  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.
