103 citations · 268 across the 12 of their papers we have counts for
12 papers
Taxonomy of Machine Learning Safety: A Survey and Primer
Sina Mohseni, Haotao Wang, Zhiding Yu +3
The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as i…
Shifting Transformation Learning for Out-of-Distribution Detection
Sina Mohseni, Arash Vahdat, Jay Yadawa
Detecting out-of-distribution (OOD) samples plays a key role in open-world and safety-critical applications such as autonomous systems and healthcare. Recently, self-supervised rep…
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Sina Mohseni, Fan Yang, Shiva Pentyala +6
Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale…
Practical Solutions for Machine Learning Safety in Autonomous Vehicles
Sina Mohseni, Mandar Pitale, Vasu Singh +1
Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to a…
XFake: Explainable Fake News Detector with Visualizations
Fan Yang, Shiva K. Pentyala, Sina Mohseni +6
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fake…
Predicting Model Failure using Saliency Maps in Autonomous Driving Systems
Sina Mohseni, Akshay Jagadeesh, Zhangyang Wang
While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations. Rise of machine learning products in s…
Open Issues in Combating Fake News: Interpretability as an Opportunity
Sina Mohseni, Eric Ragan, Xia Hu
Combating fake news needs a variety of defense methods. Although rumor detection and various linguistic analysis techniques are common methods to detect false content in social med…
Combating Fake News with Interpretable News Feed Algorithms
Sina Mohseni, Eric Ragan
Nowadays, artificial intelligence algorithms are used for targeted and personalized content distribution in the large scale as part of the intense competition for attention in the…
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Sina Mohseni, Niloofar Zarei, Eric D. Ragan
The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence applications used in everyday life. Explainable intelligen…
ProvThreads: Analytic Provenance Visualization and Segmentation
Sina Mohseni, Alyssa Pena, Eric D. Ragan
Our work aims to generate visualizations to enable meta-analysis of analytic provenance and aid better understanding of analysts' strategies during exploratory text analysis. We in…
Analytic Provenance Datasets: A Data Repository of Human Analysis Activity and Interaction Logs
Sina Mohseni, Andrew Pachuilo, Ehsanul Haque Nirjhar +3
We present an analytic provenance data repository that can be used to study human analysis activity, thought processes, and software interaction with visual analysis tools during e…
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning
Sina Mohseni, Jeremy E. Block, Eric D. Ragan
Research in interpretable machine learning proposes different computational and human subject approaches to evaluate model saliency explanations. These approaches measure different…