AI, user data and the asymmetry of understanding
Every time users belatedly discover that an artificial intelligence feature has been drawing on their data in ways they did not fully grasp, the reaction is often an instinctive sense of violation - of trust, consent and privacy. Accusations and outrage have always followed potentially invasive AI integrations, with examples ranging from email content used [... ] The post AI, user data and the asymmetry of understanding appeared first on SiliconANGLE.
Key Takeaways
- SiliconANGLE UPDATED 12:13 EDT / JUNE 20 2026 AI AI, user data and the asymmetry of understanding GUEST COLUMN by Onur Alp Soner Every time users belatedly discover that an artificial intelligence feature has been drawing on their data in ways they did not fully grasp, the reaction is often an instinctive sense of violation - of trust, consent and privacy.
- Regulators are already acknowledging how upstream data decisions persist downstream.
In late 2024, the European Data Protection Board updated its opinion on issues of anonymity, legitimate interest and AI models trained on unlawfully processed personal data, noting that this can affect whether such models can be lawfully deployed unless properly anonymized.
- In practice, the obligations should fall more heavily on companies.
- They also push for explanations covering how data is used, who is responsible, and what consequences follow.
The EU AI Act is adding further transparency duties for certain AI systems, aimed at helping users recognize when they are interacting with AI or exposed to AI-generated content so that they can make informed decisions.
- Users should still have rights and controls, but companies are the ones deciding the defaults, retention periods, data flows, vendor relationships, and increasingly how models behave in practice.
Stats & Key Facts
- #SiliconANGLE UPDATED 12:13 EDT / JUNE 20 2026 AI AI, user data and the asymmetry of understanding GUEST COLUMN by Onur Alp Soner Every time users belatedly discover that an artificial intelligence feature has been drawing on their data in ways they did not fully grasp, the reaction is often an instinctive sense of violation - of trust, consent and privacy.
- #In late 2024, the European Data Protection Board updated its opinion on issues of anonymity, legitimate interest and AI models trained on unlawfully processed personal data, noting that this can affect whether such models can be lawfully deployed unless properly anonymized.
SiliconANGLE UPDATED 12:13 EDT / JUNE 20 2026 AI AI, user data and the asymmetry of understanding GUEST COLUMN by Onur Alp Soner Every time users belatedly discover that an artificial intelligence feature has been drawing on their data in ways they did not fully grasp, the reaction is often an instinctive sense of violation - of trust, consent and privacy. Accusations and outrage have always followed potentially invasive AI integrations, with examples ranging from email content used to inform model training and large on-device models embedded in everyday software to voice assistants retaining snippets beyond explicit commands and default settings that enable cross-product activity to inform AI responses. Even when such changes are technically disclosed, awareness doesn't necessarily follow.
Updates arrive one after another, and settings default to "on," putting the onus on users to navigate a labyrinth they never asked for. The cognitive gap between what organizations understand about how their systems use data and what individuals can reasonably expect to understand seems to widen daily. Most users don't mind disclosing chat, clickstream or location history if it serves their purpose, but companies on the other side may see training data, embeddings, personalization signals, safety-tuning inputs, fraud-detection features and future product capabilities in those messages.
Regulators are already acknowledging how upstream data decisions persist downstream. In late 2024, the European Data Protection Board updated its opinion on issues of anonymity, legitimate interest and AI models trained on unlawfully processed personal data, noting that this can affect whether such models can be lawfully deployed unless properly anonymized. 's Information Commissioner's Office also stresses the need for organizations to explain AI-assisted processes and decisions to those affected.
For more details please read the original article at SiliconANGLE AI.
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