Benchmarking and survey of explanation methods for black box models
Scuola Normale Superiore · University of Pisa · +1 more institution
Abstract
Abstract The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of explainers providing many different types of explanations. With the aim of providing a compass for researchers and practitioners, this paper proposes a categorization of explanation methods from the perspective of the type of explanation they return, also considering the different input data formats. The paper accounts for the most representative explainers to date, also discussing similarities and…
Citation impact
- FWCI
- 34.79
- Percentile
- 100%
- References
- 190
Authors
6Topics & keywords
- Benchmarking
- Categorization
- Computer science
- Perspective (graphical)
- Repertoire
- Artificial intelligence
- Black box
- Data science
- Peace, Justice and strong institutions
Funding
- ETEesti TeadusagentuurAwards: SLTAT21096, CHIST-ERA-19-XAI-010
- ASAustrian Science FundAward: I 5205
- BNBulgarian National Science Fund
- H2Horizon 2020 Framework ProgrammeAwards: 952215, 952026, 654024, 871042
- EAEngineering and Physical Sciences Research CouncilAwards: EP/V055712/1, H2020, EP/V055712/1
- HEH2020 Excellent ScienceAward: 871042
- HEH2020 European Research CouncilAward: 834756
- HRH2020 Research InfrastructuresAwards: 952026, G.A. 871042, 654024, 871042
- CCHIST-ERAAward: CHIST-ERA-19-XAI-010
- HLH2020 LEIT Information and Communication TechnologiesAwards: 952215, 952026