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From the triumph of black boxes to the right to understand and the search for fairness

Gilbert Saporta 1
1 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Over the past 40 years, Machine Learning models have made great strides thanks to advances in processors and the availability of hugedatabases: natural language processing, image recognition etc. It did not matter that these models were not understandable as long as they predicted accurately.What was at first only a reluctance ofspecialists (eg. physicians, economists) to use models that were far from their natural reasoning, became a societal issue when ML algorithms startedto be massively used to make high-stake decisions concerning citizens.The biases of the algorithms, which are rather those of the learning data and of the a priori, have given rise to a literature of denunciation, codes of ethics but also scientific works particularly on algorithmic fairness. I will expose the issues by linking them to the subject of interpretability and explicability of algorithms
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https://hal-cnam.archives-ouvertes.fr/hal-03245218
Contributor : Gilbert Saporta <>
Submitted on : Tuesday, June 1, 2021 - 4:13:48 PM
Last modification on : Thursday, June 3, 2021 - 3:15:53 AM

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  • HAL Id : hal-03245218, version 1

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Gilbert Saporta. From the triumph of black boxes to the right to understand and the search for fairness. ASMDA 2021, Jun 2021, Athens, Greece. ⟨hal-03245218⟩

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