mlms only reflect the contexts and cultures of the models' training data, which does not reflect the diversity of the real world @xcite . in particular, our method implicitly relies on the idea that the distribution of the mlm has a representation of both "human" (from text that contains human pronouns) and "nonhuman" (from text that contains non-human pronouns). however, the definitions of these concepts are not static, and the mlm may only capture a subset of possible definitions. as the long literature on dehumanization shows, many people are not recognized as human in various ways: deprived of human rights, or not viewed and treated as fully human by society or in legal and state contexts. these phenomena are reinforced by language, as "the very terms that confer 'humanness' on some individuals are those that deprive certain other individuals of the possibility of achieving that status" @xcite . it is well-established that mlms reflect social biases @xcite @xcite , which also percolate into our measure. that being said, we focus on the anthropomorphism of objects and not the humanity of people, so these concerns should not affect the use of our metric. also, since anthropomorphizing metaphors are ubiquitous in english, it is inevitable that they are also embedded into the mlm's probability distributions; thus, the patterns of anthropomorphism that we uncover is a lower bound on the amount of anthropomorphism in the language of a text. regardless of any spectator, he buries his head in the water above his eyes, and greedily swallows great mouthfuls, at the rate of about ten in a minute" @xcite . we know that the terms set and tortoise all refer to tortoises, so these are the entities x that we will mask. our method works as follows: poetry example suppose we are interested in the anthropomorphism of birds in emily dickinson's poems. the input texts t are the poems, and the target entities x are words referring to birds like "bird," "hummingbird", "owl", etc. @xcite . then, our method outputs anthroscore for each poem as well as each sentence mentioning a bird.