6 citations · 9 across the 6 of their papers we have counts for
6 papers
Counterfactual Training: Teaching Models Plausible and Actionable Explanations
Patrick Altmeyer, Aleksander Buszydlik, Arie van Deursen +1
We propose a novel training regime termed counterfactual training that leverages counterfactual explanations to increase the explanatory capacity of models. Counterfactual explanat…
Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition
Floris den Hengst, Ralf Wolter, Patrick Altmeyer +1
We present Conformal Intent Classification and Clarification (CICC), a framework for fast and accurate intent classification for task-oriented dialogue systems. The framework turns…
Position: Stop Making Unscientific AGI Performance Claims
Patrick Altmeyer, Andrew M. Demetriou, Antony Bartlett +1
Developments in the field of Artificial Intelligence (AI), and particularly large language models (LLMs), have created a 'perfect storm' for observing 'sparks' of Artificial Genera…
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…
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…