20 citations · 37 across the 6 of their papers we have counts for
9 papers
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop f…
Evaluating Deception Detection Model Robustness To Linguistic Variation
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensi…
Measure Utility, Gain Trust: Practical Advice for XAI Researcher
Brittany Davis, Maria Glenski, William Sealy +1
Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughou…
Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks
Winston Wu, Dustin Arendt, Svitlana Volkova
We evaluate machine comprehension models' robustness to noise and adversarial attacks by performing novel perturbations at the character, word, and sentence level. We experiment wi…
CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation
Dustin Arendt, Zhuanyi Huang, Prasha Shrestha +3
Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model impr…
Vulnerable to Misinformation? Verifi!
Alireza Karduni, Isaac Cho, Ryan Wesslen +5
We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. On the one hand, social media platforms empower individuals and organiz…