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Found 22 papers, 13 papers with code
LLMs generate structurally realistic social networks but overestimate political homophily
To answer these questions, we develop three prompting methods for network generation and compare the generated networks to real social networks.
Annotation alignment: Comparing LLM and human annotations of conversational safety
We study this question via *annotation alignment*, the extent to which LLMs and humans agree when annotating the safety of user-chatbot conversations.
MEDIQ: Question-Asking LLMs for Adaptive and Reliable Clinical Reasoning
We develop a reliable Patient system and prototype several Expert systems, first showing that directly prompting state-of-the-art LLMs to ask questions degrades the quality of clinical reasoning, indicating that adapting LLMs to interactive information-seeking settings is nontrivial.
Participation in the age of foundation models
In addition to the "foundation" layer, our framework proposes the "subfloor'' layer, in which stakeholders develop shared technical infrastructure, norms and governance for a grounded domain, and the "surface'' layer, in which affected communities shape the use of a foundation model for a specific downstream task.
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing
Decision-makers often observe the occurrence of events through a reporting process.
Domain constraints improve risk prediction when outcome data is missing
Though our case study is in healthcare, our analysis reveals a general class of domain constraints which can improve model estimation in many settings.
Reconciling the accuracy-diversity trade-off in recommendations
In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items representing a range of categories).
Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers
Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field's future.
Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems
Many recommender systems are based on optimizing a linear weighting of different user behaviors, such as clicks, likes, shares, etc.
Coarse race data conceals disparities in clinical risk score performance
Across outcomes and metrics, we show that the risk scores exhibit significant granular performance disparities within coarse race groups.
How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?
In a top-tier computer science conference (NeurIPS 2021) with more than 23, 000 submitting authors and 9, 000 submitted papers, we survey the authors on three questions: (i) their predicted probability of acceptance for each of their papers, (ii) their perceived ranking of their own papers based on scientific contribution, and (iii) the change in their perception about their own papers after seeing the reviews.
Trucks Don't Mean Trump: Diagnosing Human Error in Image Analysis
Algorithms provide powerful tools for detecting and dissecting human bias and error.
Quantifying disparities in intimate partner violence: a machine learning method to correct for underreporting
Estimating the prevalence of a medical condition, or the proportion of the population in which it occurs, is a fundamental problem in healthcare and public health.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
Ethical Machine Learning in Health Care
The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities.
Concept Bottleneck Models
We seek to learn models that we can interact with using high-level concepts: if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis?
Predicting pregnancy using large-scale data from a women's health tracking mobile application
Predicting pregnancy has been a fundamental problem in women's health for more than 50 years.
Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data.
A large-scale analysis of racial disparities in police stops across the United States
We find that black drivers are stopped more often than white drivers relative to their share of the driving-age population, but that Hispanic drivers are stopped less often than whites.
Applications
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning
We here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure from expression data observed for heterogenous samples.
Fast Threshold Tests for Detecting Discrimination
Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions.
