“End to End” Towards a Framework for Reducing Biases and Promoting Transparency of Algorithmic Systems
Avital Shulner Tal, Khuyagbaatar Batsuren, Veronika Bogina, Fausto Giunchiglia, Alan G. Hartman, Styliani Kleanthous, Tsvi Kuflik, Jahna Otterbacher · 2019
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.