Adding guardrails to advanced chatbots
Not on the map. Why?
Abstract (arXiv)
Generative AI models continue to become more powerful. The launch of ChatGPT in November 2022 has ushered in a new era of AI. ChatGPT and other similar chatbots have a range of capabilities, from answering student homework questions to creating music and art. There are already concerns that humans may be replaced by chatbots for a variety of jobs. Because of the wide spectrum of data chatbots are built on, we know that they will have human errors and human biases built into them. These biases may cause significant harm and/or inequity toward different subpopulations. To understand the strengths and weakness of chatbot responses, we present a position paper that explores different use cases of ChatGPT to determine the types of questions that are answered fairly and the types that still need improvement. We find that ChatGPT is a fair search engine for the tasks we tested; however, it has biases on both text generation and code generation. We find that ChatGPT is very sensitive to changes in the prompt, where small changes lead to different levels of fairness. This suggests that we need to immediately implement "corrections" or mitigation strategies in order to improve fairness of these systems. We suggest different strategies to improve chatbots and also advocate for an impartial review panel that has access to the model parameters to measure the levels of different types of biases and then recommends safeguards that move toward responses that are less discriminatory and more accurate.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 44 references that are in the map. First posted on arXiv 13 Jun 2023.
Builds on
13 of 44 references.
References · 13
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 849
- 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 377
- 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
- 2021Want To Reduce Labeling Cost? GPT-3 Can Helpcited by 196
- 2016Dual Learning for Machine Translationcited by 146
- 2019Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overviewcited by 137
- 2021Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarizationcited by 76
- 2017Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American Englishcited by 69
- 2018A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognitioncited by 66
- 2020STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generationcited by 56
- 2018Challenges for Toxic Comment Classification: An In-Depth Error Analysiscited by 55
Built on by
Citing papers per year: 2024: 4; 2025: 1.
5 papers.
- 2024Large Language Model Unlearning via Embedding-Corrupted Promptscited by 112
- 2025Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignmentcited by 14
- 2024Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture for Multi-Layered Guardrails of Foundation Model Based Agentscited by 13
- 2024Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignmentcited by 3
- 2024AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Developmentcited by 1
- 2025Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment22 Jan 2025
- 2024AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development5 Nov 2024
- 2024Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture for Multi-Layered Guardrails of Foundation Model Based Agents5 Aug 2024
- 2024Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment16 Jun 2024
- 2024Large Language Model Unlearning via Embedding-Corrupted Prompts12 Jun 2024