Towards Composable Bias Rating of AI Services
  A new wave of decision-support systems are being built today using AI
services that draw insights from data (like text and video) and incorporate
them in human-in-the-loop assistance. However, just as we expect humans to be
ethical, the same expectation needs to be met by automated systems that
increasingly get delegated to act on their behalf. A very important aspect of
an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias
occurs when the data distribution is not representative enough of the natural
phenomenon one wants to model and reason about. The possibly biased behavior of
a service is hard to detect and handle if the AI service is merely being used
and not developed from scratch, since the training data set is not available.
In this situation, we envisage a 3rd party rating agency that is independent of
the API producer or consumer and has its own set of biased and unbiased data,
with customizable distributions. We propose a 2-step rating approach that
generates bias ratings signifying whether the AI service is unbiased
compensating, data-sensitive biased, or biased. The approach also works on
composite services. We implement it in the context of text translation and
report interesting results.
