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What is Data Stewardship?
What is Data Stewardship?
Data stewardship means I treat mental health data as a responsibility, not a resource. I set clear rules for what I collect, how I use it, who can access it, how I document decisions, and how I respond when something goes wrong.
Why mental health data is high-risk
Why mental health data is high-risk
Mental health data is uniquely sensitive. People can face stigma, discrimination, or personal harm if data leaks or gets misused, and privacy weaknesses in mental health apps are well-documented.
Because AI tools increasingly operate through conversational systems and digital monitoring, I treat privacy and governance as core requirements, not optional add-ons.
My Framework in 5 Pillars
My Framework in 5 Pillars
I organize stewardship into five pillars that match common governance expectations: Consent & Autonomy, Privacy & Data Protection, Fairness & Bias Monitoring, Auditability & Accountability, Incident Response & Recourse.
Implementation overview
Communicate consent clearly
Communicate consent clearly
I explain what data I collect, why, and what choices users have in plain language before collection begins.
Make data use visible
Make data use visible
I publish transparency information that users can actually understand (data categories, access roles, retention, and sharing rules). Research shows privacy concerns directly shape continued use of mental health services.
Monitor + respond + document
Monitor + respond + document
I continuously monitor fairness and risk and document decisions and incidents to ensure accountability stays real.
Glossary
Glossary
AI-Enabled Mental Health Tool- A digital tool that uses AI to support mental health tasks (screening support, conversation support, documentation aid).
Informed Consent- A clear, understandable agreement that explains data use, risks, and choices.
Data Minimization- I collect only what I need to deliver the stated purpose.
Purpose Limitation- I use data only for the reason I explained at collection time.
Retention- How long I keep data before deletion or anonymization.
Bias- Systematic unfair outcomes for certain groups due to data, design, or deployment.
Audit Trail- Records that show who accessed what, what changed, and when.
Accountability- Named roles and responsibilities for oversight and response.
Incident- A harmful event such as unauthorized access, unsafe output, or fairness failure
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