CLAIMED, a visual and scalable component library for Trusted AI
원문 초록
Deep Learning models are getting more and more popular but constraints on explainability, adversarial robustness and fairness are often major concerns for production deployment. Although the open source ecosystem is abundant on addressing those concerns, fully integrated, end to end systems are lacking in open source. Therefore we provide an entirely open source, reusable component framework, visual editor and execution engine for production grade machine learning on top of Kubernetes, a joint effort between IBM and the University Hospital Basel. It uses Kubeflow Pipelines, the AI Explainability360 toolkit, the AI Fairness360 toolkit and the Adversarial Robustness Toolkit on top of ElyraAI, Kubeflow, Kubernetes and JupyterLab. Using the Elyra pipeline editor, AI pipelines can be developed visually with a set of jupyter notebooks.
함께 살펴볼 논문
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Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label Distillation
Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
Guiding LLM Decision-Making with Fairness Reward Models
토론
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