4 citations · 4 across the 5 of their papers we have counts for
5 papers
Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users
Alfio Ventura, Tim Katzke, Jan Corazza +1
Trust calibration -- aligning user trust judgment with model capability -- is crucial for safe deployment of explainable AI (XAI), yet is often evaluated via global trust ratings d…
Evaluating Counterfactual Explanation Methods on Incomplete Inputs
Francesco Leofante, Daniel Neider, Mustafa Yalçıner
Existing algorithms for generating Counterfactual Explanations (CXs) for Machine Learning (ML) typically assume fully specified inputs. However, real-world data often contains miss…
Formal verification for robo-advisors: Irrelevant for subjective end-user trust, yet decisive for investment behavior?
Alina Tausch, Magdalena Wischnewski, Mustafa Yalciner +1
This online-vignette study investigates the impact of certification and verification as measures for quality assurance of AI on trust and use of a robo-advisor. Confronting 520 par…
VeriFlow: Modeling Distributions for Neural Network Verification
Faried Abu Zaid, Daniel Neider, Mustafa Yalçıner
Formal verification has emerged as a promising method to ensure the safety and reliability of neural networks. However, many relevant properties, such as fairness or global robustn…
Improved Approximation Algorithms for Individually Fair Clustering
Ali Vakilian, Mustafa Yalçıner
We consider the -clustering problem with -norm cost, which includes -median, -means and -center, under an individual notion of fairness proposed by Jung et al.…