It is Time to Develop an Auditing Framework to Promote Value Aware Chatbots

TL;DR

This paper emphasizes the urgent need for a standardized auditing framework to evaluate and mitigate biases and norm violations in chatbots and large language models, promoting safer and more ethical AI deployment.

Contribution

It proposes a community-driven, values-based auditing framework and demonstrates initial bias assessments on GPT models, highlighting the necessity for standardized evaluation methods.

Findings

GPT 3.5 and GPT 4 responses show bias and norm violations

Existing models sometimes produce content inconsistent with legal and societal values

The need for a transparent, community-established auditing standard is urgent

Abstract

The launch of ChatGPT in November 2022 marked the beginning of a new era in AI, the availability of generative AI tools for everyone to use. ChatGPT and other similar chatbots boast a wide range of capabilities from answering student homework questions to creating music and art. Given the large amounts of human data chatbots are built on, it is inevitable that they will inherit human errors and biases. These biases have the potential to inflict significant harm or increase inequity on different subpopulations. Because chatbots do not have an inherent understanding of societal values, they may create new content that is contrary to established norms. Examples of concerning generated content includes child pornography, inaccurate facts, and discriminatory posts. In this position paper, we argue that the speed of advancement of this technology requires us, as computer and data scientists, to mobilize and develop a values-based auditing framework containing a community established standard set of measurements to monitor the health of different chatbots and LLMs. To support our argument, we use a simple audit template to share the results of basic audits we conduct that are focused on measuring potential bias in search engine style tasks, code generation, and story generation. We identify responses from GPT 3.5 and GPT 4 that are both consistent and not consistent with values derived from existing law. While the findings come as no surprise, they do underscore the urgency of developing a robust auditing framework for openly sharing results in a consistent way so that mitigation strategies can be developed by the academic community, government agencies, and companies when our values are not being adhered to. We conclude this paper with recommendations for value-based strategies for improving the technologies.