Please use this identifier to cite or link to this item: https://repository.iimb.ac.in/handle/2074/22027
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dc.contributor.advisorKumar, U Dinesh
dc.contributor.authorYashovardhan
dc.contributor.authorGodara, Jahnavi
dc.date.accessioned2023-07-02T15:19:57Z-
dc.date.available2023-07-02T15:19:57Z-
dc.date.issued2022
dc.identifier.urihttps://repository.iimb.ac.in/handle/2074/22027-
dc.description.abstractWith 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.publisherIndian Institute of Management Bangalore
dc.relation.ispartofseriesPGP_CCS_P22_171
dc.subjectArtificial intelligence
dc.subjectAI
dc.titleExplainable AI
dc.typeCCS Project Report-PGP
dc.pages21p.
Appears in Collections:2022
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