  We are witnessing the emergence of an AI economy and society where AI
technologies are increasingly impacting health care, business, transportation
and many aspects of everyday life. Many successes have been reported where AI
systems even surpassed the accuracy of human experts. However, AI systems may
produce errors, can exhibit bias, may be sensitive to noise in the data, and
often lack technical and judicial transparency resulting in reduction in trust
and challenges in their adoption. These recent shortcomings and concerns have
been documented in scientific but also in general press such as accidents with
self driving cars, biases in healthcare, hiring and face recognition systems
for people of color, seemingly correct medical decisions later found to be made
due to wrong reasons etc. This resulted in emergence of many government and
regulatory initiatives requiring trustworthy and ethical AI to provide accuracy
and robustness, some form of explainability, human control and oversight,
elimination of bias, judicial transparency and safety. The challenges in
delivery of trustworthy AI systems motivated intense research on explainable AI
systems (XAI). Aim of XAI is to provide human understandable information of how
AI systems make their decisions. In this paper we first briefly summarize
current XAI work and then challenge the recent arguments of accuracy vs.
explainability for being mutually exclusive and being focused only on deep
learning. We then present our recommendations for the use of XAI in full
lifecycle of high stakes trustworthy AI systems delivery, e.g. development,
validation and certification, and trustworthy production and maintenance.
