TrustyAI Explainability Toolkit
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Abstract (arXiv)
Artificial intelligence (AI) is becoming increasingly more popular and can be found in workplaces and homes around the world. The decisions made by such "black box" systems are often opaque; that is, so complex as to be functionally impossible to understand. How do we ensure that these systems are behaving as desired? TrustyAI is an initiative which looks into explainable artificial intelligence (XAI) solutions to address this issue of explainability in the context of both AI models and decision services. This paper presents the TrustyAI Explainability Toolkit, a Java and Python library that provides XAI explanations of decision services and predictive models for both enterprise and data science use-cases. We describe the TrustyAI implementations and extensions to techniques such as LIME, SHAP and counterfactuals, which are benchmarked against existing implementations in a variety of experiments.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 68 references that are in the map. First posted on arXiv 26 Apr 2021.
Builds on
29 of 68 references.
References · 29
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
- 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
- 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
- 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
- 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
- 2020Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?cited by 590
- 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 243
17 more
- 2019Interpretable Counterfactual Explanations Guided by Prototypescited by 223
- 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
- 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 205
- 2019Model-Agnostic Counterfactual Explanations for Consequential Decisionscited by 204
- 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
- 2020The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Modelscited by 125
- 2020Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)cited by 79
- 2018Interpretable Credit Application Predictions With Counterfactual Explanationscited by 43
- 2020Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning modelscited by 40
- 2018Defining Locality for Surrogates in Post-hoc Interpretablitycited by 33
- 2019Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithmscited by 32
- 2021GeCo: Quality Counterfactual Explanations in Real Timecited by 32
- 2020OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithmscited by 30
- 2018DALEX: explainers for complex predictive modelscited by 28
- 2016Generalized Inverse Classificationcited by 22
- 2021Better sampling in explanation methods can prevent dieselgate-like deceptioncited by 5
- 2020An Extension of LIME with Improvement of Interpretability and Fidelitycited by 2
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