Advances in Automatically Rating the Trustworthiness of Text Processing Services

TL;DR

This paper explores methods to automatically rate the trustworthiness of text processing AI services, aiming to inform consumers and improve decision-making through independent assessments.

Contribution

It introduces recent progress in rating methods for text-based AI services and discusses challenges and future directions for principled, causality-based trust assessments.

Findings

Promising rating methods validated by user studies

Identification of key challenges in trust rating methodologies

Proposed vision for multi-modal, causality-based ratings

Abstract

AI services are known to have unstable behavior when subjected to changes in data, models or users. Such behaviors, whether triggered by omission or commission, lead to trust issues when AI works with humans. The current approach of assessing AI services in a black box setting, where the consumer does not have access to the AI's source code or training data, is limited. The consumer has to rely on the AI developer's documentation and trust that the system has been built as stated. Further, if the AI consumer reuses the service to build other services which they sell to their customers, the consumer is at the risk of the service providers (both data and model providers). Our approach, in this context, is inspired by the success of nutritional labeling in food industry to promote health and seeks to assess and rate AI services for trust from the perspective of an independent stakeholder. The ratings become a means to communicate the behavior of AI systems so that the consumer is informed about the risks and can make an informed decision. In this paper, we will first describe recent progress in developing rating methods for text-based machine translator AI services that have been found promising with user studies. Then, we will outline challenges and vision for a principled, multi-modal, causality-based rating methodologies and its implication for decision-support in real-world scenarios like health and food recommendation.