Contextual privacy design

Privacy expectations around AI systems change dramatically depending on context. Users may be comfortable with an AI analyzing their photos to organize them privately, but feel uncomfortable when the same AI technology analyzes their images for advertising purposes. Contextual privacy design recognizes that AI privacy isn't a fixed preference but varies based on situation, perceived value, and social expectations.

Here are key approaches to contextual AI privacy:

• Map AI-specific privacy sensitivities for your application. Consider which AI functions users see as more invasive, like emotion detection versus simple object recognition in images, or sentiment analysis versus topic classification for text.
• Design AI data practices to match contextual expectations. For example, an AI assistant might process voice commands immediately without saving recordings for routine tasks, but ask permission before storing more sensitive health-related queries.
• Adjust AI transparency based on context. When an AI system is performing background tasks like spam filtering, minimal disclosure is appropriate. For more sensitive applications like content moderation or health assessments, provide more detailed information about how AI analyzes user data.

Recognize that AI privacy contexts evolve. Features that initially seem intrusive, like facial recognition for photo organization, may become accepted as users understand the benefits and limitations.