Search Results for author:
Found 30 papers, 2 papers with code
Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation
In this study, we propose a new method that addresses the challenges of medical dialogue generation by incorporating medical knowledge into transformer-based language models.
Automatic Fake News Detection in Political Platforms - A Transformer-based Approach
Our proposed framework exploits the information from news articles and social contexts to detect fake news.
Accuracy meets Diversity in a News Recommender System
We customize an augmented vector for each query and news item to introduce information interaction between the two towers.
Developing Safe and Responsible Large Language Models -- A Comprehensive Framework
We introduce Safe and Responsible Large Language Model (SR$_{\text{LLM}}$) , a model designed to enhance the safety of language generation using LLMs.
Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions
These findings emphasize the importance of choosing ML models carefully to ensure both accuracy and fairness for all patients.
FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections
In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information.
Progress in Privacy Protection: A Review of Privacy Preserving Techniques in Recommender Systems, Edge Computing, and Cloud Computing
As digital technology evolves, the increasing use of connected devices brings both challenges and opportunities in the areas of mobile crowdsourcing, edge computing, and recommender systems.
FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?
The rapid evolution of Large Language Models (LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
Reliability Analysis of Psychological Concept Extraction and Classification in User-penned Text
The social NLP research community witness a recent surge in the computational advancements of mental health analysis to build responsible AI models for a complex interplay between language use and self-perception.
Analyzing the Impact of Fake News on the Anticipated Outcome of the 2024 Election Ahead of Time
Despite increasing awareness and research around fake news, there is still a significant need for datasets that specifically target racial slurs and biases within North American political speeches.
Navigating News Narratives: A Media Bias Analysis Dataset
The proliferation of biased news narratives across various media platforms has become a prominent challenge, influencing public opinion on critical topics like politics, health, and climate change.
FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections
We have created a novel dataset of North American election-related news articles through a blend of advanced language models (LMs) and thorough human verification, for precision and relevance.
She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models
Additionally, implementing a test suite such as ours lowers the environmental overhead of making models safe and fair.
Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis
Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making.
NBIAS: A Natural Language Processing Framework for Bias Identification in Text
Therefore, it is crucial to detect and remove these biases to ensure the fair and ethical use of data.
Mitigating Bias in Conversations: A Hate Speech Classifier and Debiaser with Prompts
Discriminatory language and biases are often present in hate speech during conversations, which usually lead to negative impacts on targeted groups such as those based on race, gender, and religion.
Fairness in Machine Learning meets with Equity in Healthcare
With the growing utilization of machine learning in healthcare, there is increasing potential to enhance healthcare outcomes.
Auditing ICU Readmission Rates in an Clinical Database: An Analysis of Risk Factors and Clinical Outcomes
This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes.
Connecting Fairness in Machine Learning with Public Health Equity
Machine learning (ML) has become a critical tool in public health, offering the potential to improve population health, diagnosis, treatment selection, and health system efficiency.
Leveraging Foundation Models for Clinical Text Analysis
Infectious diseases are a significant public health concern globally, and extracting relevant information from scientific literature can facilitate the development of effective prevention and treatment strategies.
Addressing Biases in the Texts using an End-to-End Pipeline Approach
The concept of fairness is gaining popularity in academia and industry.
Dbias: Detecting biases and ensuring Fairness in news articles
Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature.
BERT4Loc: BERT for Location -- POI Recommender System
Our model combines location information and user preferences to provide more relevant recommendations compared to models that predict the next POI in a sequence.
An Approach to Ensure Fairness in News Articles
This paper introduces Dbias, which is a Python package to ensure fairness in news articles.
A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities
There are a few challenges related to the task of biomedical named entity recognition, which are: the existing methods consider a fewer number of biomedical entities (e. g., disease, symptom, proteins, genes); and these methods do not consider the social determinants of health (age, gender, employment, race), which are the non-medical factors related to patients' health.
A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission
In this paper, we propose a machine learning pipeline capable of making predictions as well as detecting and mitigating biases in the data and model predictions.
A COVID-19 Search Engine (CO-SE) with Transformer-based Architecture
This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications.
A News Recommender System Considering Temporal Dynamics and Diversity
In a news recommender system, a reader's preferences change over time.
Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System
News reading is also driven by a blend of a reader's long-term and short-term interests.
News Recommender System: A review of recent progress, challenges, and opportunities
Nowadays, more and more news readers tend to read news online where they have access to millions of news articles from multiple sources.
