Human Intelligence for Privacy & AI Governance
Privacy impact assessment workflows for professional judgment.
TrustDPO is building PIA Studio, a pre-launch workflow product for privacy professionals, consultants, and governance teams working on privacy impact assessments, AI-related reviews, and evidence-based privacy documentation.
TrustDPO is in pre-launch development. The website is informational only. There is currently no product access, no account creation, no analytics, no tracking, no cookies for analytics or advertising, no AI processing, and no intake form.
Books
Two practical guides on privacy impact assessments and algorithmic bias — read free articles or get the book on Amazon.
Explore the books
Privacy Impact Assessment Basics
Essential Strategies to Identify High-Risk Processing and Build Trusted Compliance
A practical guide to identify high-risk data processing and build PIA documentation regulators respect, across GDPR, LGPD, PIPEDA, and Quebec Law 25.
Algorithmic Bias
How Intelligent Systems Learn to Be Unfair — and How to Fix It
How facial recognition, hiring algorithms, and credit scoring learn to discriminate — and the governance design that can hold them accountable.
Latest insights
The Amazon Hiring Algorithm That Learned to Discriminate Against Women
July 31, 2026
Amazon built an AI recruiting tool that penalized résumés containing the word "women's." Here is why removing the offending words didn't fix it, and what that means for any company screening candidates with AI.
When Is a PIA Legally Mandatory? GDPR vs LGPD vs PIPEDA vs Quebec Law 25
July 29, 2026
A side-by-side look at when a Privacy Impact Assessment is legally required under the GDPR, Brazil's LGPD, Canada's PIPEDA, and Quebec's Law 25 — with the real cases that show what happens when it isn't.
The Robert Williams Case: What Facial Recognition Bias Really Costs
July 27, 2026
Detroit police arrested Robert Williams in front of his kids over a facial recognition match that was never independently checked. Here is what the case actually teaches about algorithmic bias.
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