  Modern AI systems are reaping the advantage of novel learning methods. With
their increasing usage, we are realizing the limitations and shortfalls of
these systems. Brittleness to minor adversarial changes in the input data,
ability to explain the decisions, address the bias in their training data, high
opacity in terms of revealing the lineage of the system, how they were trained
and tested, and under which parameters and conditions they can reliably
guarantee a certain level of performance, are some of the most prominent
limitations. Ensuring the privacy and security of the data, assigning
appropriate credits to data sources, and delivering decent outputs are also
required features of an AI system. We propose the tutorial on Trustworthy AI to
address six critical issues in enhancing user and public trust in AI systems,
namely: (i) bias and fairness, (ii) explainability, (iii) robust mitigation of
adversarial attacks, (iv) improved privacy and security in model building, (v)
being decent, and (vi) model attribution, including the right level of credit
assignment to the data sources, model architectures, and transparency in
lineage.
