21 citations · 53 across the 16 of their papers we have counts for
9 papers · 1 filter
Bias-Aware Mislabeling Detection via Decoupled Confident Learning
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
Reliable data is a cornerstone of modern organizational systems. A notable data integrity challenge stems from label bias, which refers to systematic errors in a label, a covariate…
Using Machine Bias To Measure Human Bias
Wanxue Dong, Maria De-Arteaga, Maytal Saar-Tsechansky
Biased human decisions have consequential impacts across various domains, yielding unfair treatment of individuals and resulting in suboptimal outcomes for organizations and societ…
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II
Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky
Problem Definition. Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Me…
Mitigating Label Bias via Decoupled Confident Learning
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels i…
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
An increased awareness concerning risks of algorithmic bias has driven a surge of efforts around bias mitigation strategies. A vast majority of the proposed approaches fall under o…
A Machine Learning Framework Towards Transparency in Experts' Decision Quality
Wanxue Dong, Maytal Saar-Tsechansky, Tomer Geva
Expert workers make non-trivial decisions with significant implications. Experts' decision accuracy is thus a fundamental aspect of their judgment quality, key to both management a…
Cost-Accuracy Aware Adaptive Labeling for Active Learning
Ruijiang Gao, Maytal Saar-tsechansky
Conventional active learning algorithms assume a single labeler that produces noiseless label at a given, fixed cost, and aim to achieve the best generalization performance for giv…
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness
Tong Wang, Maytal Saar-Tsechansky
We propose a model-agnostic approach for mitigating the prediction bias of a black-box decision-maker, and in particular, a human decision-maker. Our method detects in the feature…
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation
Elad Liebman, Maytal Saar-Tsechansky, Peter Stone
In recent years, there has been growing focus on the study of automated recommender systems. Music recommendation systems serve as a prominent domain for such works, both from an a…