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Found 12 papers, 1 papers with code
AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts
As Large Language Models (LLMs) and generative AI become more widespread, the content safety risks associated with their use also increase.
Simulation of Attacker Defender Interaction in a Noisy Security Game
In the cybersecurity setting, defenders are often at the mercy of their detection technologies and subject to the information and experiences that individual analysts have.
Robustness and Usefulness in AI Explanation Methods
Explainability in machine learning has become incredibly important as machine learning-powered systems become ubiquitous and both regulation and public sentiment begin to demand an understanding of how these systems make decisions.
Towards a Responsible AI Development Lifecycle: Lessons From Information Security
Legislation and public sentiment throughout the world have promoted fairness metrics, explainability, and interpretability as prescriptions for the responsible development of ethical artificial intelligence systems.
Evaluating Attacker Risk Behavior in an Internet of Things Ecosystem
In cybersecurity, attackers range from brash, unsophisticated script kiddies and cybercriminals to stealthy, patient advanced persistent threats.
Who's Afraid of Thomas Bayes?
In many cases, neural networks perform well on test data, but tend to overestimate their confidence on out-of-distribution data.
The State of AI Ethics Report (January 2021)
The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020.
The Influence of Dropout on Membership Inference in Differentially Private Models
Differentially private models seek to protect the privacy of data the model is trained on, making it an important component of model security and privacy.
The State of AI Ethics Report (October 2020)
The 2nd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since July 2020.
Malicious Network Traffic Detection via Deep Learning: An Information Theoretic View
Applying our results can serve to guide analysis methods for machine learning engineers and suggests that neural networks that can exploit the convolution theorem are equally accurate as standard convolutional neural networks, and can be more computationally efficient.
Green Lighting ML: Confidentiality, Integrity, and Availability of Machine Learning Systems in Deployment
In this hand-off, the engineers responsible for model deployment are often not privy to the details of the model and thus, the potential vulnerabilities associated with its usage, exposure, or compromise.
The State of AI Ethics Report (June 2020)
These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivalled pace has left the internet and technology watchers aghast.
