  The increasing amount of available data, computing power, and the constant
pursuit for higher performance results in the growing complexity of predictive
models. Their black-box nature leads to opaqueness debt phenomenon inflicting
increased risks of discrimination, lack of reproducibility, and deflated
performance due to data drift. To manage these risks, good MLOps practices ask
for better validation of model performance and fairness, higher explainability,
and continuous monitoring. The necessity of deeper model transparency appears
not only from scientific and social domains, but also emerging laws and
regulations on artificial intelligence. To facilitate the development of
responsible machine learning models, we showcase dalex, a Python package which
implements the model-agnostic interface for interactive model exploration. It
adopts the design crafted through the development of various tools for
responsible machine learning; thus, it aims at the unification of the existing
solutions. This library's source code and documentation are available under
open license at https://python.drwhy.ai/.
