  In the past decade, Artificial Intelligence (AI) has become a part of our
daily lives due to major advances in Machine Learning (ML) techniques. In spite
of an explosive growth in the raw AI technology and in consumer facing
applications on the internet, its adoption in business applications has
conspicuously lagged behind. For business/mission-critical systems, serious
concerns about reliability and maintainability of AI applications remain. Due
to the statistical nature of the output, software 'defects' are not well
defined. Consequently, many traditional quality management techniques such as
program debugging, static code analysis, functional testing, etc. have to be
reevaluated. Beyond the correctness of an AI model, many other new quality
attributes, such as fairness, robustness, explainability, transparency, etc.
become important in delivering an AI system. The purpose of this paper is to
present a view of a holistic quality management framework for ML applications
based on the current advances and identify new areas of software engineering
research to achieve a more trustworthy AI.
