61 citations · 225 across the 36 of their papers we have counts for
7 papers · 1 filter
Data vs. Model Machine Learning Fairness Testing: An Empirical Study
Arumoy Shome, Luis Cruz, Arie van Deursen
Although several fairness definitions and bias mitigation techniques exist in the literature, all existing solutions evaluate fairness of Machine Learning (ML) systems after the tr…
Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals
Patrick Altmeyer, Mojtaba Farmanbar, Arie van Deursen +1
Counterfactual explanations offer an intuitive and straightforward way to explain black-box models and offer algorithmic recourse to individuals. To address the need for plausible…
Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World
Lorena Poenaru-Olaru, Natalia Karpova, Luis Cruz +2
Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operati…
Endogenous Macrodynamics in Algorithmic Recourse
Patrick Altmeyer, Giovan Angela, Aleksander Buszydlik +3
Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the g…
Explaining Black-Box Models through Counterfactuals
Patrick Altmeyer, Arie van Deursen, Cynthia C. S. Liem
We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inpu…
Batching for Green AI -- An Exploratory Study on Inference
Tim Yarally, Luís Cruz, Daniel Feitosa +2
The batch size is an essential parameter to tune during the development of new neural networks. Amongst other quality indicators, it has a large degree of influence on the model's…
Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
Tim Yarally, Luís Cruz, Daniel Feitosa +2
Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results" often refers to the achieved accuracy on a competitive pro…