Harnessing value from data science in business: ensuring explainability and fairness of solutions

• URL: http://arxiv.org/abs/2108.07714v1
• Date: Tue, 10 Aug 2021 11:59:38 GMT
• Title: Harnessing value from data science in business: ensuring explainability and fairness of solutions
• Authors: Krzysztof Chomiak and Micha{\l} Miktus
• Abstract summary: The paper introduces concepts of fairness and explainability (XAI) in artificial intelligence, oriented to solve a sophisticated business problems. For fairness, the authors discuss the bias-inducing specifics, as well as relevant mitigation methods, concluding with a set of recipes for introducing fairness in data-driven organizations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper introduces concepts of fairness and explainability (XAI) in artificial intelligence, oriented to solve a sophisticated business problems. For fairness, the authors discuss the bias-inducing specifics, as well as relevant mitigation methods, concluding with a set of recipes for introducing fairness in data-driven organizations. Additionally, for XAI, the authors audit specific algorithms paired with demonstrational business use-cases, discuss a plethora of techniques of explanations quality quantification and provide an overview of future research avenues.

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