Privacy Policies for AI/ML Apps: Data, Decisions, GDPR 22
Practical guidance for AI/ML app privacy policies: training data duties, automated decision safeguards, and GDPR Art 22 rights. Built for business owners.
Training data can make or break your AI/ML privacy posture. Map every dataset, document provenance and licenses, and state a lawful basis for training (GDPR Art 6) that aligns with purpose limitation (Art 5(1)(b)). Avoid scraping personal data without notice; give a clear notice at collection and opt-out under CCPA/CPRA. Prefer de-identified or truly anonymized data, and exclude sensitive categories unless you have explicit consent. Sign DPAs with data vendors, and record processing (Art 30). If you ingest children's data, apply COPPA and age gating.
Explain any automated decisions plainly. GDPR Art 22 restricts decisions based solely on automation that produce legal or similarly significant effects; offer a human review path, the ability to contest, and meaningful information about the logic, significance, and consequences (Arts 13-15, 22). For credit, hiring, or housing, watch ECOA, FCRA, and Title VII disparate impact. Under CPRA, prepare to honor rights around automated decision-making as regulations mature. Log features used, error rates, and overrides to support audits and user-facing summaries.
Turn principles into policy and process. Describe retention tied to model lifecycle, security measures (GDPR Art 32), cross-border transfers using SCCs or adequacy, and how to submit rights requests and appeals. Run DPIAs for high-risk uses (Art 35) and LIAs if you rely on legitimate interests. Maintain vendor due diligence and training opt-out mechanisms. Honor GPC signals for CCPA/CPRA. LegalDocs.ai can generate tailored privacy policies, DPIA checklists, and Art 22 notices, helping you ship compliant AI features without slowing teams.