CARLA
responsible-ai-toolbox
| CARLA | responsible-ai-toolbox | |
|---|---|---|
| 2 | 2 | |
| 263 | 1,208 | |
| 0.4% | 2.7% | |
| 0.0 | 9.6 | |
| 7 months ago | 13 days ago | |
| Python | TypeScript | |
| MIT License | MIT License |
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CARLA
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[R] CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Abstract: Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve favourable outcomes in the future (e.g., insurance approval). Choosing an appropriate method is a crucial aspect for meaningful counterfactual explanations. As documented in recent reviews, there exists a quickly growing literature with available methods. Yet, in the absence of widely available open–source implementations, the decision in favour of certain models is primarily based on what is readily available. Going forward – to guarantee meaningful comparisons across explanation methods – we present CARLA (Counterfactual And Recourse Library), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods. We have open sourced CARLA and our experimental results on GitHub, making them available as competitive baselines. We welcome contributions from other research groups and practitioners.
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University of Tübingen Researchers Open-Source ‘CARLA’, A Python Library for Benchmarking Counterfactual Explanation Methods Across Data Sets and Machine Learning Models
4 Min Read| Paper | Github
responsible-ai-toolbox
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Debugging Machine Learning "[N]"
http://erroranalysis.ai/ is a new open-source tool for in-depth understanding and diagnosis of Machine Learning Errors. The tool is available as a highly interactive jupyter widget and brings in several visualization primitives all centered around debugging activities in ML (main repo: https://github.com/microsoft/responsible-ai-widgets).
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[N] Visualize Your Model Errors! Microsoft Toolkit Identifies and Diagnoses ML Failures
The toolkit is on the project GitHub. Additional information is available on the Error Analysis website.
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