AI-Driven Personalization Without Losing Trust
Instead of black-box outputs, we show why a scenario ranks higher: the key factors, assumed thresholds, and sensitivity to income or deductions. Short tooltips translate model logic into plain English. Tell us where the explanations feel unclear, and we will improve them.
AI-Driven Personalization Without Losing Trust
We minimize personal data, encrypt in transit and at rest, and apply strict access controls. Aggregation and anonymization protect patterns without exposing identities. Share your privacy expectations, and we will publish updates to our controls and retention timelines.
AI-Driven Personalization Without Losing Trust
Users decide which data to share, can reset preferences, and disable personalization entirely. Clear labels distinguish suggestions from rules. Post feedback on these controls, and vote on future toggles that would make you more comfortable sharing context for comparisons.
AI-Driven Personalization Without Losing Trust
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