  Artificial intelligence (AI) is becoming increasingly more popular and can be
found in workplaces and homes around the world. However, how do we ensure trust
in these systems? Regulation changes such as the GDPR mean that users have a
right to understand how their data has been processed as well as saved.
Therefore if, for example, you are denied a loan you have the right to ask why.
This can be hard if the method for working this out uses "black box" machine
learning techniques such as neural networks. TrustyAI is a new initiative which
looks into explainable artificial intelligence (XAI) solutions to address
trustworthiness in ML as well as decision services landscapes.
  In this paper we will look at how TrustyAI can support trust in decision
services and predictive models. We investigate techniques such as LIME, SHAP
and counterfactuals, benchmarking both LIME and counterfactual techniques
against existing implementations. We also look into an extended version of
SHAP, which supports background data selection to be evaluated based on
quantitative data and allows for error bounds.
