21 citations · 53 across the 16 of their papers we have counts for
19 papers
The Differential Effects of AI Abstention on Advice Acceptance
Nicholas Wolczynski, Yotam Liel, Maytal Saar-Tsechansky +1
AI advising systems are typically designed to always provide a response. Recent work has focused on developing systems that withhold advice in some instances, such as when advice q…
Conformal Policy Selection: When to Deploy AI Decisions for Targeted Intervention
Junyu Cao, Ruijiang Gao, Maytal Saar-Tsechansky +1
Artificial-intelligence (AI) policies are increasingly used to make consequential decisions in medicine, finance, education, and public services, yet deploying them responsibly req…
Collaborative Theory of Mind: Learning to Self-Revise by Anticipating User Responses
Fanzhi Zeng, Anubrata Das, Maytal Saar-Tsechansky +1
Large language models (LLMs) demonstrate strong individual task performance, yet still struggle to collaborate with users over extended, multi-turn interactions, for example by att…
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…
The Value of AI Advice: Personalized and Value-Maximizing AI Advisors Are Necessary to Reliably Benefit Experts and Organizations
Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang
Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Cons…
How high-status women promote repeated collaboration among women in male-dominated contexts
Huimin Xu, Jamie Strassman, Ying Ding +2
Male-dominated contexts pose a dilemma: they increase the benefits of repeated collaboration among women, yet at the same time, make such collaborations less likely. This paper see…
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…
Learning Complementary Policies for Human-AI Teams
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga
This paper tackles the critical challenge of human-AI complementarity in decision-making. Departing from the traditional focus on algorithmic performance in favor of performance of…
Learning to Advise Humans in High-Stakes Settings
Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang
Expert decision-makers (DMs) in high-stakes AI-assisted decision-making (AIaDM) settings receive and reconcile recommendations from AI systems before making their final decisions.…
Algorithmic Fairness in Business Analytics: Directions for Research and Practice
Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky
The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising lega…
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…
Human-AI Collaboration with Bandit Feedback
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga +3
Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic…
Modeling Longitudinal Dynamics of Comorbidities
Basil Maag, Stefan Feuerriegel, Mathias Kraus +2
In medicine, comorbidities refer to the presence of multiple, co-occurring diseases. Due to their co-occurring nature, the course of one comorbidity is often highly dependent on th…
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…