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Responsible Artificial Intelligence (AI) - the practice of developing, evaluating, and maintaining accurate AI systems that also exhibit essential properties such as robustness and explainability - represents a multifaceted challenge that often stretches standard machine learning tooling, frameworks, and testing methods beyond their limits.
On evaluating adversarial robustness
Carlini, N.; et al. 2019 · 1902
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One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Arya, V.; et al. 2019 · 1909
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Augmix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D.; et al. 2019 · 1912
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A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
Falcon, W.; and Cho, K. 2020 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
Abadi, M.; et al. 2015 · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O.; et al. 2015 · 2015
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A survey on metamorphic testing
Segura, S.; et al. 2016 · 2016
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Foolbox: A python toolbox to benchmark the robustness of machine learning models
Rauber, J.; Brendel, W.; and Bethge, M. 2017 · 2017
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JAX: composable transformations of Python+NumPy programs
Bradbury, J.; et al. 2018 · 2018
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Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; et al. 2018 · 2018
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Adversarial Robustness Toolbox v1. 0.0
Nicolae, M.-I.; et al. 2018 · 2018
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Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Papernot, N.; et al. 2018 · 2018
Cited alongside, same era.
Robustness (Python Library)
Engstrom, L.; et al. 2019 · 2019
Cited alongside, same era.
Ethics Guidelines for Trustworthy AI
High-Level Expert Group on Artificial Intelligence. 2019 · 2019
Cited alongside, same era.
Functional Adversarial Attacks
Laidlaw, C.; and Feizi, S. 2019 · 2019
Cited alongside, same era.
In Praise of Property-Based Testing
MacIver, D. 2019 · 2019
Cited alongside, same era.
Hypothesis: A new approach to property-based testing
MacIver, D. R.; Hatfield-Dodds, Z.; et al. 2019 · 2019
Cited alongside, same era.
Test-Case Reduction via Test-Case Generation: Insights from the Hypothesis Reducer (Tool Insights Paper)
MacIver, D. R.; and Donaldson, A. F. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J.; et al. 2020 · 2020
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Comet.ML home page
Comet.ML. 2021 · 2021
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Responsible AI practices
Google. 2021 · 2021
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Trustworthy AI is human-centered
IBM. 2021 · 2021
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Responsible AI
Microsoft. 2021 · 2021
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MLFlow home page
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; et al. 2019 · 2019
Cited alongside, same era.
Hydra - A framework for elegantly configuring complex applications
Yadan, O. 2019 · 2019
Cited alongside, same era.
Experiment Tracking with Weights and Biases
Biewald, L. 2020 · 2020
Cited alongside, same era.
DOD Adopts Ethical Principles for Artificial Intelligence
Department of Defense. 2020 · 2020
Cited alongside, same era.
Array programming with NumPy
Harris, C. R.; et al. 2020 · 2020
Cited alongside, same era.
MLFlow. 2021 · 2021
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Controllably Sparse Perturbations of Robust Classifiers for Explaining Predictions and Probing Learned Concepts
Roberts, J.; and Tsiligkaridis, T. 2021 · 2021
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Responsible AI: bridging from ethics to practice
Shneiderman, B. 2021 · 2021
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Don’t Repeat Yourself: Keeping DRY with Dynamically-Generated Configs
Soklaski, R. 2021 · 2021
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hydra-zen: A library that facilitates configurable, reproducible, and scalable workflow
Soklaski, R.; and Goodwin, J. 2021 · 2021
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