7 Ethical Considerations in Image Annotation Workflows
Image annotation workflows must protect individual privacy and follow data use guidelines. Annotators should ensure informed consent, anonymize sensitive details, and maintain transparency about how images and labels are used. Fair, unbiased labeling prevents discrimination in AI outputs.
Ethical workflows also include fair treatment of workers, clear guidelines, and diverse datasets. Human oversight, quality control, and accountability help detect bias and improve dataset fairness, supporting responsible AI development that respects both people and society.
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7 Ethical Considerations in Image Annotation Workflows | nasscom | The Official Community of Indian IT Industry
Ethical image annotation ensures labeling accuracy to safeguard privacy, accountability and fairness across the entire lifecycle of the image data. Teams have to ensure all data transfer is lawful, documentation is transparent and all practices maintain privacy by design. Audit trails and oversight ..
https://community.nasscom.in/communities/ai/7-ethical-considerations-image-annotation-workflows
