A Practical guide on Explainable AI Techniques applied on Biomedical use case applications Article Swipe
Adrien Bennetot
,
Ivan Donadello
,
Ayoub El Qadi
,
Mauro Dragoni
,
Thomas Frossard
,
B.J. Wagner
,
Anna Saranti
,
Silvia Tulli
,
Maria Trocan
,
Raja Chatila
,
Andreas Holzinger
,
Artur S. d’Avila Garcez
,
Natalia Díaz-Rodríguez
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.14260
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.14260
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and prediction skills, their functioning does not allow obtaining detailed explanations of their behaviour. As opaque machine learning models are increasingly being employed to make important predictions in critical environments, the danger is to create and use decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing machine learning models with explainability. EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This guide is meant to be the go-to handbook for any audience with a computer science background aiming at getting intuitive insights on machine learning models, accompanied with straight, fast, and intuitive explanations out of the box. This article aims to fill the lack of compelling XAI guide by applying XAI techniques in their particular day-to-day models, datasets and use-cases. Figure 1 acts as a flowchart/map for the reader and should help him to find the ideal method to use according to his type of data. In each chapter, the reader will find a description of the proposed method as well as an example of use on a Biomedical application and a Python notebook. It can be easily modified in order to be applied to specific applications.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.14260
- https://arxiv.org/pdf/2111.14260
- OA Status
- green
- Cited By
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4294959402Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2111.14260Digital Object Identifier
- Title
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A Practical guide on Explainable AI Techniques applied on Biomedical use case applicationsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-13Full publication date if available
- Authors
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Adrien Bennetot, Ivan Donadello, Ayoub El Qadi, Mauro Dragoni, Thomas Frossard, B.J. Wagner, Anna Saranti, Silvia Tulli, Maria Trocan, Raja Chatila, Andreas Holzinger, Artur S. d’Avila Garcez, Natalia Díaz-RodríguezList of authors in order
- Landing page
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https://arxiv.org/abs/2111.14260Publisher landing page
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https://arxiv.org/pdf/2111.14260Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2111.14260Direct OA link when available
- Concepts
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Computer science, Transparency (behavior), Artificial intelligence, Python (programming language), Generalization, Machine learning, Trustworthiness, Data science, Programming language, Mathematical analysis, Mathematics, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.method | 188, 209 |
| abstract_inverted_index.models | 43, 82 |
| abstract_inverted_index.opaque | 9, 40 |
| abstract_inverted_index.reader | 179, 201 |
| abstract_inverted_index.should | 181 |
| abstract_inverted_index.verify | 93 |
| abstract_inverted_index.(DNNs). | 19 |
| abstract_inverted_index.applied | 234 |
| abstract_inverted_index.article | 149 |
| abstract_inverted_index.certify | 95 |
| abstract_inverted_index.enhance | 99 |
| abstract_inverted_index.example | 214 |
| abstract_inverted_index.general | 73 |
| abstract_inverted_index.getting | 130 |
| abstract_inverted_index.machine | 41, 80, 134 |
| abstract_inverted_index.models, | 136, 167 |
| abstract_inverted_index.notions | 103 |
| abstract_inverted_index.outputs | 97 |
| abstract_inverted_index.science | 126 |
| abstract_inverted_index.skills, | 27 |
| abstract_inverted_index.support | 12 |
| abstract_inverted_index.upsurge | 7 |
| abstract_inverted_index.Although | 20 |
| abstract_inverted_index.Networks | 18 |
| abstract_inverted_index.applying | 160 |
| abstract_inverted_index.audience | 122 |
| abstract_inverted_index.chapter, | 199 |
| abstract_inverted_index.computer | 125 |
| abstract_inverted_index.critical | 53 |
| abstract_inverted_index.datasets | 168 |
| abstract_inverted_index.decision | 11 |
| abstract_inverted_index.detailed | 34 |
| abstract_inverted_index.employed | 47 |
| abstract_inverted_index.endowing | 79 |
| abstract_inverted_index.handbook | 119 |
| abstract_inverted_index.insights | 132 |
| abstract_inverted_index.learning | 42, 81, 135 |
| abstract_inverted_index.modified | 229 |
| abstract_inverted_index.proposed | 208 |
| abstract_inverted_index.specific | 236 |
| abstract_inverted_index.systems, | 13 |
| abstract_inverted_index.according | 191 |
| abstract_inverted_index.agreement | 74 |
| abstract_inverted_index.automatic | 10 |
| abstract_inverted_index.decisions | 62 |
| abstract_inverted_index.desirable | 102 |
| abstract_inverted_index.fairness. | 110 |
| abstract_inverted_index.important | 50 |
| abstract_inverted_index.intuitive | 131, 142 |
| abstract_inverted_index.notebook. | 224 |
| abstract_inverted_index.obtaining | 33 |
| abstract_inverted_index.straight, | 139 |
| abstract_inverted_index.Artificial | 86 |
| abstract_inverted_index.Biomedical | 219 |
| abstract_inverted_index.Therefore, | 69 |
| abstract_inverted_index.background | 127 |
| abstract_inverted_index.behaviour. | 38 |
| abstract_inverted_index.compelling | 156 |
| abstract_inverted_index.day-to-day | 166 |
| abstract_inverted_index.importance | 77 |
| abstract_inverted_index.particular | 165 |
| abstract_inverted_index.prediction | 26 |
| abstract_inverted_index.techniques | 89, 162 |
| abstract_inverted_index.use-cases. | 170 |
| abstract_inverted_index.EXplainable | 85 |
| abstract_inverted_index.accompanied | 137 |
| abstract_inverted_index.application | 220 |
| abstract_inverted_index.description | 205 |
| abstract_inverted_index.functioning | 29 |
| abstract_inverted_index.justifiable | 66 |
| abstract_inverted_index.legitimate. | 68 |
| abstract_inverted_index.predictions | 51 |
| abstract_inverted_index.Intelligence | 87 |
| abstract_inverted_index.explanations | 35, 143 |
| abstract_inverted_index.increasingly | 45 |
| abstract_inverted_index.transparency | 108 |
| abstract_inverted_index.applications. | 237 |
| abstract_inverted_index.characterized | 4 |
| abstract_inverted_index.environments, | 54 |
| abstract_inverted_index.flowchart/map | 176 |
| abstract_inverted_index.generalization | 24 |
| abstract_inverted_index.accountability, | 107 |
| abstract_inverted_index.explainability. | 84 |
| abstract_inverted_index.trustworthiness, | 106 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |