Which Artificial Intelligences Do People Care About Most? A Conjoint Experiment on Moral Consideration

TL;DR

This study systematically quantifies how 11 AI features influence moral consideration using a preregistered, partial-profile conjoint design with 1,163 participants. All features increase perceived wrongness of harming the AI, with the largest effects stemming from human-like bodies and prosocial capabilities (emotion expression/recognition, cooperation, and moral judgment). A three-category pattern emerges: strongest effects for moral judgment and emotion expression, moderate effects for emotion recognition, body, and cooperation, and weaker effects for autonomy, complexity, damage avoidance, language, and purpose. The findings have practical implications for AI design and human–AI interaction, suggesting that prosocial behavior and human-like embodiment can elevate perceived moral status, but designers must consider potential ethical and psychological trade-offs in real-world deployment.

Abstract

Many studies have identified particular features of artificial intelligences (AI), such as their autonomy and emotion expression, that affect the extent to which they are treated as subjects of moral consideration. However, there has not yet been a comparison of the relative importance of features as is necessary to design and understand increasingly capable, multi-faceted AI systems. We conducted an online conjoint experiment in which 1,163 participants evaluated descriptions of AIs that varied on these features. All 11 features increased how morally wrong participants considered it to harm the AIs. The largest effects were from human-like physical bodies and prosociality (i.e., emotion expression, emotion recognition, cooperation, and moral judgment). For human-computer interaction designers, the importance of prosociality suggests that, because AIs are often seen as threatening, the highest levels of moral consideration may only be granted if the AI has positive intentions.

Figures (2)

• Figure 1: Example choice task. Each participant completed 13 such choice tasks. The seven features presented to participants were selected randomly and presented in a random order that was held fixed across tasks; the levels for each of the features were randomized in each task.
• Figure 2: Average Marginal Component Effects. The dots with horizontal bars (color-coded for each feature) represent the means and 95% confidence intervals of the effects of feature level on the probability of choosing an artificial being as being more wrong to harm relative to the baseline level, which is shown as a dot on the vertical line crossing the x-axis at 0%. Where the bars do not cross the vertical line at 0%, the effects can be interpreted as statistically significant. Confidence intervals are calculated based on standard errors clustered at the respondent level.