Ethical, Privacy and Sustainability Considerations in PyTorch S...
summary
PyTorch models sit inside data pipelines, APIs, logs and infrastructure so risks appear beyond weights and accuracy. The Ethical Software Framework and an Ethical IT Audit map concrete checks: data provenance, anonymization, storage retention, logging granularity, model explainability, human-in-loop gating and carbon accounting for inference and training. Example: a candidate-ranking PyTorch system requires sources of training CVs, sensitivity labels, audit trails for each ranking decision, GDPR-aligned retention and measurable bias metrics before deployment.
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