  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.
