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Found 6 papers, 4 papers with code
Detecting clinician implicit biases in diagnoses using proximal causal inference
In this work, we propose a causal inference approach to detect the effect of clinician implicit biases on patient outcomes in large-scale medical data.
Individual Fairness Through Reweighting and Tuning
Recently, Graph Laplacian Regularizer (GLR), a regularization technique from the semi-supervised learning literature has been used as a substitute for the common Lipschitz condition to enhance individual fairness.
CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments
We showcase the potential of CRISPR-GPT for assisting non-expert researchers with gene-editing experiments from scratch and validate the agent's effectiveness in a real-world use case.
Prospector Heads: Generalized Feature Attribution for Large Models & Data
Feature attribution, the ability to localize regions of the input data that are relevant for classification, is an important capability for ML models in scientific and biomedical domains.
Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings
Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
