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

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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Dates and versions

hal-03245218 , version 1 (01-06-2021)

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  • HAL Id : hal-03245218 , version 1

Cite

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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