  The advances in Artificial Intelligence are creating new opportunities to
improve lives of people around the world, from business to healthcare, from
lifestyle to education. For example, some systems profile the users using their
demographic and behavioral characteristics to make certain domain-specific
predictions. Often, such predictions impact the life of the user directly or
indirectly (e.g., loan disbursement, determining insurance coverage,
shortlisting applications, etc.). As a result, the concerns over such
AI-enabled systems are also increasing. To address these concerns, such systems
are mandated to be responsible i.e., transparent, fair, and explainable to
developers and end-users. In this paper, we present ComplAI, a unique framework
to enable, observe, analyze and quantify explainability, robustness,
performance, fairness, and model behavior in drift scenarios, and to provide a
single Trust Factor that evaluates different supervised Machine Learning models
not just from their ability to make correct predictions but from overall
responsibility perspective. The framework helps users to (a) connect their
models and enable explanations, (b) assess and visualize different aspects of
the model, such as robustness, drift susceptibility, and fairness, and (c)
compare different models (from different model families or obtained through
different hyperparameter settings) from an overall perspective thereby
facilitating actionable recourse for improvement of the models. It is model
agnostic and works with different supervised machine learning scenarios (i.e.,
Binary Classification, Multi-class Classification, and Regression) and
frameworks. It can be seamlessly integrated with any ML life-cycle framework.
Thus, this already deployed framework aims to unify critical aspects of
Responsible AI systems for regulating the development process of such real
systems.
