71 citations · 76 across the 3 of their papers we have counts for
3 papers
Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness
Carolyn Ashurst, Ryan Carey, Silvia Chiappa +1
In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects. We refer to this as int…
AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements
Carolyn Ashurst, Emmie Hine, Paul Sedille +1
Ethics statements have been proposed as a mechanism to increase transparency and promote reflection on the societal impacts of published research. In 2020, the machine learning (ML…
Institutionalising Ethics in AI through Broader Impact Requirements
Carina Prunkl, Carolyn Ashurst, Markus Anderljung +3
Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this article, we reflect on a novel governance initiative by…