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Found 36 papers, 14 papers with code
A Survey of Learning Causality with Data: Problems and Methods
This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations.
Large Language Models for Data Annotation: A Survey
Furthermore, the paper includes an in-depth taxonomy of methodologies employing LLMs for data annotation, a comprehensive review of learning strategies for models incorporating LLM-generated annotations, and a detailed discussion on primary challenges and limitations associated with using LLMs for data annotation.
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks
Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society.
Ranked #1 on
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JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning
The scaling of Large Language Models (LLMs) for retrieval-based tasks, particularly in Retrieval Augmented Generation (RAG), faces significant memory constraints, especially when fine-tuning extensive prompt sequences.
Long-Term Effect Estimation with Surrogate Representation
Second, short-term outcomes are often directly used as the proxy of the primary outcome, i. e., the surrogate.
Debiasing Word Embeddings with Nonlinear Geometry
Therefore, this work studies biases associated with multiple social categories: joint biases induced by the union of different categories and intersectional biases that do not overlap with the biases of the constituent categories.
Improving Cyberbully Detection with User Interaction
Cyberbullying, identified as intended and repeated online bullying behavior, has become increasingly prevalent in the past few decades.
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication
Online review systems are the primary means through which many businesses seek to build the brand and spread their messages.
Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies
This work empirically examines the causal effects of user-generated online reviews on a granular level: we consider multiple aspects, e. g., the Food and Service of a restaurant.
Distributional Shift Adaptation using Domain-Specific Features
Machine learning algorithms typically assume that the training and test samples come from the same distributions, i. e., in-distribution.
Fairness through Aleatoric Uncertainty
We then propose a principled model to improve fairness when aleatoric uncertainty is high and improve utility elsewhere.
Interpreting Pretrained Language Models via Concept Bottlenecks
Pretrained language models (PLMs) have made significant strides in various natural language processing tasks.
Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach
The element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session.
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack
Adversarial contrastive learning (ACL) is considered an effective way to improve the robustness of pre-trained models.
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives.
Causal Mediation Analysis with Hidden Confounders
An important problem in causal inference is to break down the total effect of a treatment on an outcome into different causal pathways and to quantify the causal effect in each pathway.
Causal Learning for Socially Responsible AI
The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.
Analysis of Legal Documents via Non-negative Matrix Factorization Methods
The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files.
Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science
This work studies publications in the field of cognitive science and utilizes mathematical techniques to connect the analysis of the papers' content (abstracts) to the context (citation, journals).
A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation
These mechanisms could be manifested in two forms: (1) the bias of social media's recommender systems and (2) internal biases such as confirmation bias and homophily.
Evaluation Methods and Measures for Causal Learning Algorithms
To bridge from conventional causal inference (i. e., based on statistical methods) to causal learning with big data (i. e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning.
Human Instance Segmentation and Tracking via Data Association and Single-stage Detector
To tracking the instance across the video, we have adopted data association strategy for matching the same instance in the video sequence, where we jointly learn target instance appearances and their affinities in a pair of video frames in an end-to-end fashion.
Causal Disentanglement with Network Information for Debiased Recommendations
Aside from the user conformity, aspects of confounding such as item popularity present in the network information is also captured in our method with the aid of \textit{causal disentanglement} which unravels the learned representations into independent factors that are responsible for (a) modeling the exposure of an item to the user, (b) predicting the ratings, and (c) controlling the hidden confounders.
Toward Understanding Bias Correlations for Mitigation in NLP
In particular, we examine bias mitigation in two common NLP tasks -- toxicity detection and word embeddings -- on three social identities, i. e., race, gender, and religion.
Inferring High-level Geographical Concepts via Knowledge Graph and Multi-scale Data Integration: A Case Study of C-shaped Building Pattern Recognition
To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns.
A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges
The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications.
Intersectionality and Testimonial Injustice in Medical Records
To illustrate this, we use real-world medical data to determine whether medical records exhibit words that could lead to testimonial injustice, employ fairness metrics (e. g. demographic parity, differential intersectional fairness, and subgroup fairness) to assess the severity to which subgroups are experiencing testimonial injustice, and analyze how the intersectionality of demographic features (e. g. gender and race) make a difference in uncovering testimonial injustice.
Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs
Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks.
Fair Few-shot Learning with Auxiliary Sets
These auxiliary sets contain several labeled training samples that can enhance the model performance regarding fairness in meta-test tasks, thereby allowing for the transfer of learned useful fairness-oriented knowledge to meta-test tasks.
STANCE-C3: Domain-adaptive Cross-target Stance Detection via Contrastive Learning and Counterfactual Generation
We also propose a modified self-supervised contrastive learning as a component of STANCE-C3 to prevent overfitting for the existing domain and target and enable cross-target stance detection.
A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier
While the accuracy-fairness trade-off has been frequently observed in the literature of fair machine learning, rigorous theoretical analyses have been scarce.
Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models
This work investigates the potential of undermining both fairness and detection performance in abusive language detection.
A Survey on Safe Multi-Modal Learning System
In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs.
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access
This study aims to address the pervasive challenge of quantifying uncertainty in large language models (LLMs) without logit-access.
Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media
News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence.
