In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes

TL;DR

This study investigates how various factors such as stakes, authority, interpretability, and transparency influence people's trust in AI-supported decision-making processes across different scenarios.

Contribution

It identifies key factors affecting trust in AI-infused decisions and provides empirical insights into their relative influence.

Findings

Decision stakes significantly affect trust levels.

Model interpretability increases perceived trustworthiness.

Social transparency enhances trust in AI decisions.

Abstract

Many decision-making processes have begun to incorporate an AI element, including prison sentence recommendations, college admissions, hiring, and mortgage approval. In all of these cases, AI models are being trained to help human decision makers reach accurate and fair judgments, but little is known about what factors influence the extent to which people consider an AI-infused decision-making process to be trustworthy. We aim to understand how different factors about a decision-making process, and an AI model that supports that process, influences peoples' perceptions of the trustworthiness of that process. We report on our evaluation of how seven different factors -- decision stakes, decision authority, model trainer, model interpretability, social transparency, and model confidence -- influence ratings of trust in a scenario-based study.