8 citations · 14 across the 3 of their papers we have counts for
5 papers · 1 filter
Explaining how your AI system is fair
Boris Ruf, Marcin Detyniecki
To implement fair machine learning in a sustainable way, choosing the right fairness objective is key. Since fairness is a concept of justice which comes in various, sometimes conf…
Towards the Right Kind of Fairness in AI
Boris Ruf, Marcin Detyniecki
Fairness is a concept of justice. Various definitions exist, some of them conflicting with each other. In the absence of an uniformly accepted notion of fairness, choosing the righ…
Active Fairness Instead of Unawareness
Boris Ruf, Marcin Detyniecki
The possible risk that AI systems could promote discrimination by reproducing and enforcing unwanted bias in data has been broadly discussed in research and society. Many current l…
Getting Fairness Right: Towards a Toolbox for Practitioners
Boris Ruf, Chaouki Boutharouite, Marcin Detyniecki
The potential risk of AI systems unintentionally embedding and reproducing bias has attracted the attention of machine learning practitioners and society at large. As policy makers…
Contract Statements Knowledge Service for Chatbots
Boris Ruf, Matteo Sammarco, Marcin Detyniecki
Towards conversational agents that are capable of handling more complex questions on contractual conditions, formalizing contract statements in a machine readable way is crucial. H…