In a new working paper, Crocodile Tears: Can the Ethical-Moral Intelligence of AI Models Be Trusted?, Allen Lab authors Sarah Hubbard, David Kidd, and Andrei Stupu introduce an ethical-moral intelligence framework for evaluating AI models across dimensions of moral expertise, sensitivity, coherence, and transparency.
As AI becomes increasingly embedded into every aspect of our lives, there is evidence that people are turning to these systems for guidance on complex issues and moral dilemmas. Whether or not one agrees that people should do so, the fact that they are necessitates a clearer understanding of the moral reasoning of these systems. To address this gap, Crocodile Tears: Can the Ethical-Moral Intelligence of AI Be Trusted? introduces an ethical-moral intelligence (EMI) framework for evaluating AI models across dimensions of moral expertise, sensitivity, coherence, and transparency. We present findings from a pre-registered experiment testing the moral sensitivity in four AI models (Claude, GPT, Llama, and DeepSeek) using ethically challenging scenarios. While models demonstrate moral sensitivity to ethical dilemmas in ways that closely mimic human responses, they exhibit greater certainty than humans when choosing between conflicting sacred values, despite recognizing such tragic trade-offs as difficult. This discrepancy between reported difficulty and decisiveness raises important questions about their coherence and transparency, undermining trustworthiness. The research reveals a critical need for more comprehensive ethical evaluation of AI systems. We discuss the implications of these specific findings, how psychological methods might be applied to understand the ethical-moral intelligence of AI models, and outline recommendations for developing more ethically aware AI that augments human moral reasoning.
Sarah Hubbard is the Associate Director for Technology & Democracy at the Ash Center’s Allen Lab for Democracy Renovation and was previously a Technology & Public Purpose Fellow at the Belfer Center.
David Kidd is a member of the Ash Center’s Allen Lab for Democracy Renovation in addition to working for Harvard University’s Edmond and Lily Safra Center.
Andrei Stupu was a previous Allen Lab for Democracy Renovation fellow at the Ash Center.
The views expressed in this article are those of the author(s) alone and do not necessarily represent the positions of the Ash Center or its affiliates.
Sunset Section 230 and Unleash the First Amendment
Allen Lab for Democracy Renovation Senior Fellow Allison Stanger, in collaboration with Jaron Lanier and Audrey Tang, envision a post-Section 230 landscape that fosters innovation in digital public spaces using models optimized for public interest rather than attention metrics.
Digital Civic Infrastructure for Massachusetts Workshop
The Allen Lab for Democracy Renovation and Bloomberg Center for Cities brought together civic technologists, researchers, as well as municipal and state leaders across Massachusetts for a workshop on digital civic infrastructure.
Creating a healthy digital civic infrastructure ecosystem means not just deploying technology for the sake of efficiency, but thoughtfully designing tools built to enhance democratic engagement from connection to action.
Sunset Section 230 and Unleash the First Amendment
Allen Lab for Democracy Renovation Senior Fellow Allison Stanger, in collaboration with Jaron Lanier and Audrey Tang, envision a post-Section 230 landscape that fosters innovation in digital public spaces using models optimized for public interest rather than attention metrics.
Digital Civic Infrastructure for Massachusetts Workshop
The Allen Lab for Democracy Renovation and Bloomberg Center for Cities brought together civic technologists, researchers, as well as municipal and state leaders across Massachusetts for a workshop on digital civic infrastructure.
Creating a healthy digital civic infrastructure ecosystem means not just deploying technology for the sake of efficiency, but thoughtfully designing tools built to enhance democratic engagement from connection to action.