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The increasing amount of available data, computing power, and the constantpursuit for higher performance results in the growing complexity of predictivemodels. Their black-box nature leads to opaqueness debt phenomenon inflictingincreased risks of discrimination, lack of reproducibility, and deflatedperformance due to data drift. To manage these risks, good MLOps practices askfor better validation of model performance and fairness, higher explainability,and continuous monitoring. The necessity of deeper model transparency appearsnot only from scientific and social domains, but also emerging laws andregulations on artificial intelligence. To facilitate the development ofresponsible machine learning models, we showcase dalex, a Python package whichimplements the model-agnostic interface for interactive model exploration. Itadopts the design crafted through the development of various tools forresponsible machine learning; thus, it aims at the unification of the existingsolutions. This library's source code and documentation are available underopen license at https://python.drwhy.ai/.

dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python