21 citations · 24 across the 3 of their papers we have counts for
4 papers
"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
Lin Luo, Yuri Nakao, Mathieu Chollet +2
Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds to assess outcome fair…
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders
Lin Luo, Yuri Nakao, Mathieu Chollet +2
Numerous fairness metrics have been proposed and employed by artificial intelligence (AI) experts to quantitatively measure bias and define fairness in AI models. Recognizing the n…
ERIC: Extracting Relations Inferred from Convolutions
Joe Townsend, Theodoros Kasioumis, Hiroya Inakoshi
Our main contribution is to show that the behaviour of kernels across multiple layers of a convolutional neural network can be approximated using a logic program. The extracted log…
DTM-based Filtrations
Hirokazu Anai, Frédéric Chazal, Marc Glisse +4
Despite strong stability properties, the persistent homology of filtrations classically used in Topological Data Analysis, such as, e.g. the Cech or Vietoris-Rips filtrations, are…