Fostering Trust and Quantifying Value of AI and ML
This addresses the problem of undefined trust metrics for AI/ML providers and users, though it appears incremental as it builds on existing discussions without introducing a new paradigm.
The paper tackles the lack of frameworks to quantify trust in AI/ML systems by examining trust dynamics between providers and users, and proposes a framework with metrics to compute a trust score for objectively assessing system trustworthiness over time.
Artificial Intelligence (AI) and Machine Learning (ML) providers have a responsibility to develop valid and reliable systems. Much has been discussed about trusting AI and ML inferences (the process of running live data through a trained AI model to make a prediction or solve a task), but little has been done to define what that means. Those in the space of ML- based products are familiar with topics such as transparency, explainability, safety, bias, and so forth. Yet, there are no frameworks to quantify and measure those. Producing ever more trustworthy machine learning inferences is a path to increase the value of products (i.e., increased trust in the results) and to engage in conversations with users to gather feedback to improve products. In this paper, we begin by examining the dynamic of trust between a provider (Trustor) and users (Trustees). Trustors are required to be trusting and trustworthy, whereas trustees need not be trusting nor trustworthy. The challenge for trustors is to provide results that are good enough to make a trustee increase their level of trust above a minimum threshold for: 1- doing business together; 2- continuation of service. We conclude by defining and proposing a framework, and a set of viable metrics, to be used for computing a trust score and objectively understand how trustworthy a machine learning system can claim to be, plus their behavior over time.