AI Trust Framework and Maturity Model: Improving Metrics for Evaluating Security & Trust in Autonomous Human Machine Teams & Systems

Open Access
Article
Conference Proceedings
Authors: Michael MylreaNikki Robinson

Abstract: The following article develops an AI Trust Framework and Maturity Model (AI-TFMM) to improve trust in AI technologies used by Autonomous Human Machine Teams & Systems (A-HMT-S). The framework establishes a methodology to improve quantification of trust in AI technologies. Key areas of exploration include security, privacy, explainability, transparency and other requirements for AI technologies to be ethical in their development and application. A maturity model framework approach to measuring trust is applied to improve gaps in quantifying trust and associated metrics of evaluation. Finding the right balance between performance, governance and ethics also raises several critical questions on AI technology and trust. Research examines methods needed to develop an AI-TFMM. Validation tests of the framework are run and analyzed against the popular AI technology (Chat GPT). OpenAI's GPT, which stands for "Generative Pre-training Transformer," is a deep learning language model that can generate human-like text by predicting the next word in a sequence based on a given prompt. ChatGPT is a version of GPT that is tailored for conversation and dialogue, and it has been trained on a dataset of human conversations to generate responses that are coherent and relevant to the context. The article concludes with results and conclusions from testing the AI Trust Framework and Maturity Model (AI-TFMM) applied to AI technology. Based on these findings, this paper highlights gaps that could be filled with future research to improve the accuracy, efficacy, application, and methodology of the AI-TFMM.

Keywords: Autonomous, Human, Machine, Teams, Artificial Intelligence, Machine Learning, Trust

DOI: 10.54941/ahfe1003760

Cite this paper:

Downloads
152
Visits
331
Download