21 citations · 34 across the 12 of their papers we have counts for
12 papers
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…
Should you use LLMs to simulate opinions? Quality checks for early-stage deliberation
Terrence Neumann, Maria De-Arteaga, Sina Fazelpour
The emergent capabilities of large language models (LLMs) have prompted interest in using them as surrogates for human subjects in opinion surveys. However, prior evaluations of LL…
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…
Fairness-Aware Multi-Group Target Detection in Online Discussion
Soumyajit Gupta, Maria De-Arteaga, Matthew Lease
Target-group detection is the task of detecting which group(s) a piece of content is ``directed at or about''. Applications include targeted marketing, content recommendation, and…
Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation
Terrence Neumann, Sooyong Lee, Maria De-Arteaga +2
The pervasive spread of misinformation and disinformation poses a significant threat to society. Professional fact-checkers play a key role in addressing this threat, but the vast…
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…
Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection
Soumyajit Gupta, Sooyong Lee, Maria De-Arteaga +1
Algorithmic bias often arises as a result of differential subgroup validity, in which predictive relationships vary across groups. For example, in toxic language detection, comment…
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…
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…
Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms
Terrence Neumann, Maria De-Arteaga, Sina Fazelpour
Faced with the scale and surge of misinformation on social media, many platforms and fact-checking organizations have turned to algorithms for automating key parts of misinformatio…
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…