Algorithms play an increasing role in our everyday lives. Recently, the harmful potential of biased algorithms has been recognized by researchers and practitioners. We have also witnessed a growing interest in ensuring the fairness and transparency of algorithmic systems. However, so far there is no agreed upon solution and not even an agreed terminology. The proposed research defines the problem space, solution space and a prototype of comprehensive framework for the detection and reducing biases in algorithmic systems.
“End to End” Towards a Framework for Reducing Biases and Promoting Transparency of Algorithmic Systems / Shulner Tal, Avital; Hartman, Alan; Batsuren, Khuyagbaatar; Kleanthous Loizou, Styliani; Bogina, Veronika; Tsvi, Kuflik; Giunchiglia, Fausto; Otterbacher, Jahna. - (2019), pp. 54-59. ( 14th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2019 Larnaca, Cyprus 9-10 June 2019) [10.1109/SMAP.2019.8864914].
“End to End” Towards a Framework for Reducing Biases and Promoting Transparency of Algorithmic Systems
Khuyagbaatar Batsuren;Fausto Giunchiglia;
2019-01-01
Abstract
Algorithms play an increasing role in our everyday lives. Recently, the harmful potential of biased algorithms has been recognized by researchers and practitioners. We have also witnessed a growing interest in ensuring the fairness and transparency of algorithmic systems. However, so far there is no agreed upon solution and not even an agreed terminology. The proposed research defines the problem space, solution space and a prototype of comprehensive framework for the detection and reducing biases in algorithmic systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
