Designing Consent Workflows for AI Identity Verification
Blog post from Didit
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AI-native identity verification systems that use document scanning, facial recognition, liveness detection, and biometric matching require clear, informed user consent to balance security, privacy, regulatory compliance, and conversion. Effective consent workflows use plain language, contextual just-in-time prompts, visual aids, layered explanations, and explicit statements of purpose and data protections, while giving users granular opt-in choices, explaining the consequences of refusal, and supporting consent review or withdrawal where appropriate. Consent should be integrated smoothly into the verification journey through consistent design, relevant placement, progress indicators, and actionable guidance. Didit presents its modular, developer-focused platform and no-code Business Console as tools for creating customizable consent steps within identity verification workflows, with support for fraud detection, varying regulatory needs, a free KYC tier, and pay-per-successful-check pricing.
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