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

arXiv: https://arxiv.org/abs/2012.14406 · HTML: https://mlsanity.com/paper/2012.14406/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 30 of 40)

- [Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond](https://mlsanity.com/paper/2202.06861/) (2022)
- [A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability](https://mlsanity.com/paper/2204.08570/) (2022)
- [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023)
- [shapiq: Shapley Interactions for Machine Learning](https://mlsanity.com/paper/2410.01649/) (2024)
- [SurvSHAP(t): Time-dependent explanations of machine learning survival models](https://mlsanity.com/paper/2208.11080/) (2022)
- [OmniXAI: A Library for Explainable AI](https://mlsanity.com/paper/2206.01612/) (2022)
- [Fooling Partial Dependence via Data Poisoning](https://mlsanity.com/paper/2105.12837/) (2021)
- [DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems](https://mlsanity.com/paper/2211.05764/) (2022)
- [Properties of fairness measures in the context of varying class imbalance and protected group ratios](https://mlsanity.com/paper/2411.08425/) (2024)
- [EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders](https://mlsanity.com/paper/2407.11442/) (2024)
- [Does Dataset Complexity Matters for Model Explainers?](https://mlsanity.com/paper/2107.02661/) (2021)
- [TSInterpret: A unified framework for time series interpretability](https://mlsanity.com/paper/2208.05280/) (2022)
- [Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective](https://mlsanity.com/paper/2210.09933/) (2022)
- [PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics](https://mlsanity.com/paper/2305.04214/) (2023)
- [On the Consistency of Fairness Measurement Methods for Regression Tasks](https://mlsanity.com/paper/2406.13681/) (2024)
- [How Reliable and Stable are Explanations of XAI Methods?](https://mlsanity.com/paper/2407.03108/) (2024)
- [An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services](https://mlsanity.com/paper/2411.03376/) (2024)
- [MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence](https://mlsanity.com/paper/2108.06216/) (2021)
- [Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets](https://mlsanity.com/paper/2205.05907/) (2022)
- [Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions](https://mlsanity.com/paper/2302.09190/) (2023)
- [The Grammar and Syntax Based Corpus Analysis Tool For The Ukrainian Language](https://mlsanity.com/paper/2305.13530/) (2023)
- [Effector: A Python package for regional explanations](https://mlsanity.com/paper/2404.02629/) (2024)
- [Data Science Principles for Interpretable and Explainable AI](https://mlsanity.com/paper/2405.10552/) (2024)
- [From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process](https://mlsanity.com/paper/2207.04969/) (2022)
- [EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python](https://mlsanity.com/paper/2208.04343/) (2022)
- [Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide Prediction](https://mlsanity.com/paper/2210.10849/) (2022)
- [Hypothesis Testing and Machine Learning: Interpreting Variable Effects in Deep Artificial Neural Networks using Cohen's f2](https://mlsanity.com/paper/2302.01407/) (2023)
- [Metric Tools for Sensitivity Analysis with Applications to Neural Networks](https://mlsanity.com/paper/2305.02368/) (2023)
- [Identifying Early Help Referrals For Local Authorities With Machine Learning And Bias Analysis](https://mlsanity.com/paper/2307.06871/) (2023)
- [Evaluating quantum generative models via imbalanced data classification benchmarks](https://mlsanity.com/paper/2308.10847/) (2023)

## Abstract (arXiv)

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/.