Fostering Trust and Quantifying Value of AI and ML

TL;DR

This paper explores how to define, measure, and improve trustworthiness in AI and ML systems, proposing a framework and metrics to quantify trust and enhance the value of AI products.

Contribution

It introduces a novel framework and metrics for quantifying trust in AI/ML systems, addressing a gap in measuring transparency, safety, and bias.

Findings

Proposed a trust score framework for AI/ML systems

Identified key metrics for trustworthiness assessment

Analyzed trust dynamics between providers and users

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.