Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users' perceptions of fairness toward an algorithmic system - researchr publication

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Avital Shulner Tal, Tsvi Kuflik, Doron Kliger. Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users' perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2, 2022. [doi]

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