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Found 20 papers, 3 papers with code
xLP: Explainable Link Prediction for Master Data Management
Explaining neural model predictions to users requires creativity.
LLMGuard: Guarding Against Unsafe LLM Behavior
Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns.
"Beware of deception": Detecting Half-Truth and Debunking it through Controlled Claim Editing
To help combat this problem, we have created a comprehensive pipeline consisting of a half-truth detection model and a claim editing model.
CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation
Our experiments show that CFL achieves such a detoxification without much impact on the model perplexity.
Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets
We attempt to re-look at the data quality issues in the context of building a machine learning pipeline and build a tool that can detect, explain and remediate issues in the data, and systematically and automatically capture all the changes applied to the data.
Explainable Link Prediction for Privacy-Preserving Contact Tracing
Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus.
Data Readiness Report
Data exploration and quality analysis is an important yet tedious process in the AI pipeline.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
Extracting Fairness Policies from Legal Documents
Automatic extraction of fairness policies, or in general, any specific kind of policies from large legal corpus can be very useful for the study of bias and fairness in the context of AI applications.
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity
We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers.
Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction
The presence of gender stereotypes in many aspects of society is a well-known phenomenon.
Generating Clues for Gender based Occupation De-biasing in Text
Vast availability of text data has enabled widespread training and use of AI systems that not only learn and predict attributes from the text but also generate text automatically.
Hardening Deep Neural Networks via Adversarial Model Cascades
Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an adversary to produce erroneous outputs.
Model Extraction Warning in MLaaS Paradigm
In this work, we present a cloud-based extraction monitor that can quantify the extraction status of models by observing the query and response streams of both individual and colluding adversarial users.
Bollywood Movie Corpus for Text, Images and Videos
In this work, we have worked with movie data from Wikipedia plots and movie trailers from YouTube.
A Survey on Resilient Machine Learning
Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc.
Towards Crafting Text Adversarial Samples
Our algorithm works best for the datasets which have sub-categories within each of the classes of examples.
