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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2206.11104 |
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| _version_ | 1866916159003033600 |
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| author | Agarwal, Chirag Ley, Dan Krishna, Satyapriya Saxena, Eshika Pawelczyk, Martin Johnson, Nari Puri, Isha Zitnik, Marinka Lakkaraju, Himabindu |
| author_facet | Agarwal, Chirag Ley, Dan Krishna, Satyapriya Saxena, Eshika Pawelczyk, Martin Johnson, Nari Puri, Isha Zitnik, Marinka Lakkaraju, Himabindu |
| contents | While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible open-source framework for evaluating and benchmarking post hoc explanation methods. OpenXAI comprises of the following key components: (i) a flexible synthetic data generator and a collection of diverse real-world datasets, pre-trained models, and state-of-the-art feature attribution methods, and (ii) open-source implementations of eleven quantitative metrics for evaluating faithfulness, stability (robustness), and fairness of explanation methods, in turn providing comparisons of several explanation methods across a wide variety of metrics, models, and datasets. OpenXAI is easily extensible, as users can readily evaluate custom explanation methods and incorporate them into our leaderboards. Overall, OpenXAI provides an automated end-to-end pipeline that not only simplifies and standardizes the evaluation of post hoc explanation methods, but also promotes transparency and reproducibility in benchmarking these methods. While the first release of OpenXAI supports only tabular datasets, the explanation methods and metrics that we consider are general enough to be applicable to other data modalities. OpenXAI datasets and models, implementations of state-of-the-art explanation methods and evaluation metrics, are publicly available at this GitHub link. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_11104 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | OpenXAI: Towards a Transparent Evaluation of Model Explanations Agarwal, Chirag Ley, Dan Krishna, Satyapriya Saxena, Eshika Pawelczyk, Martin Johnson, Nari Puri, Isha Zitnik, Marinka Lakkaraju, Himabindu Machine Learning Artificial Intelligence While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible open-source framework for evaluating and benchmarking post hoc explanation methods. OpenXAI comprises of the following key components: (i) a flexible synthetic data generator and a collection of diverse real-world datasets, pre-trained models, and state-of-the-art feature attribution methods, and (ii) open-source implementations of eleven quantitative metrics for evaluating faithfulness, stability (robustness), and fairness of explanation methods, in turn providing comparisons of several explanation methods across a wide variety of metrics, models, and datasets. OpenXAI is easily extensible, as users can readily evaluate custom explanation methods and incorporate them into our leaderboards. Overall, OpenXAI provides an automated end-to-end pipeline that not only simplifies and standardizes the evaluation of post hoc explanation methods, but also promotes transparency and reproducibility in benchmarking these methods. While the first release of OpenXAI supports only tabular datasets, the explanation methods and metrics that we consider are general enough to be applicable to other data modalities. OpenXAI datasets and models, implementations of state-of-the-art explanation methods and evaluation metrics, are publicly available at this GitHub link. |
| title | OpenXAI: Towards a Transparent Evaluation of Model Explanations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2206.11104 |