Implicit Humanization in Everyday LLM Moral Judgments

Hoda Ayad; Tanu Mitra

cs.CYMar 23, 2026

Implicit Humanization in Everyday LLM Moral Judgments

Authors: Hoda Ayad, Tanu Mitra

Organizations: University of Washington Seattle, WA, USA

Abstract

Recent adoption of conversational information systems has expanded the scope of user queries to include complex tasks such as personal advice-seeking. However, we identify a specific type of sought advice-a request for a moral judgment (i.e. "who was wrong?") in a social conflict-as an implicitly humanizing query which carries potentially harmful anthropomorphic projections. In this study, we examine the reinforcement of these assumptions in the responses of four major general-purpose LLMs through the use of linguistic, behavioral, and cognitive anthropomorphic cues. We also contribute a novel dataset of simulated user queries for moral judgments. We find current LLM system responses reinforce implicit humanization in queries, potentially exacerbating risks like overreliance or misplaced trust. We call for future work to expand the understanding of anthropomorphism to include implicit userside humanization and to design solutions that address user needs while correcting misaligned expectations of model capabilities.

Anthropomorphism in AI

Anthropomorphism in AI studies how people attribute human traits, minds, and social roles to artificial systems, and how design and language shape those perceptions.

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LLMs Can Better Capture Human Judgments--With the Right Prompts

Danica Dillion, Chen Cecilia Liu, Baihui Wang, Daniele Barolo+4

Complexity Science Hub, Vienna · The Ohio State University · University of Cambridge+2

Human Preference Evaluation LLM Alignment Moral Reasoning in Language Models Human Preference Modeling LLM Prompting LLM Uncertainty Estimation

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Are large language models (LLMs) bad at capturing human judgment? Two commonly stated limitations are that LLMs fail to capture full distributions of responses, and that their judgments are unstable across wording variations. We demonstrate simple prompting strategies that mitigate these limitations. Across two datasets--a U.S.-representative set of 144 moral scenarios and 38 moral beliefs from the International Social Survey Programme's Family and Changing Gender Roles module covering 32 countries--we show how simple elicitation techniques help improve AI-human alignment. First, prompting models to report standard deviations and response proportions recovers the full range of human responses better than common strategies. Second, ensuring scenarios are clear to human participants--as reflected in human confusion ratings--boosts model alignment, and LLMs can track human confusion ratings. At the same time, we find that LLMs' estimates of their own error are poorly calibrated, though they can predict human variability relatively well. These results suggest that asking better questions to LLMs can yield better answers.

Abstract

Anthropomorphism in AI

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