  The problem of human trust in artificial intelligence is one of the most
fundamental problems in applied machine learning. Our processes for evaluating
AI trustworthiness have substantial ramifications for ML's impact on science,
health, and humanity, yet confusion surrounds foundational concepts. What does
it mean to trust an AI, and how do humans assess AI trustworthiness? What are
the mechanisms for building trustworthy AI? And what is the role of
interpretable ML in trust? Here, we draw from statistical learning theory and
sociological lenses on human-automation trust to motivate an AI-as-tool
framework, which distinguishes human-AI trust from human-AI-human trust.
Evaluating an AI's contractual trustworthiness involves predicting future model
behavior using behavior certificates (BCs) that aggregate behavioral evidence
from diverse sources including empirical out-of-distribution and out-of-task
evaluation and theoretical proofs linking model architecture to behavior. We
clarify the role of interpretability in trust with a ladder of model access.
Interpretability (level 3) is not necessary or even sufficient for trust, while
the ability to run a black-box model at-will (level 2) is necessary and
sufficient. While interpretability can offer benefits for trust, it can also
incur costs. We clarify ways interpretability can contribute to trust, while
questioning the perceived centrality of interpretability to trust in popular
discourse. How can we empower people with tools to evaluate trust? Instead of
trying to understand how a model works, we argue for understanding how a model
behaves. Instead of opening up black boxes, we should create more behavior
certificates that are more correct, relevant, and understandable. We discuss
how to build trusted and trustworthy AI responsibly.
