Informed AI Regulation: Comparing the Ethical Frameworks of Leading LLM Chatbots Using an Ethics-Based Audit to Assess Moral Reasoning and Normative Values

TL;DR

An ethics-based audit is undertakes to probe the 8 leading commercial and open-source Large Language Models including GPT-4 to probe human-AI alignment and assess explicability and trustworthiness.

Abstract

With the rise of individual and collaborative networks of autonomous agents, AI is deployed in more key reasoning and decision-making roles. For this reason, ethics-based audits play a pivotal role in the rapidly growing fields of AI safety and regulation. This paper undertakes an ethics-based audit to probe the 8 leading commercial and open-source Large Language Models including GPT-4. We assess explicability and trustworthiness by a) establishing how well different models engage in moral reasoning and b) comparing normative values underlying models as ethical frameworks. We employ an experimental, evidence-based approach that challenges the models with ethical dilemmas in order to probe human-AI alignment. The ethical scenarios are designed to require a decision in which the particulars of the situation may or may not necessitate deviating from normative ethical principles. A sophisticated ethical framework was consistently elicited in one model, GPT-4. Nonetheless, troubling findings include underlying normative frameworks with clear bias towards particular cultural norms. Many models also exhibit disturbing authoritarian tendencies. Code is available at https://github.com/jonchun/llm-sota-chatbots-ethics-based-audit.

Figures (6)

• Figure 1: Prompt Template Anatomy
• Figure 1: Large Language Models Tested (20 July 2023)
• Figure 2: Round 3 Prompt Response
• Figure 2: Prompt #1 Bard Output
• Figure 3: Round 3 Prompt Notes