TY - GEN
T1 - Love the Way You Lie
T2 - 23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023
AU - Kumar, Yulia
AU - Gordon, Zachary
AU - Morreale, Patricia
AU - Li, J. Jenny
AU - Hannon, Brendan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Within the dynamic realm of Artificial Intelligence (AI), models like ChatGPT, Bard, and Bing are renowned for replicating human language. However, their emergence sparks debate over biases and trustworthiness. This research delves into the predominant inaccuracies in chatbots that tend to mislead novices and explores the possibility of establishing an AI Reliability (AIR) framework to fortify trust in these entities. Errors are categorized as factual inaccuracies, misinformation, fabricated data, and deviations from topics, among others. The in-progress AIR Framework offers a meticulous approach to assess chatbot accuracy, leveraging the experiences of nearly 100 CS/IT students with mainly ChatGPT. Recognizing the limitations and hallucinations of these models is essential as they become integral to our lives, underscoring the imperative for responsible and reliable AI.
AB - Within the dynamic realm of Artificial Intelligence (AI), models like ChatGPT, Bard, and Bing are renowned for replicating human language. However, their emergence sparks debate over biases and trustworthiness. This research delves into the predominant inaccuracies in chatbots that tend to mislead novices and explores the possibility of establishing an AI Reliability (AIR) framework to fortify trust in these entities. Errors are categorized as factual inaccuracies, misinformation, fabricated data, and deviations from topics, among others. The in-progress AIR Framework offers a meticulous approach to assess chatbot accuracy, leveraging the experiences of nearly 100 CS/IT students with mainly ChatGPT. Recognizing the limitations and hallucinations of these models is essential as they become integral to our lives, underscoring the imperative for responsible and reliable AI.
KW - AI bias
KW - AI Misinformation
KW - AI Reliability (AIR) Framework
KW - Ethics of AI
KW - Large Language Models (LLMs)
UR - http://www.scopus.com/inward/record.url?scp=85186743537&partnerID=8YFLogxK
U2 - 10.1109/QRS-C60940.2023.00049
DO - 10.1109/QRS-C60940.2023.00049
M3 - Conference contribution
AN - SCOPUS:85186743537
T3 - Proceedings - 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023
SP - 875
EP - 876
BT - Proceedings - 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2023 through 26 October 2023
ER -