Litcius/Paper detail

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

Avital Shulner Tal, Tsvi Kuflik, Doron Kliger

2022Ethics and Information Technology52 citationsDOI

Topics & Concepts

PerceptionTransparency (behavior)Outcome (game theory)AuditProcess (computing)Computer scienceCertificationSystem justificationPsychologySocial psychologyComputer securityBusinessPoliticsAccountingLawPolitical scienceMathematical economicsOperating systemNeuroscienceMathematicsIdeologyExplainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education
Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system | Litcius