Fostering Trust and Quantifying Value of AI and ML

TL;DR

The paper tackles the problem of quantifying trust in AI/ML systems by defining trust as the willingness to engage with imperfect inferences and proposing a quantitative trust framework built around a trustor–trustee dynamic. It formalizes a Trust Game with remittance $R_u$, magnification $K$, repayment $B_u$, and residual value, extending to $n$ cycles to derive accumulations $A_u$ and $N_v$ and establishing a threshold condition for trust. Through four simulations, it demonstrates how the magnification factor $K$ governs trust dynamics from value-added scenarios ($K>1$) to erosion ($K<0$), and introduces a comprehensive risk-measurement scheme aligned with seven NIST RMF categories to produce a trust score $W = M \cdot S^{\mathsf{T}}$. The approach culminates in a fair-trade region identified via eigenvector analysis of a next-state matrix, enabling scalable, mutually beneficial exchanges. Overall, the work provides a concrete, quantitative pathway to monitor, maintain, and improve trust in AI/ML systems with practical implications for transparency, safety, and user engagement.

Abstract

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.

Figures (7)

• Figure 1: Trust Game payoffs.
• Figure 2: Accumulated gains $(K > 1)$.
• Figure 3: Accumulated Gains $(K = 1)$.
• Figure 4: Accumulated Gains $(0 \leq K < 1)$.
• Figure 5: Accumulated Gains $(K < 0)$.