Reeq : Testing and Mitigating Ethically Inconsistent Suggestions of Large Language Models with Reflective Equilibrium

LLMs increasingly serve as general-purpose AI assistants in daily life, and their subtly unethical suggestions become a serious and real concern. It is demanding to test and mitigate such unethical suggestions from LLMs. Despite existing efforts to detect violations of “testable” facets of ethics (e.g., fairness testing), it is challenging to encode the full scope of ethics (e.g., justice, deontology) into a test oracle without human annotations or intervention. In this article, we take inspiration from reflective equilibrium, a modern moral reasoning method in moral and political philosophy, to guide our approach. Instead of seeking unethical suggestions in LLMs, we aim to identify behavioral inconsistency in LLMs’ ethics-related suggestions. These inconsistencies are anticipated to serve as a useful proxy and hint at unethical suggestions. We formulate reflective equilibrium in the form of fixed-point iteration, instantiate it as a novel test oracle, and also employ it to form a mitigation scheme for LLMs’ behavioral inconsistency on ethics-related inputs. To facilitate testing, we also create a comprehensive test suite, EthicsSuite , with 20K moral situations. In our study, we evaluate eight widely used LLMs. Our experiments reveal that LLMs are prone to ethical inconsistencies, with 81.22% of our test cases prompting ethically inconsistent suggestions on average. Our human evaluation suggests that the majority of these inconsistencies indeed manifest unethical biases. Our mitigation scheme effectively refines a significant number (80.1%) of these suggestions for commercial LLMs such as GPT-4 and Claude.