Ethics Testing: Proactive Identification of Generative AI System Harms

TL;DR

This paper introduces ethics testing as a systematic approach to identify potential harms in content generated by AI systems, addressing a gap in current testing methodologies.

Contribution

It proposes the novel concept of ethics testing to detect unethical behaviors and harms in generative AI outputs, supported by five case studies.

Findings

Ethics testing can systematically identify software harms in AI-generated content.

Five case studies demonstrate the practical application of ethics testing.

The approach addresses limitations of existing fairness testing methods.

Abstract

Generative Artificial Intelligence (GAI) systems that can automatically generate content in the form of source code or other contents (e.g., images) has seen increasing popularity due to the emergence of tools such as ChatGPT which rely on Large Language Models (LLMs). Misuse of the automatically generated content can incur serious consequences due to potential harms in the generated content. Despite the importance of ensuring the quality of automatically generated content, there is little to no approach that can systematically generate tests for identifying software harms in the content generated by these GAI systems. In this article, we introduce the novel concept of ethics testing which aims to systematically generate tests for identifying software harms. Different from existing testing methodologies (e.g., fairness testing that aims to identifying software discrimination), ethics testing aims to systematically detect software harms that could be induced due to unethical behavior (e.g., harmful behavior or behavior that violates intellectual property rights) in automatically generated content. We introduced the concept of ethics testing, discussed the challenges therewithin, and conducted five case studies to show how ethics testing can be performed for generative AI systems.