Search Results for author:
Found 8 papers, 4 papers with code
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from Large Language Models
The process of scale calibration in ranking systems involves adjusting the outputs of rankers to correspond with significant qualities like click-through rates or relevance, crucial for mirroring real-world value and thereby boosting the system's effectiveness and reliability.
Dissecting users' needs for search result explanations
There is a growing demand for transparency in search engines to understand how search results are curated and to enhance users' trust.
Counterfactual Editing for Search Result Explanation
In this work, we aim to investigate the effectiveness of this perspective via proposing and evaluating counterfactual explanations for the task of SeRE.
An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings
Applications of machine learning (ML) to high-stakes policy settings -- such as education, criminal justice, healthcare, and social service delivery -- have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems.
Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy
Growing use of machine learning in policy and social impact settings have raised concerns for fairness implications, especially for racial minorities.
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions
The contribution is 1) a methodology for explainable ML researchers to identify use cases and develop methods targeted at them and 2) using that methodology for the domain of public policy and giving an example for the researchers on developing explainable ML methods that result in real-world impact.
Item Recommendation with Evolving User Preferences and Experience
As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style.
The Many Faces of Link Fraud
Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior.
