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08/28/2023
Identifying and Mitigating the Security Risks of Generative AI
Every major technical invention resurfaces the dual-use dilemma – the ne...
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08/28/2023
AI Deception: A Survey of Examples, Risks, and Potential Solutions
This paper argues that a range of current AI systems have learned how to...
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06/21/2023
An Overview of Catastrophic AI Risks
Rapid advancements in artificial intelligence (AI) have sparked growing ...
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06/20/2023
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Generative Pre-trained Transformer (GPT) models have exhibited exciting ...
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04/06/2023
Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark
Artificial agents have traditionally been trained to maximize reward, wh...
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03/28/2023
Natural Selection Favors AIs over Humans
For billions of years, evolution has been the driving force behind the d...
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01/02/2023
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
Reading comprehension of legal text can be a particularly challenging ta...
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10/18/2022
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios
In recent years, deep neural networks have demonstrated increasingly str...
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10/13/2022
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Out-of-distribution (OOD) detection is vital to safety-critical machine ...
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06/30/2022
Forecasting Future World Events with Neural Networks
Forecasting future world events is a challenging but valuable task. Fore...
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06/17/2022
Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks
Artificial intelligence (AI) systems can provide many beneficial capabil...
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06/13/2022
X-Risk Analysis for AI Research
Artificial intelligence (AI) has the potential to greatly improve societ...
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12/09/2021
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
In real-world applications of machine learning, reliable and safe system...
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10/26/2021
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
Machine learning models often encounter samples that are diverged from t...
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10/25/2021
What Would Jiminy Cricket Do? Towards Agents That Behave Morally
When making everyday decisions, people are guided by their conscience, a...
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09/28/2021
Unsolved Problems in ML Safety
Machine learning (ML) systems are rapidly increasing in size, are acquir...
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07/23/2021
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Progress in machine learning is typically measured by training and testi...
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05/20/2021
Measuring Coding Challenge Competence With APPS
While programming is one of the most broadly applicable skills in modern...
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03/10/2021
CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review
Many specialized domains remain untouched by deep learning, as large lab...
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03/05/2021
Measuring Mathematical Problem Solving With the MATH Dataset
Many intellectual endeavors require mathematical problem solving, but th...
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09/07/2020
Measuring Massive Multitask Language Understanding
We propose a new test to measure a text model's multitask accuracy. The ...
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08/05/2020
Aligning AI With Shared Human Values
We show how to assess a language model's knowledge of basic concepts of ...
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06/29/2020
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
We introduce three new robustness benchmarks consisting of naturally occ...
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04/13/2020
Pretrained Transformers Improve Out-of-Distribution Robustness
Although pretrained Transformers such as BERT achieve high accuracy on i...
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12/05/2019
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Modern deep neural networks can achieve high accuracy when the training ...
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11/25/2019
A Benchmark for Anomaly Segmentation
Detecting out-of-distribution examples is important for safety-critical ...
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08/21/2019
Testing Robustness Against Unforeseen Adversaries
Considerable work on adversarial defense has studied robustness to a fix...
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07/16/2019
Natural Adversarial Examples
We introduce natural adversarial examples -- real-world, unmodified, and...
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06/28/2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Self-supervision provides effective representations for downstream tasks...
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05/03/2019
Transfer of Adversarial Robustness Between Perturbation Types
We study the transfer of adversarial robustness of deep neural networks ...
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03/28/2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
In this paper we establish rigorous benchmarks for image classifier robu...
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01/28/2019
Using Pre-Training Can Improve Model Robustness and Uncertainty
Tuning a pre-trained network is commonly thought to improve data efficie...
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12/11/2018
Deep Anomaly Detection with Outlier Exposure
It is important to detect and handle anomalous inputs when deploying mac...
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08/01/2018
Open Category Detection with PAC Guarantees
Open category detection is the problem of detecting "alien" test instanc...
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07/04/2018
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
In this paper we establish rigorous benchmarks for image classifier robu...
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02/14/2018
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
The growing importance of massive datasets with the advent of deep learn...
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10/07/2016
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
We consider the two related problems of detecting if an example is miscl...
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08/01/2016
Early Methods for Detecting Adversarial Images
Many machine learning classifiers are vulnerable to adversarial perturba...
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07/08/2016