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

arXiv: https://arxiv.org/abs/2409.01539 · HTML: https://mlsanity.com/paper/2409.01539/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Dual Learning for Machine Translation](https://mlsanity.com/paper/1611.00179/) (2016)
- [Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://mlsanity.com/paper/1912.11078/) (2019)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English](https://mlsanity.com/paper/1707.00061/) (2017)
- [A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition](https://mlsanity.com/paper/1807.10857/) (2018)
- [STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation](https://mlsanity.com/paper/2010.01717/) (2020)

## Built on by (most cited first, 1 of 1)

- [What Makes Code Generation Ethically Sourced?](https://mlsanity.com/paper/2507.19743/) (2025)

## Abstract (arXiv)

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.