Please use this identifier to cite or link to this item:
https://repository.iimb.ac.in/handle/2074/22027
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kumar, U Dinesh | |
dc.contributor.author | Yashovardhan | |
dc.contributor.author | Godara, Jahnavi | |
dc.date.accessioned | 2023-07-02T15:19:57Z | - |
dc.date.available | 2023-07-02T15:19:57Z | - |
dc.date.issued | 2022 | |
dc.identifier.uri | https://repository.iimb.ac.in/handle/2074/22027 | - |
dc.description.abstract | With the availability of wide variety of complex machine learning tools, industries have started opting for modelling techniques which have high interpretability, leading to the development of a whole new branch in artific ial intelligence called Explainable AI. This project involves creating a quantitative measure to gauge the explaining ability of a model. We have performed literature survey to gain deeper understanding about the latest model development in the field. We have created a framework to benchmark the interpretability of various models, thus helping the industry in selecting the best model for their particular use case. | |
dc.publisher | Indian Institute of Management Bangalore | |
dc.relation.ispartofseries | PGP_CCS_P22_171 | |
dc.subject | Artificial intelligence | |
dc.subject | AI | |
dc.title | Explainable AI | |
dc.type | CCS Project Report-PGP | |
dc.pages | 21p. | |
Appears in Collections: | 2022 |
Files in This Item:
File | Size | Format | |
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PGP_CCS_P22_171.pdf | 2.33 MB | Adobe PDF | View/Open Request a copy |
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